Publications
Dammeier, Sascha; Nahnsen, Sven; Veit, Johannes; Wehner, Frank; Ueffing, Marius; Kohlbacher, Oliver Mass Spectrometry-based Proteomics Reveals Organspecific Expression Patterns To Be Used as Forensic Evidence J. Proteome Res., 15 (1), pp. 182-92, 2016. @article{Forensics_JPR_2016, title = {Mass Spectrometry-based Proteomics Reveals Organspecific Expression Patterns To Be Used as Forensic Evidence}, author = {Sascha Dammeier and Sven Nahnsen and Johannes Veit and Frank Wehner and Marius Ueffing and Oliver Kohlbacher}, url = {https://doi.org/10.1021/acs.jproteome.5b00704}, year = {2016}, date = {2016-01-01}, journal = {J. Proteome Res.}, volume = {15}, number = {1}, pages = {182-92}, abstract = {Standard forensic procedures to examine bullets after an exchange of fire include a mechanical or ballistic reconstruction of the event. While this is routine to identify which projectile hit a subject by DNA analysis of biological material on the surface of the projectile, it is rather difficult to determine which projectile caused the lethal injury – often the crucial point with regard to legal proceedings. With respect to fundamental law it is the duty of the public authority to make every endeavor in order to solve every homicide case. To improve forensic examinations we present a forensic proteomic method to investigate biological material from a projectile’s surface and determine the tissues traversed by it. To obtain a range of relevant samples, different major bovine organs were penetrated with projectiles experimentally. After tryptic “on-surface” digestion, mass spectrometry-based proteome analysis and statistical data analysis, we were able to achieve a cross-validated organ classification accuracy of over 99%. Different types of anticipated external variables exhibited no prominent influence on the findings. In addition, shooting experiments were performed to validate the results. Finally, we show that these concepts could be applied to a real case of murder in order to substantially improve the forensic reconstruction.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Standard forensic procedures to examine bullets after an exchange of fire include a mechanical or ballistic reconstruction of the event. While this is routine to identify which projectile hit a subject by DNA analysis of biological material on the surface of the projectile, it is rather difficult to determine which projectile caused the lethal injury – often the crucial point with regard to legal proceedings. With respect to fundamental law it is the duty of the public authority to make every endeavor in order to solve every homicide case. To improve forensic examinations we present a forensic proteomic method to investigate biological material from a projectile’s surface and determine the tissues traversed by it. To obtain a range of relevant samples, different major bovine organs were penetrated with projectiles experimentally. After tryptic “on-surface” digestion, mass spectrometry-based proteome analysis and statistical data analysis, we were able to achieve a cross-validated organ classification accuracy of over 99%. Different types of anticipated external variables exhibited no prominent influence on the findings. In addition, shooting experiments were performed to validate the results. Finally, we show that these concepts could be applied to a real case of murder in order to substantially improve the forensic reconstruction. |
Schubert, Benjamin; Kohlbacher, Oliver Designing string-of-beads vaccines with optimal spacers Genome Med., 8 (1), pp. 9, 2016. @article{SOB-Design2016, title = {Designing string-of-beads vaccines with optimal spacers}, author = {Benjamin Schubert and Oliver Kohlbacher}, url = {http://www.genomemedicine.com/content/8/1/9}, year = {2016}, date = {2016-01-01}, journal = {Genome Med.}, volume = {8}, number = {1}, pages = {9}, abstract = {String-of-beads polypeptides allow convenient delivery of epitope-based vaccines. The success of a polypeptide relies on efficient processing: constituent epitopes need to be recovered while avoiding neo-epitopes from epitope junctions. Spacers between epitopes are employed to ensure this, but spacer selection is non-trivial.We present a framework to determine optimally the length and sequence of a spacer through multi-objective optimization for human leukocyte antigen class I restricted polypeptides. The method yields string-of-bead vaccines with flexible spacer lengths that increase the predicted epitope recovery rate fivefold while reducing the immunogenicity from neo-epitopes by 44 % compared to designs without spacers.}, keywords = {}, pubstate = {published}, tppubtype = {article} } String-of-beads polypeptides allow convenient delivery of epitope-based vaccines. The success of a polypeptide relies on efficient processing: constituent epitopes need to be recovered while avoiding neo-epitopes from epitope junctions. Spacers between epitopes are employed to ensure this, but spacer selection is non-trivial.We present a framework to determine optimally the length and sequence of a spacer through multi-objective optimization for human leukocyte antigen class I restricted polypeptides. The method yields string-of-bead vaccines with flexible spacer lengths that increase the predicted epitope recovery rate fivefold while reducing the immunogenicity from neo-epitopes by 44 % compared to designs without spacers. |
Stickel, Juliane; Kowalewski, Daniel; Walz, Simon; Backert, Linus; Schuster, Heiko; Kohlbacher, Oliver; Weisel, Katja; Rittig, Susanne; Kanz, Lothar; Salih, Helmut; Rammensee, Hans-Georg; Stevanovic, Stefan Carfilzomib alters the HLA-presented peptidome of myeloma cells and impairs presentation of peptides with aromatic C-termini Blood Cancer J., 6 , pp. e411, 2016. @article{BCJStickel2016, title = {Carfilzomib alters the HLA-presented peptidome of myeloma cells and impairs presentation of peptides with aromatic C-termini}, author = {Juliane Stickel and Daniel Kowalewski and Simon Walz and Linus Backert and Heiko Schuster and Oliver Kohlbacher and Katja Weisel and Susanne Rittig and Lothar Kanz and Helmut Salih and Hans-Georg Rammensee and Stefan Stevanovic}, doi = {https://doi.org/10.1038/bcj.2016.14}, year = {2016}, date = {2016-01-01}, journal = {Blood Cancer J.}, volume = {6}, pages = {e411}, abstract = {Recent studies suggest that multiple myeloma is an immunogenic disease, which might be effectively targeted by antigen-specific T-cell immunotherapy. As standard of care in myeloma includes proteasome inhibitor therapy, it is of great importance to characterize the effects of this treatment on HLA-restricted antigen presentation and implement only robustly presented targets for immunotherapeutic intervention. Here, we present a study that longitudinally and semi-quantitatively maps the effects of the proteasome inhibitor carfilzomib on HLA-restricted antigen presentation. The relative presentation levels of 4780 different HLA ligands were quantified in an in vitro model employing carfilzomib treatment of MM.1S and U266 myeloma cells, which revealed significant modulation of a substantial fraction of the HLA-presented peptidome. Strikingly, we detected selective down-modulation of HLA ligands with aromatic C-terminal anchor amino acids. This particularly manifested as a marked reduction in the presentation of HLA ligands through the HLA allotypes A*23:01 and A*24:02 on MM.1S cells. These findings implicate that carfilzomib mediates a direct, peptide motif-specific inhibitory effect on HLA ligand processing and presentation. As a substantial proportion of HLA allotypes present peptides with aromatic C-termini, our results may have broad implications for the implementation of antigen-specific treatment approaches in patients undergoing carfilzomib treatment.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recent studies suggest that multiple myeloma is an immunogenic disease, which might be effectively targeted by antigen-specific T-cell immunotherapy. As standard of care in myeloma includes proteasome inhibitor therapy, it is of great importance to characterize the effects of this treatment on HLA-restricted antigen presentation and implement only robustly presented targets for immunotherapeutic intervention. Here, we present a study that longitudinally and semi-quantitatively maps the effects of the proteasome inhibitor carfilzomib on HLA-restricted antigen presentation. The relative presentation levels of 4780 different HLA ligands were quantified in an in vitro model employing carfilzomib treatment of MM.1S and U266 myeloma cells, which revealed significant modulation of a substantial fraction of the HLA-presented peptidome. Strikingly, we detected selective down-modulation of HLA ligands with aromatic C-terminal anchor amino acids. This particularly manifested as a marked reduction in the presentation of HLA ligands through the HLA allotypes A*23:01 and A*24:02 on MM.1S cells. These findings implicate that carfilzomib mediates a direct, peptide motif-specific inhibitory effect on HLA ligand processing and presentation. As a substantial proportion of HLA allotypes present peptides with aromatic C-termini, our results may have broad implications for the implementation of antigen-specific treatment approaches in patients undergoing carfilzomib treatment. |
Kohlbacher, Oliver; Vitek, Olga; Weintraub, Susan T Challenges in Large-Scale Computational Mass Spectrometry and Multiomics J. Proteome Res., 15 (3), pp. 681-2, 2016. @article{JPRSIEditorial2016, title = {Challenges in Large-Scale Computational Mass Spectrometry and Multiomics}, author = {Oliver Kohlbacher and Olga Vitek and Susan T Weintraub}, url = {https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00067}, year = {2016}, date = {2016-01-01}, journal = {J. Proteome Res.}, volume = {15}, number = {3}, pages = {681-2}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Müller, Sabine C; Backes, Christina; Gress, Alexander; Baumgarten, Nina; Kalinina, Olga V; Moll, Andreas; Kohlbacher, Oliver; Meese, Eckart; Keller, Andreas BALL-SNPgp – from genetic variants towards computational diagnostics Bioinformatics, 2016. @article{BALLsnp2016, title = {BALL-SNPgp – from genetic variants towards computational diagnostics}, author = {Sabine C Müller and Christina Backes and Alexander Gress and Nina Baumgarten and Olga V Kalinina and Andreas Moll and Oliver Kohlbacher and Eckart Meese and Andreas Keller}, doi = {https://doi.org/10.1093/bioinformatics/btw084}, year = {2016}, date = {2016-01-01}, journal = {Bioinformatics}, abstract = {Summary: In medical research, it is crucial to understand the functional consequences of genetic alterations, for example non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a 3D visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites. Availability and implementation: BALL-SNPgp is based on the comprehensive C++ framework Biochemical Algorithms Library (BALL) and its visualization front-end BALLView. Our tool is available at www.ccb.uni-saarland.de/BALL-SNPgp.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Summary: In medical research, it is crucial to understand the functional consequences of genetic alterations, for example non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a 3D visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites. Availability and implementation: BALL-SNPgp is based on the comprehensive C++ framework Biochemical Algorithms Library (BALL) and its visualization front-end BALLView. Our tool is available at www.ccb.uni-saarland.de/BALL-SNPgp. |
Schubert, Benjamin; Walzer, Mathias; Brachvogel, Hans-Philipp; Szolek, Andras; Mohr, Christopher; Kohlbacher, Oliver FRED 2 – An Immunoinformatics Framework for Python Bioinformatics, 32 (13), pp. 2044-6, 2016. @article{FRED2_Bioinfo, title = {FRED 2 – An Immunoinformatics Framework for Python}, author = {Benjamin Schubert and Mathias Walzer and Hans-Philipp Brachvogel and Andras Szolek and Christopher Mohr and Oliver Kohlbacher}, doi = {https://doi.org/10.1093/bioinformatics/btw113}, year = {2016}, date = {2016-01-01}, journal = {Bioinformatics}, volume = {32}, number = {13}, pages = {2044-6}, abstract = {Summary: Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications. Availability: FRED 2 is available at http://fred-2.github.io Contact: Supplementary information: Supplementary data are available at Bioinformatics online.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Summary: Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications. Availability: FRED 2 is available at http://fred-2.github.io Contact: Supplementary information: Supplementary data are available at Bioinformatics online. |
de la Garza, Luis; Veit, Johannes; Szolek, Andras; Röttig, Marc; Aiche, Stephan; Gesing, Sandra; Reinert, Knut; Kohlbacher, Oliver From the Desktop to the Grid: scalable Bioinformatics via Workflow Conversion BMC Bioinformatics, 17 (1), pp. 127, 2016. @article{delaGarza2016, title = {From the Desktop to the Grid: scalable Bioinformatics via Workflow Conversion}, author = {Luis de la Garza and Johannes Veit and Andras Szolek and Marc Röttig and Stephan Aiche and Sandra Gesing and Knut Reinert and Oliver Kohlbacher}, doi = {https://doi.org/10.1186/s12859-016-0978-9}, year = {2016}, date = {2016-01-01}, journal = {BMC Bioinformatics}, volume = {17}, number = {1}, pages = {127}, abstract = {BACKGROUND: Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free -an aspect that could potentially drive away members of the scientific community. RESULTS: We have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources. CONCLUSIONS: Our work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free -an aspect that could potentially drive away members of the scientific community. RESULTS: We have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources. CONCLUSIONS: Our work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results. |
Breckels, Lisa M; Holden, Sean; Wojnar, David; Mulvey, Claire M; Christoforou, Adny; Groen, Arnoud; Kohlbacher, Oliver; Lilley, Kathryn S; Gatto, Laurent Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics PLoS Comput. Biol., 12 (5), pp. e1004920, 2016. @article{SpatialProtPLOSCB, title = {Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics}, author = {Lisa M Breckels and Sean Holden and David Wojnar and Claire M Mulvey and Adny Christoforou and Arnoud Groen and Oliver Kohlbacher and Kathryn S Lilley and Laurent Gatto}, doi = {https://doi.org/10.1371/journal.pcbi.1004920}, year = {2016}, date = {2016-01-01}, journal = {PLoS Comput. Biol.}, volume = {12}, number = {5}, pages = {e1004920}, abstract = {Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis. |
Mohr, Christopher; Friedrich, Andreas; Wojnar, David; Kenar, Erhan; Polatkan, Aydin Can; Codrea, Marius Cosmin; Czemmel, Stefan; Kohlbacher, Oliver; Nahnsen, Sven qPortal – A Science Gateway for Biomedical Applications Proc. Int. Workshop Science Gateways (IWSG 2016), 2016. (BibTeX) @article{qPortal-IWSG2016, title = {qPortal – A Science Gateway for Biomedical Applications}, author = {Christopher Mohr and Andreas Friedrich and David Wojnar and Erhan Kenar and Aydin Can Polatkan and Marius Cosmin Codrea and Stefan Czemmel and Oliver Kohlbacher and Sven Nahnsen}, year = {2016}, date = {2016-01-01}, journal = {Proc. Int. Workshop Science Gateways (IWSG 2016)}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
de la Garza, Luis; Aicheler, Fabian; Kohlbacher, Oliver From the Desktop to the Grid and Cloud: Conversion of KNIME Workflows to gUSE Proc. Int. Workshop Science Gateways (IWSG 2016), 2016. (BibTeX) @article{KNIME2gUSE-IWSG2016, title = {From the Desktop to the Grid and Cloud: Conversion of KNIME Workflows to gUSE}, author = {Luis de la Garza and Fabian Aicheler and Oliver Kohlbacher}, year = {2016}, date = {2016-01-01}, journal = {Proc. Int. Workshop Science Gateways (IWSG 2016)}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Griss, Johannes; Perez-Riverol, Yasset; Lewis, Steve; Tabb, David L; Dianes, José A; del-Toro, Noemi; Rurik, Marc; Walzer, Mathias M; Kohlbacher, Oliver; Hermjakob, Henning; Wang, Rui; Vizcaíno, Juan Antonio Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets Nat. Methods, 13 (8), pp. 651-656, 2016. @article{ClusteringNatMethods2016, title = {Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets}, author = {Johannes Griss and Yasset Perez-Riverol and Steve Lewis and David L Tabb and José A Dianes and Noemi del-Toro and Marc Rurik and Mathias M Walzer and Oliver Kohlbacher and Henning Hermjakob and Rui Wang and Juan Antonio Vizcaíno}, doi = {https://dx.doi.org/10.1038%2Fnmeth.3902}, year = {2016}, date = {2016-01-01}, journal = {Nat. Methods}, volume = {13}, number = {8}, pages = {651-656}, abstract = {Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average, 75% of spectra analyzed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large scale to shed light on these unidentified spectra. The Proteomics Identifications (PRIDE) Database Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in the PRIDE Archive, coming from hundreds of data sets, we were able to consistently characterize spectra into three distinct groups: (1) incorrectly identified, (2) correctly identified but below the set scoring threshold, and (3) truly unidentified. Using multiple complementary analysis approaches, we were able to identify ~20% of the consistently unidentified spectra. The complete spectrum-clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average, 75% of spectra analyzed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large scale to shed light on these unidentified spectra. The Proteomics Identifications (PRIDE) Database Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in the PRIDE Archive, coming from hundreds of data sets, we were able to consistently characterize spectra into three distinct groups: (1) incorrectly identified, (2) correctly identified but below the set scoring threshold, and (3) truly unidentified. Using multiple complementary analysis approaches, we were able to identify ~20% of the consistently unidentified spectra. The complete spectrum-clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra. |
Ranninger, Christina; Schmidt, Lukas E; Rurik, Marc; Limonciel, Alice; Jennings, Paul; Kohlbacher, Oliver; Huber, Christian G Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry Anal. Chim. Acta, 930 , pp. 13-22, 2016. @article{ScanRangeACA2016, title = {Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry}, author = {Christina Ranninger and Lukas E Schmidt and Marc Rurik and Alice Limonciel and Paul Jennings and Oliver Kohlbacher and Christian G Huber}, doi = {https://doi.org/10.1016/j.aca.2016.05.017}, year = {2016}, date = {2016-01-01}, journal = {Anal. Chim. Acta}, volume = {930}, pages = {13-22}, abstract = {Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates. Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50-1000 relevant for metabolites into two separate segments (m/z 50-200 and m/z 200-1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50-1000 in a single scan, or scanning from m/z 50-200 and from m/z 200-1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50-200 and m/z 200-1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification. The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates. Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50-1000 relevant for metabolites into two separate segments (m/z 50-200 and m/z 200-1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50-1000 in a single scan, or scanning from m/z 50-200 and from m/z 200-1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50-200 and m/z 200-1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification. The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode. |
Perez-Riverol, Yasset; Gatto, Laurent; Wang, Rui; Sachsenberg, Timo; Uszkoreit, Julian; Leprevost, Felipe; Fufezan, Christian; Ternent, Tobias; Eglen, Stephen J; Katz, Daniel S S; Pollard, Tom J; Konovalov, Alexander; Flight, Robert M; Blin, Kai; Vizcaino, Juan Antonio Ten Simple Rules for Taking Advantage of git and GitHub bioRxiv, 2016. @article{Perez-Riverol048744, title = {Ten Simple Rules for Taking Advantage of git and GitHub}, author = {Yasset Perez-Riverol and Laurent Gatto and Rui Wang and Timo Sachsenberg and Julian Uszkoreit and Felipe Leprevost and Christian Fufezan and Tobias Ternent and Stephen J Eglen and Daniel S S Katz and Tom J Pollard and Alexander Konovalov and Robert M Flight and Kai Blin and Juan Antonio Vizcaino}, url = {http://www.biorxiv.org/content/early/2016/05/13/048744}, year = {2016}, date = {2016-01-01}, journal = {bioRxiv}, abstract = {A ’Ten Simple Rules’ guide to git and GitHub. We describe and provide examples on how to use these software to track projects, as users, teams and organizations. We document collaborative development using branching and forking, interaction between collaborators using issues and continuous integration and automation using, for example, Travis CI and codecov. We also describe dissemination and social aspects of GitHub such as GitHub pages, following and watching repositories, and give advice on how to make code citable.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A ’Ten Simple Rules’ guide to git and GitHub. We describe and provide examples on how to use these software to track projects, as users, teams and organizations. We document collaborative development using branching and forking, interaction between collaborators using issues and continuous integration and automation using, for example, Travis CI and codecov. We also describe dissemination and social aspects of GitHub such as GitHub pages, following and watching repositories, and give advice on how to make code citable. |
Löffler, Markus; Chandra, Anoop P; Laske, Karoline; Schroeder, Christopher; Bonzheim, Irina; Hilke, Franz J; Kowalewski, Daniel J; Trautwein, Nico; Schuster, Heiko; Gründer, Marc; Walzer, Mathias; Mohr, Christopher; Nguyen, Huu-Phuc; Riess, Olaf; Bauer, Peter; Nahnsen, Sven; Königsrainer, Alfred; Nadalina, Silvio; Zieker, Derek; Glatzle, Jörg; Thiel, Karolin; Clasen, Stephan; Bösmüller, Hans; Fend, Falko; Kohlbacher, Oliver; Gouttefangeas, Cecile; Stevanovic, Stefan; Rammensee, Hans-Georg Personalized peptide vaccine induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient J. Hepatol., 65 (4), pp. 849-55, 2016. @article{CaseStudyJHepatol2016, title = {Personalized peptide vaccine induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient}, author = {Markus Löffler and Anoop P Chandra and Karoline Laske and Christopher Schroeder and Irina Bonzheim and Franz J Hilke and Daniel J Kowalewski and Nico Trautwein and Heiko Schuster and Marc Gründer and Mathias Walzer and Christopher Mohr and Huu-Phuc Nguyen and Olaf Riess and Peter Bauer and Sven Nahnsen and Alfred Königsrainer and Silvio Nadalina and Derek Zieker and Jörg Glatzle and Karolin Thiel and Stephan Clasen and Hans Bösmüller and Falko Fend and Oliver Kohlbacher and Cecile Gouttefangeas and Stefan Stevanovic and Hans-Georg Rammensee}, doi = {https://doi.org/10.1016/j.jhep.2016.06.027}, year = {2016}, date = {2016-01-01}, journal = {J. Hepatol.}, volume = {65}, number = {4}, pages = {849-55}, abstract = {BACKGROUND & AIMS: We report a novel experimental immunotherapeutic approach in a patient with metastatic intrahepatic cholangiocarcinoma. In the 5year course of the disease, the initial tumor mass, two local recurrences and a lung metastasis were surgically removed. Lacking alternative treatment options, aiming at the induction of anti-tumor T cells responses, we initiated a personalized multi-peptide vaccination, based on in-depth analysis of tumor antigens (immunopeptidome) and sequencing. METHODS: Tumors were characterized by immunohistochemistry, next-generation sequencing and mass spectrometry of HLA ligands. RESULTS: Although several tumor-specific neo-epitopes were predicted in silico, none could be validated by mass spectrometry. Instead, a personalized multi-peptide vaccine containing non-mutated tumor-associated epitopes was designed and applied. Immunomonitoring showed vaccine-induced T cell responses to three out of seven peptides administered. The pulmonary metastasis resected after start of vaccination showed strong immune cell infiltration and perforin positivity, in contrast to the previous lesions. The patient remains clinically healthy, without any radiologically detectable tumors since March 2013 and the vaccination is continued. CONCLUSIONS: This remarkable clinical course encourages formal clinical studies on adjuvant personalized peptide vaccination in cholangiocarcinoma. LAY SUMMARY: Metastatic cholangiocarcinomas, cancers that originate from the liver bile ducts, have very limited treatment options and a fatal prognosis. We describe a novel therapeutic approach in such a patient using a personalized multi-peptide vaccine. This vaccine, developed based on the characterization of the patient's tumor, evoked detectable anti-tumor immune responses, associating with long-term tumor-free survival.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND & AIMS: We report a novel experimental immunotherapeutic approach in a patient with metastatic intrahepatic cholangiocarcinoma. In the 5year course of the disease, the initial tumor mass, two local recurrences and a lung metastasis were surgically removed. Lacking alternative treatment options, aiming at the induction of anti-tumor T cells responses, we initiated a personalized multi-peptide vaccination, based on in-depth analysis of tumor antigens (immunopeptidome) and sequencing. METHODS: Tumors were characterized by immunohistochemistry, next-generation sequencing and mass spectrometry of HLA ligands. RESULTS: Although several tumor-specific neo-epitopes were predicted in silico, none could be validated by mass spectrometry. Instead, a personalized multi-peptide vaccine containing non-mutated tumor-associated epitopes was designed and applied. Immunomonitoring showed vaccine-induced T cell responses to three out of seven peptides administered. The pulmonary metastasis resected after start of vaccination showed strong immune cell infiltration and perforin positivity, in contrast to the previous lesions. The patient remains clinically healthy, without any radiologically detectable tumors since March 2013 and the vaccination is continued. CONCLUSIONS: This remarkable clinical course encourages formal clinical studies on adjuvant personalized peptide vaccination in cholangiocarcinoma. LAY SUMMARY: Metastatic cholangiocarcinomas, cancers that originate from the liver bile ducts, have very limited treatment options and a fatal prognosis. We describe a novel therapeutic approach in such a patient using a personalized multi-peptide vaccine. This vaccine, developed based on the characterization of the patient's tumor, evoked detectable anti-tumor immune responses, associating with long-term tumor-free survival. |
Röst, Hannes L; Sachsenberg, Timo; Aiche, Stephan; Bielow, Chris; Weisser, Hendrik; Aicheler, Fabian; Andreotti, Sandro; Ehrlich, Hans-Christian; Gutenbrunner, Petra; Kenar, Erhan; Liang, Xiao; Nahnsen, Sven; Nilse, Lars; Pfeuffer, Julianus; Rosenberger, George; Rurik, Marc; Schmitt, Uwe; Veit, Johannes; Walzer, Mathias; Wojnar, David; Wolski, Witold E; Schilling, Oliver; Choudhary, Jyoti S; Malmström, Lars; Aebersold, Ruedi; Reinert, Knut; Kohlbacher, Oliver OpenMS: A flexible open-source software platform for mass spectrometry data analysis Nat. Methods, 13 (9), pp. 741-748, 2016. @article{OpenMS2_2016, title = {OpenMS: A flexible open-source software platform for mass spectrometry data analysis}, author = {Hannes L Röst and Timo Sachsenberg and Stephan Aiche and Chris Bielow and Hendrik Weisser and Fabian Aicheler and Sandro Andreotti and Hans-Christian Ehrlich and Petra Gutenbrunner and Erhan Kenar and Xiao Liang and Sven Nahnsen and Lars Nilse and Julianus Pfeuffer and George Rosenberger and Marc Rurik and Uwe Schmitt and Johannes Veit and Mathias Walzer and David Wojnar and Witold E Wolski and Oliver Schilling and Jyoti S Choudhary and Lars Malmström and Ruedi Aebersold and Knut Reinert and Oliver Kohlbacher}, doi = {https://doi.org/10.1038/nmeth.3959}, year = {2016}, date = {2016-01-01}, journal = {Nat. Methods}, volume = {13}, number = {9}, pages = {741-748}, abstract = {High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of mass spectrometry data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.org/), a robust open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined API in C++ and Python using standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflow to carry out common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.}, keywords = {}, pubstate = {published}, tppubtype = {article} } High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of mass spectrometry data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.org/), a robust open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined API in C++ and Python using standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflow to carry out common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease. |
Veit, Johannes; Sachsenberg, Timo; Chernev, Alexsandr; Aicheler, Fabian; Urlaub, Henning; Kohlbacher, Oliver LFQProfiler and RNPxl: Open-Source Tools for Label-Free Quantification and Protein-RNA Cross-Linking Integrated into Proteome Discoverer J. Proteome Res., 15 (9), pp. 3441-8., 2016. @article{RNPxlPD_JPR_2016, title = {LFQProfiler and RNPxl: Open-Source Tools for Label-Free Quantification and Protein-RNA Cross-Linking Integrated into Proteome Discoverer}, author = {Johannes Veit and Timo Sachsenberg and Alexsandr Chernev and Fabian Aicheler and Henning Urlaub and Oliver Kohlbacher}, url = {https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00407}, year = {2016}, date = {2016-01-01}, journal = {J. Proteome Res.}, volume = {15}, number = {9}, pages = {3441-8.}, abstract = {Modern mass spectrometry setups used in today's proteomics studies generate vast amounts of raw data, calling for highly efficient data processing and analysis tools. Software for analyzing these data is either monolithic (easy to use, but sometimes too rigid) or workflow-driven (easy to customize, but sometimes complex). Thermo Proteome Discoverer (PD) is a powerful software for workflow-driven data analysis in proteomics which, in our eyes, achieves a good trade-off between flexibility and usability. Here, we present two open-source plugins for PD providing additional functionality: LFQProfiler for label-free quantification of peptides and proteins, and RNPxl for UV-induced peptide-RNA cross-linking data analysis. LFQProfiler interacts with existing PD nodes for peptide identification and validation and takes care of the entire quantitative part of the workflow. We show that it performs at least on par with other state-of-the-art software solutions for label-free quantification in a recently published benchmark ( Ramus, C.; J. Proteomics 2016 , 132 , 51 - 62 ). The second workflow, RNPxl, represents the first software solution to date for identification of peptide-RNA cross-links including automatic localization of the cross-links at amino acid resolution and localization scoring. It comes with a customized integrated cross-link fragment spectrum viewer for convenient manual inspection and validation of the results.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Modern mass spectrometry setups used in today's proteomics studies generate vast amounts of raw data, calling for highly efficient data processing and analysis tools. Software for analyzing these data is either monolithic (easy to use, but sometimes too rigid) or workflow-driven (easy to customize, but sometimes complex). Thermo Proteome Discoverer (PD) is a powerful software for workflow-driven data analysis in proteomics which, in our eyes, achieves a good trade-off between flexibility and usability. Here, we present two open-source plugins for PD providing additional functionality: LFQProfiler for label-free quantification of peptides and proteins, and RNPxl for UV-induced peptide-RNA cross-linking data analysis. LFQProfiler interacts with existing PD nodes for peptide identification and validation and takes care of the entire quantitative part of the workflow. We show that it performs at least on par with other state-of-the-art software solutions for label-free quantification in a recently published benchmark ( Ramus, C.; J. Proteomics 2016 , 132 , 51 - 62 ). The second workflow, RNPxl, represents the first software solution to date for identification of peptide-RNA cross-links including automatic localization of the cross-links at amino acid resolution and localization scoring. It comes with a customized integrated cross-link fragment spectrum viewer for convenient manual inspection and validation of the results. |
Boyles, Matthe; Ranninger, Christina; Reischl, Roland; Rurik, Marc; Tessadri, Richard; Kohlbacher, Oliver; Duschl, Albert; Huber, Christian Copper oxide nanoparticle toxicity profiling using untargeted metabolomics Part. Fiber Toxicol., 13 , pp. 49, 2016. @article{CuOTox2016, title = {Copper oxide nanoparticle toxicity profiling using untargeted metabolomics}, author = {Matthe Boyles and Christina Ranninger and Roland Reischl and Marc Rurik and Richard Tessadri and Oliver Kohlbacher and Albert Duschl and Christian Huber}, doi = {https://doi.org/10.1186/s12989-016-0160-6}, year = {2016}, date = {2016-01-01}, journal = {Part. Fiber Toxicol.}, volume = {13}, pages = {49}, abstract = {Background The rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity. Results We have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis. Conclusions Our findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation. Keywords Untargeted metabolomics Copper oxide nanoparticles Apoptosis Oxidative stress Toxicity profiling Adverse outcome pathways}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background The rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity. Results We have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis. Conclusions Our findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation. Keywords Untargeted metabolomics Copper oxide nanoparticles Apoptosis Oxidative stress Toxicity profiling Adverse outcome pathways |
Nicoludis, John M; Vogt, Bennett E; Green, Anna G; Schärfe, Charlotta PI; Marks, Debora S; Gaudet, Rachelle Antiparallel protocadherin homodimers use distinct affinity- and specificity-mediating regions in cadherin repeats 1-4 eLife, 5 , pp. e18449, 2016. @article{10.7554-eLife.18449, title = {Antiparallel protocadherin homodimers use distinct affinity- and specificity-mediating regions in cadherin repeats 1-4}, author = {John M Nicoludis and Bennett E Vogt and Anna G Green and Charlotta PI Schärfe and Debora S Marks and Rachelle Gaudet}, url = {https://dx.doi.org/10.7554/eLife.18449}, year = {2016}, date = {2016-01-01}, journal = {eLife}, volume = {5}, pages = {e18449}, abstract = {Protocadherins (Pcdhs) are cell adhesion and signaling proteins used by neurons to develop and maintain neuronal networks, relying on trans homophilic interactions between their extracellular cadherin (EC) repeat domains. We present the structure of the antiparallel EC1-4 homodimer of human PcdhγB3, a member of the γ subfamily of clustered Pcdhs. Structure and sequence comparisons of α, β, and γ clustered Pcdh isoforms illustrate that subfamilies encode specificity in distinct ways through diversification of loop region structure and composition in EC2 and EC3, which contains isoform-specific conservation of primarily polar residues. In contrast, the EC1/EC4 interface comprises hydrophobic interactions that provide non-selective dimerization affinity. Using sequence coevolution analysis, we found evidence for a similar antiparallel EC1-4 interaction in non-clustered Pcdh families. We thus deduce that the EC1-4 antiparallel homodimer is a general interaction strategy that evolved before the divergence of these distinct protocadherin families.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Protocadherins (Pcdhs) are cell adhesion and signaling proteins used by neurons to develop and maintain neuronal networks, relying on trans homophilic interactions between their extracellular cadherin (EC) repeat domains. We present the structure of the antiparallel EC1-4 homodimer of human PcdhγB3, a member of the γ subfamily of clustered Pcdhs. Structure and sequence comparisons of α, β, and γ clustered Pcdh isoforms illustrate that subfamilies encode specificity in distinct ways through diversification of loop region structure and composition in EC2 and EC3, which contains isoform-specific conservation of primarily polar residues. In contrast, the EC1/EC4 interface comprises hydrophobic interactions that provide non-selective dimerization affinity. Using sequence coevolution analysis, we found evidence for a similar antiparallel EC1-4 interaction in non-clustered Pcdh families. We thus deduce that the EC1-4 antiparallel homodimer is a general interaction strategy that evolved before the divergence of these distinct protocadherin families. |
Krüger, Jens; Thiel, Philipp; Merelli, Ivan; Grunzke, Richard; Gesing, Sandra Portals and Web-based Resources for Virtual Screening. Curr Drug Targets, 2016, (automatic medline import). @article{KrugerEtAl2016, title = {Portals and Web-based Resources for Virtual Screening.}, author = {Jens Krüger and Philipp Thiel and Ivan Merelli and Richard Grunzke and Sandra Gesing}, doi = {https://doi.org/10.2174/1389450117666160201105806}, year = {2016}, date = {2016-01-01}, journal = {Curr Drug Targets}, abstract = {Virtual screening for active compounds has become an essential step within the drug development pipeline. The computer based prediction of compound-binding modes is one of the most time and cost efficient methods for screening ligand libraries and enrich results of potential drugs. Here we present an overview about currently available online resources regarding compound databases, docking applications, and science gateways for drug discovery and virtual screening, in order to help structural biologists in choosing the best tools for their analysis. The appearance of the user interface, authentication and security aspects, data management, and computational performance will be discussed. We anticipate a broad overview about currently available solutions, guiding computational chemists and users from related fields towards scientifically reliable results.}, note = {automatic medline import}, keywords = {}, pubstate = {published}, tppubtype = {article} } Virtual screening for active compounds has become an essential step within the drug development pipeline. The computer based prediction of compound-binding modes is one of the most time and cost efficient methods for screening ligand libraries and enrich results of potential drugs. Here we present an overview about currently available online resources regarding compound databases, docking applications, and science gateways for drug discovery and virtual screening, in order to help structural biologists in choosing the best tools for their analysis. The appearance of the user interface, authentication and security aspects, data management, and computational performance will be discussed. We anticipate a broad overview about currently available solutions, guiding computational chemists and users from related fields towards scientifically reliable results. |
Hong, Henoch S; Koch, Sven D; Scheel, Birgit; Gnad-Vogt, Ulrike; Schröder, Andreas; Kallen, Karl-Josef; Wiegand, Volker; Backert, Linus; Kohlbacher, Oliver; Hoerr, Ingmar; Fotin-Mleczek, Mariola; Billingsley, James M Distinct transcriptional changes in non-small cell lung cancer patients associated with multi-antigenic RNActive® CV9201 immunotherapy Oncoimmunol., 5 (12), pp. e1249560., 2016. @article{OncoImmuno2016, title = {Distinct transcriptional changes in non-small cell lung cancer patients associated with multi-antigenic RNActive® CV9201 immunotherapy}, author = {Henoch S Hong and Sven D Koch and Birgit Scheel and Ulrike Gnad-Vogt and Andreas Schröder and Karl-Josef Kallen and Volker Wiegand and Linus Backert and Oliver Kohlbacher and Ingmar Hoerr and Mariola Fotin-Mleczek and James M Billingsley}, doi = {https://doi.org/10.1080/2162402X.2016.1249560}, year = {2016}, date = {2016-01-01}, journal = {Oncoimmunol.}, volume = {5}, number = {12}, pages = {e1249560.}, abstract = {We recently completed a phase I/IIa trial of RNActive® CV9201, a novel mRNA-based therapeutic vaccine targeting five tumor-associated antigens in non-small cell lung cancer (NSCLC) patients. The aim of the study presented here was to comprehensively analyze changes in peripheral blood during the vaccination period and to generate hypotheses facilitating the identification of potential biomarkers correlating with differential clinical outcomes post RNActive® immunotherapy. We performed whole-genome expression profiling in a subgroup of 22 stage IV NSCLC patients before and after initiation of treatment with CV9201. Utilizing an analytic approach based on blood transcriptional modules (BTMs), a previously described, sensitive tool for blood transcriptome data analysis, patients segregated into two major clusters based on transcriptional changes post RNActive® treatment. The first group of patients was characterized by the upregulation of an expression signature associated with myeloid cells and inflammation, whereas the other group exhibited an expression signature associated with T and NK cells. Patients with an enrichment of T and NK cell modules after treatment compared to baseline exhibited significantly longer progression-free and overall survival compared to patients with an upregulation of myeloid cell and inflammatory modules. Notably, these gene expression signatures were mutually exclusive and inversely correlated. Furthermore, our findings correlated with phenotypic data derived by flow cytometry as well as the neutrophil-to-lymphocyte ratio. Our study thus demonstrates non-overlapping, distinct transcriptional profiles correlating with survival warranting further validation for the development of biomarker candidates for mRNA-based immunotherapy.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We recently completed a phase I/IIa trial of RNActive® CV9201, a novel mRNA-based therapeutic vaccine targeting five tumor-associated antigens in non-small cell lung cancer (NSCLC) patients. The aim of the study presented here was to comprehensively analyze changes in peripheral blood during the vaccination period and to generate hypotheses facilitating the identification of potential biomarkers correlating with differential clinical outcomes post RNActive® immunotherapy. We performed whole-genome expression profiling in a subgroup of 22 stage IV NSCLC patients before and after initiation of treatment with CV9201. Utilizing an analytic approach based on blood transcriptional modules (BTMs), a previously described, sensitive tool for blood transcriptome data analysis, patients segregated into two major clusters based on transcriptional changes post RNActive® treatment. The first group of patients was characterized by the upregulation of an expression signature associated with myeloid cells and inflammation, whereas the other group exhibited an expression signature associated with T and NK cells. Patients with an enrichment of T and NK cell modules after treatment compared to baseline exhibited significantly longer progression-free and overall survival compared to patients with an upregulation of myeloid cell and inflammatory modules. Notably, these gene expression signatures were mutually exclusive and inversely correlated. Furthermore, our findings correlated with phenotypic data derived by flow cytometry as well as the neutrophil-to-lymphocyte ratio. Our study thus demonstrates non-overlapping, distinct transcriptional profiles correlating with survival warranting further validation for the development of biomarker candidates for mRNA-based immunotherapy. |
Thijssen, Bram; Dijkstra, Tjeerd; Heskes, Tom; Wessels, Lodewyk BCM: toolkit for Bayesian analysis of Computational Models using samplers BMC Systems Biology, 10:100 , 2016. @article{articlereference.2016-11-08.1400143489, title = {BCM: toolkit for Bayesian analysis of Computational Models using samplers}, author = {Bram Thijssen and Tjeerd Dijkstra and Tom Heskes and Lodewyk Wessels}, url = {https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-016-0339-3}, year = {2016}, date = {2016-01-01}, journal = {BMC Systems Biology}, volume = {10:100}, abstract = {We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved. |
Dietsche, Tobias; Mebrhatu, Mehari Tesfazgi; Brunner, Matthias J; Abrusci, Patrizia; Yan, Jun; Franz-Wachtel, Mirita; Schärfe, Charlotta; Zilkenat, Susann; Grin, Iwan; Galán, Jorge E; Kohlbacher, Oliver; Lea, Susan; Macek, Boris; Marlovits, Thomas C; Robinson, Carol; Wagner, Samuel Structural and functional characterization of the bacterial type III secretion export apparatus PLoS Pathogens, 12 (12), pp. e1006071., 2016. @article{PlosPath2016, title = {Structural and functional characterization of the bacterial type III secretion export apparatus}, author = {Tobias Dietsche and Mehari Tesfazgi Mebrhatu and Matthias J Brunner and Patrizia Abrusci and Jun Yan and Mirita Franz-Wachtel and Charlotta Schärfe and Susann Zilkenat and Iwan Grin and Jorge E Galán and Oliver Kohlbacher and Susan Lea and Boris Macek and Thomas C Marlovits and Carol Robinson and Samuel Wagner}, doi = {https://doi.org/10.1371/journal.ppat.1006071}, year = {2016}, date = {2016-01-01}, journal = {PLoS Pathogens}, volume = {12}, number = {12}, pages = {e1006071.}, abstract = {Bacterial type III protein secretion systems inject effector proteins into eukaryotic host cells in order to promote survival and colonization of Gram-negative pathogens and symbionts. Secretion across the bacterial cell envelope and injection into host cells is facilitated by a so-called injectisome. Its small hydrophobic export apparatus components SpaP and SpaR were shown to nucleate assembly of the needle complex and to form the central “cup” substructure of a Salmonella Typhimurium secretion system. However, the in vivo placement of these components in the needle complex and their function during the secretion process remained poorly defined. Here we present evidence that a SpaP pentamer forms a 15 Å wide pore and provide a detailed map of SpaP interactions with the export apparatus components SpaQ, SpaR, and SpaS. We further refine the current view of export apparatus assembly, consolidate transmembrane topology models for SpaP and SpaR, and present intimate interactions of the periplasmic domains of SpaP and SpaR with the inner rod protein PrgJ, indicating how export apparatus and needle filament are connected to create a continuous conduit for substrate translocation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Bacterial type III protein secretion systems inject effector proteins into eukaryotic host cells in order to promote survival and colonization of Gram-negative pathogens and symbionts. Secretion across the bacterial cell envelope and injection into host cells is facilitated by a so-called injectisome. Its small hydrophobic export apparatus components SpaP and SpaR were shown to nucleate assembly of the needle complex and to form the central “cup” substructure of a Salmonella Typhimurium secretion system. However, the in vivo placement of these components in the needle complex and their function during the secretion process remained poorly defined. Here we present evidence that a SpaP pentamer forms a 15 Å wide pore and provide a detailed map of SpaP interactions with the export apparatus components SpaQ, SpaR, and SpaS. We further refine the current view of export apparatus assembly, consolidate transmembrane topology models for SpaP and SpaR, and present intimate interactions of the periplasmic domains of SpaP and SpaR with the inner rod protein PrgJ, indicating how export apparatus and needle filament are connected to create a continuous conduit for substrate translocation. |
Krüger, Jens; Kohlbacher, Oliver Containerization and wrapping of a mass spectra prediction workflow 2016. @preprint{KruegerKohlbacher2016, title = {Containerization and wrapping of a mass spectra prediction workflow}, author = {Jens Krüger and Oliver Kohlbacher}, url = {https://peerj.com/preprints/2528/}, year = {2016}, date = {2016-01-01}, abstract = {Practical experiences are reported about implementing a workflow for the prediction of mass spectra. QCEIMS is used to simulate the fragmentation trajectories consequently leading to predicted mass spectra for small molecules, such as metabolites. The individual calculations are embedded into UNICORE workflow nodes using Docker containerization for the applications themselves. Challenges, caveats, but also advantages are discussed, providing guidance for the deployment of a scientific protocol on high performance computing resources.}, keywords = {}, pubstate = {published}, tppubtype = {preprint} } Practical experiences are reported about implementing a workflow for the prediction of mass spectra. QCEIMS is used to simulate the fragmentation trajectories consequently leading to predicted mass spectra for small molecules, such as metabolites. The individual calculations are embedded into UNICORE workflow nodes using Docker containerization for the applications themselves. Challenges, caveats, but also advantages are discussed, providing guidance for the deployment of a scientific protocol on high performance computing resources. |
Aebersold, Rudolf; Kohlbacher, Oliver; Vitek, Olga Computational Mass Spectrometry (Dagstuhl Seminar 15351) Dagstuhl Reports, 5 (8), pp. 9–33, 2016, ISSN: 2192-5283. @article{aebersold_et_al:DR:2016:5676, title = {Computational Mass Spectrometry (Dagstuhl Seminar 15351)}, author = {Rudolf Aebersold and Oliver Kohlbacher and Olga Vitek}, editor = {Rudolf Aebersold and Oliver Kohlbacher and Olga Vitek}, url = {http://drops.dagstuhl.de/opus/volltexte/2016/5676}, doi = {10.4230/DagRep.5.8.9}, issn = {2192-5283}, year = {2016}, date = {2016-01-01}, journal = {Dagstuhl Reports}, volume = {5}, number = {8}, pages = {9--33}, publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik}, address = {Dagstuhl, Germany}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Hildebrandt, Anna Katharina; Stöckel, Daniel; Fischer, Nina; de la Trevino, Luis Garza; Krüger, Jens; Nickels, Stefan; Röttig, Marc; Schärfe, Charlotta; Schumann, Marcel; Thiel, Philipp; Lenhof, Hans-Peter; Kohlbacher, Oliver; Hildebrandt, Andreas ballaxy: web services for structural bioinformatics Bioinformatics, 31 (1), pp. 121-2, 2015. @article{BALLaxyBioinfo, title = {ballaxy: web services for structural bioinformatics}, author = {Anna Katharina Hildebrandt and Daniel Stöckel and Nina Fischer and Luis de la Garza Trevino and Jens Krüger and Stefan Nickels and Marc Röttig and Charlotta Schärfe and Marcel Schumann and Philipp Thiel and Hans-Peter Lenhof and Oliver Kohlbacher and Andreas Hildebrandt}, doi = {https://doi.org/10.1093/bioinformatics/btu574}, year = {2015}, date = {2015-01-01}, journal = {Bioinformatics}, volume = {31}, number = {1}, pages = {121-2}, abstract = {MOTIVATION: Web-based workflow systems have gained considerable momentum in sequence-oriented bioinformatics. In structural bioinformatics, however, such systems are still relatively rare: while commercial stand-alone workflow applications are common in the pharmaceutical industry, academic researchers often still rely on command-line scripting to glue individual tools together. RESULTS: In this work, we address the problem of building a web-based system for workflows in structural bioinformatics. For the underlying molecular modelling engine, we opted for the BALL framework (Hildebrandt et al., 2010) due to its extensive and well tested functionality in the field of structural bioinformatics. The large number of molecular data structures and algorithms implemented in BALL allows for elegant and sophisticated development of new approaches in the field. We hence connected the versatile BALL library and its visualization and editing frontend BALLView with the Galaxy workflow framework. The result, which we call ballaxy, enables the user to simply and intuitively create sophisticated pipelines for applications in structure-based computational biology, integrated into a standard tool for molecular modelling. AVAILABILITY: ballaxy consists of three parts: some minor modifications to the Galaxy system, a collection of tools, and an integration into the BALL-framework and the BALLView application for molecular modelling. Modifications to Galaxy will be submitted to the Galaxy project, and the BALL and BALLView integrations will be integrated in the next major BALL release. After acceptance of the modifications into the Galaxy project, we will publish all ballaxy tools via the Galaxy toolshed. In the meantime, all three components are available from http://www.ball-project.org/ballaxy. Ballaxy is licensed under the terms of the GPL.}, keywords = {}, pubstate = {published}, tppubtype = {article} } MOTIVATION: Web-based workflow systems have gained considerable momentum in sequence-oriented bioinformatics. In structural bioinformatics, however, such systems are still relatively rare: while commercial stand-alone workflow applications are common in the pharmaceutical industry, academic researchers often still rely on command-line scripting to glue individual tools together. RESULTS: In this work, we address the problem of building a web-based system for workflows in structural bioinformatics. For the underlying molecular modelling engine, we opted for the BALL framework (Hildebrandt et al., 2010) due to its extensive and well tested functionality in the field of structural bioinformatics. The large number of molecular data structures and algorithms implemented in BALL allows for elegant and sophisticated development of new approaches in the field. We hence connected the versatile BALL library and its visualization and editing frontend BALLView with the Galaxy workflow framework. The result, which we call ballaxy, enables the user to simply and intuitively create sophisticated pipelines for applications in structure-based computational biology, integrated into a standard tool for molecular modelling. AVAILABILITY: ballaxy consists of three parts: some minor modifications to the Galaxy system, a collection of tools, and an integration into the BALL-framework and the BALLView application for molecular modelling. Modifications to Galaxy will be submitted to the Galaxy project, and the BALL and BALLView integrations will be integrated in the next major BALL release. After acceptance of the modifications into the Galaxy project, we will publish all ballaxy tools via the Galaxy toolshed. In the meantime, all three components are available from http://www.ball-project.org/ballaxy. Ballaxy is licensed under the terms of the GPL. |
Proikas-Cezanne, Tassula; Takacs, Zsuzsanna; Dönnes, Pierre; Kohlbacher, Oliver WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome J. Cell Sci., 128 (2), pp. 207-217, 2015. @article{WIPI_JOCES, title = {WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome}, author = {Tassula Proikas-Cezanne and Zsuzsanna Takacs and Pierre Dönnes and Oliver Kohlbacher}, doi = {https://doi.org/10.1242/jcs.146258}, year = {2015}, date = {2015-01-01}, journal = {J. Cell Sci.}, volume = {128}, number = {2}, pages = {207-217}, abstract = {Autophagy is a pivotal cytoprotective process that secures cellular homeostasis, fulfills essential roles in development, immunity and defence against pathogens, and determines the lifespan of eukaryotic organisms. However, autophagy also crucially contributes to the development of age-related human pathologies, including cancer and neurodegeneration. Macroautophagy (hereafter referred to as autophagy) clears the cytoplasm by stochastic or specific cargo recognition and destruction, and is initiated and executed by autophagy related (ATG) proteins functioning in dynamical hierarchies to form autophagosomes. Autophagosomes sequester cytoplasmic cargo material, including proteins, lipids and organelles, and acquire acidic hydrolases from the lysosomal compartment for cargo degradation. Prerequisite and essential for autophagosome formation is the production of phosphatidylinositol 3-phosphate (PtdIns3P) by phosphatidylinositol 3-kinase class III (PI3KC3, also known as PIK3C3) in complex with beclin 1, p150 (also known as PIK3R4; Vps15 in yeast) and ATG14L. Members of the human WD-repeat protein interacting with phosphoinositides (WIPI) family play an important role in recognizing and decoding the PtdIns3P signal at the nascent autophagosome, and hence function as autophagy-specific PtdIns3P-binding effectors, similar to their ancestral yeast Atg18 homolog. The PtdIns3P effector function of human WIPI proteins appears to be compromised in cancer and neurodegeneration, and WIPI genes and proteins might present novel targets for rational therapies. Here, we summarize the current knowledge on the roles of the four human WIPI proteins, WIPI1-4, in autophagy. This article is part of a Focus on Autophagosome biogenesis. For further reading, please see related articles: 'ERES: sites for autophagosome biogenesis and maturation?' by Jana Sanchez-Wandelmer et al. (J. Cell Sci. 128, 185-192) and 'Membrane dynamics in autophagosome biogenesis' by Sven R. Carlsson and Anne Simonsen (J. Cell Sci. 128, 193-205).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Autophagy is a pivotal cytoprotective process that secures cellular homeostasis, fulfills essential roles in development, immunity and defence against pathogens, and determines the lifespan of eukaryotic organisms. However, autophagy also crucially contributes to the development of age-related human pathologies, including cancer and neurodegeneration. Macroautophagy (hereafter referred to as autophagy) clears the cytoplasm by stochastic or specific cargo recognition and destruction, and is initiated and executed by autophagy related (ATG) proteins functioning in dynamical hierarchies to form autophagosomes. Autophagosomes sequester cytoplasmic cargo material, including proteins, lipids and organelles, and acquire acidic hydrolases from the lysosomal compartment for cargo degradation. Prerequisite and essential for autophagosome formation is the production of phosphatidylinositol 3-phosphate (PtdIns3P) by phosphatidylinositol 3-kinase class III (PI3KC3, also known as PIK3C3) in complex with beclin 1, p150 (also known as PIK3R4; Vps15 in yeast) and ATG14L. Members of the human WD-repeat protein interacting with phosphoinositides (WIPI) family play an important role in recognizing and decoding the PtdIns3P signal at the nascent autophagosome, and hence function as autophagy-specific PtdIns3P-binding effectors, similar to their ancestral yeast Atg18 homolog. The PtdIns3P effector function of human WIPI proteins appears to be compromised in cancer and neurodegeneration, and WIPI genes and proteins might present novel targets for rational therapies. Here, we summarize the current knowledge on the roles of the four human WIPI proteins, WIPI1-4, in autophagy. This article is part of a Focus on Autophagosome biogenesis. For further reading, please see related articles: 'ERES: sites for autophagosome biogenesis and maturation?' by Jana Sanchez-Wandelmer et al. (J. Cell Sci. 128, 185-192) and 'Membrane dynamics in autophagosome biogenesis' by Sven R. Carlsson and Anne Simonsen (J. Cell Sci. 128, 193-205). |
Thost, Ann-Kathrin; Dönnes, Pierre; Kohlbacher, Oliver; Proikas-Cezanne, Tassula Fluorescence-based imaging of autophagy progression by human WIPI beta-propeller protein detection in single cells Methods, 75 , pp. 69-78, 2015. @article{WIPI_Meth, title = {Fluorescence-based imaging of autophagy progression by human WIPI beta-propeller protein detection in single cells}, author = {Ann-Kathrin Thost and Pierre Dönnes and Oliver Kohlbacher and Tassula Proikas-Cezanne}, doi = {https://doi.org/10.1016/j.ymeth.2014.11.011}, year = {2015}, date = {2015-01-01}, journal = {Methods}, volume = {75}, pages = {69-78}, abstract = {Central to the process of macroautophagy (hereafter autophagy) is the formation of autophagosomes, double-membrane vesicles that sequester cytoplasmic cargo, including proteins, lipids and organelles, for lysosomal degradation and macromolecule recycling. Tight regulation of both autophagic activity and capacity is crucial to secure cellular homeostasis and aberrant autophagy is tightly linked to the development of many human diseases. Hence it is of great importance to accurately measure autophagy progression in health and disease. Members of the human WIPI β-propeller proteins associate with autophagosomal membranes due to specific phosphatidylinositol 3-phosphate (PtdIns3P) binding at the onset of autophagy. The specific autophagosomal localization of both WIPI1 and WIPI2 (refered to as WIPI puncta) has been employed to assess autophagy using fluorescence microscopy methods, such as confocal and live-cell video microscopy and was extended for automated high-throughput image acquisition and analyses procedures. We here provide an overview on the employment of human WIPI members for the assessment of autophagy in higher eukaryotic cells, suitable for systems biology approaches such as mathematical modelling.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Central to the process of macroautophagy (hereafter autophagy) is the formation of autophagosomes, double-membrane vesicles that sequester cytoplasmic cargo, including proteins, lipids and organelles, for lysosomal degradation and macromolecule recycling. Tight regulation of both autophagic activity and capacity is crucial to secure cellular homeostasis and aberrant autophagy is tightly linked to the development of many human diseases. Hence it is of great importance to accurately measure autophagy progression in health and disease. Members of the human WIPI β-propeller proteins associate with autophagosomal membranes due to specific phosphatidylinositol 3-phosphate (PtdIns3P) binding at the onset of autophagy. The specific autophagosomal localization of both WIPI1 and WIPI2 (refered to as WIPI puncta) has been employed to assess autophagy using fluorescence microscopy methods, such as confocal and live-cell video microscopy and was extended for automated high-throughput image acquisition and analyses procedures. We here provide an overview on the employment of human WIPI members for the assessment of autophagy in higher eukaryotic cells, suitable for systems biology approaches such as mathematical modelling. |
Sachsenberg, Timo; Herbst, Florian-Alexander; Taubert, Martin; Kermer, René; Jehmlich, Nico; von Bergen, Martin; Seifert, Jana; Kohlbacher, Oliver MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics J. Proteome Res., 14 (2), pp. 619-27, 2015. @article{MetaProSIP_2015, title = {MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics}, author = {Timo Sachsenberg and Florian-Alexander Herbst and Martin Taubert and René Kermer and Nico Jehmlich and Martin von Bergen and Jana Seifert and Oliver Kohlbacher}, url = {https://pubs.acs.org/doi/abs/10.1021/pr500245w}, year = {2015}, date = {2015-01-01}, journal = {J. Proteome Res.}, volume = {14}, number = {2}, pages = {619-27}, abstract = {We propose a joint experimental and theoretical approach to the automated reconstruction of elemental fluxes in microbial communities. While stable isotope probing of proteins (protein-SIP) has been successfully applied to study interactions and elemental carbon and nitrogen fluxes, the volume and complexity of mass spectrometric data in protein-SIP experiments poses new challenges for data analysis. Together with a flexible experimental setup, the novel bioinformatics tool MetaProSIP offers an automated high-throughput solution for a wide range of 13C or 15N protein-SIP experiments with special emphasis on the analysis of metaproteomic experiments where differential labeling of organisms can occur. The information calculated in MetaProSIP includes the determination of multiple relative isotopic abundances, the labeling ratio between old and new synthesized proteins and the shape of the isotopic distribution. These parameters define the metabolic capacities and dynamics within the investigated microbial culture. MetaProSIP features a high degree of reproducibility, reliability, and quality control reporting. Embedding into the OpenMS framework allows for flexible construction of custom-tailored workflows. Software and documentation are available under an open-source license at www.openms.de/MetaProSIP.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We propose a joint experimental and theoretical approach to the automated reconstruction of elemental fluxes in microbial communities. While stable isotope probing of proteins (protein-SIP) has been successfully applied to study interactions and elemental carbon and nitrogen fluxes, the volume and complexity of mass spectrometric data in protein-SIP experiments poses new challenges for data analysis. Together with a flexible experimental setup, the novel bioinformatics tool MetaProSIP offers an automated high-throughput solution for a wide range of 13C or 15N protein-SIP experiments with special emphasis on the analysis of metaproteomic experiments where differential labeling of organisms can occur. The information calculated in MetaProSIP includes the determination of multiple relative isotopic abundances, the labeling ratio between old and new synthesized proteins and the shape of the isotopic distribution. These parameters define the metabolic capacities and dynamics within the investigated microbial culture. MetaProSIP features a high degree of reproducibility, reliability, and quality control reporting. Embedding into the OpenMS framework allows for flexible construction of custom-tailored workflows. Software and documentation are available under an open-source license at www.openms.de/MetaProSIP. |
Gerasch, Andreas; Küntzer, Jan; Niermann, Peter; Stöckel, Daniel; Kohlbacher, Oliver; Lenhof, Hans-Peter Network-based interactive navigation and analysis of large biological datasets it - Information Technology, 57 (1), pp. 37-48, 2015. @article{itITVisAnalytics2015, title = {Network-based interactive navigation and analysis of large biological datasets}, author = {Andreas Gerasch and Jan Küntzer and Peter Niermann and Daniel Stöckel and Oliver Kohlbacher and Hans-Peter Lenhof}, doi = {https://doi.org/10.1515/itit-2014-1076}, year = {2015}, date = {2015-01-01}, journal = {it - Information Technology}, volume = {57}, number = {1}, pages = {37-48}, abstract = {Over the last decade, advances in high-throughput technologies have resulted in a flood of new biological data. Here, individual samples can extend up into terabyte size. While potential applications are broad, ranging from biotechnology to medical applications, the analysis of these datasets poses massive challenges. In order to make use of the produced terabytes of data, these datasets need to be integrated, need to be mapped onto existing biological knowledge, and need to be explored by experts. We present UniPAX and BiNA, a scalable system for the integration and analysis of high-throughput data (genomics, transcriptomics, proteomics, and metabolomics) in a network context. A central data warehouse holds the core dataset. A flexible middleware can execute custom queries on these datasets and communicate with our visual analytics tool BiNA, the Biological Network Analyzer. We demonstrate how the combination of these tools permits an efficient analysis of large-scale datasets for medical applications.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Over the last decade, advances in high-throughput technologies have resulted in a flood of new biological data. Here, individual samples can extend up into terabyte size. While potential applications are broad, ranging from biotechnology to medical applications, the analysis of these datasets poses massive challenges. In order to make use of the produced terabytes of data, these datasets need to be integrated, need to be mapped onto existing biological knowledge, and need to be explored by experts. We present UniPAX and BiNA, a scalable system for the integration and analysis of high-throughput data (genomics, transcriptomics, proteomics, and metabolomics) in a network context. A central data warehouse holds the core dataset. A flexible middleware can execute custom queries on these datasets and communicate with our visual analytics tool BiNA, the Biological Network Analyzer. We demonstrate how the combination of these tools permits an efficient analysis of large-scale datasets for medical applications. |
Ziller, Michael J; Edri, Reuven; Yaffe, Yakey; Donaghey, Julie; Pop, Ramona; Mallard, William; Issner, Robbyn; Gifford, Casey A; Goren, Alon; Xing, Jeffrey; Gu, Hongcang; Cacchiarelli, Davide; Tsankov, Alexander M; Epstein, Charles; Rinn, John L; Mikkelsen, Tarjei S; Kohlbacher, Oliver; Gnirke, Andreas; Bernstein, Bradley E; Elkabetz, Yechiel; Meissner, Alexander Dissecting neural differentiation regulatory networks through epigenetic footprinting. Nature, 518 , pp. 355-359, 2015. @article{Ziller2015, title = {Dissecting neural differentiation regulatory networks through epigenetic footprinting.}, author = {Michael J Ziller and Reuven Edri and Yakey Yaffe and Julie Donaghey and Ramona Pop and William Mallard and Robbyn Issner and Casey A Gifford and Alon Goren and Jeffrey Xing and Hongcang Gu and Davide Cacchiarelli and Alexander M Tsankov and Charles Epstein and John L Rinn and Tarjei S Mikkelsen and Oliver Kohlbacher and Andreas Gnirke and Bradley E Bernstein and Yechiel Elkabetz and Alexander Meissner}, url = {http://dx.doi.org/10.1038/nature13990}, year = {2015}, date = {2015-01-01}, journal = {Nature}, volume = {518}, pages = {355-359}, abstract = {Models derived from human pluripotent stem cells that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system. Here we report the transcriptional and epigenomic analysis of six consecutive neural progenitor cell stages derived from a HES5::eGFP reporter human embryonic stem cell line. Using this system, we aimed to model cell-fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and suggest here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Models derived from human pluripotent stem cells that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system. Here we report the transcriptional and epigenomic analysis of six consecutive neural progenitor cell stages derived from a HES5::eGFP reporter human embryonic stem cell line. Using this system, we aimed to model cell-fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and suggest here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation. |
Aiche, Stephan; Sachsenberg, Timo; Kenar, Erhan; Walzer, Mathias; Wiswedel, Bernd; Kristl, Theresa; Boyles, Matthew; Duschl, Albert; Huber, Christian; Berthold, Michael; Reinert, Knut; Kohlbacher, Oliver Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry Proteomics, 15 (8), pp. 1443-7, 2015. @article{ProtWFVIs2015, title = {Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry}, author = {Stephan Aiche and Timo Sachsenberg and Erhan Kenar and Mathias Walzer and Bernd Wiswedel and Theresa Kristl and Matthew Boyles and Albert Duschl and Christian Huber and Michael Berthold and Knut Reinert and Oliver Kohlbacher}, doi = {https://doi.org/10.1002/pmic.201400391}, year = {2015}, date = {2015-01-01}, journal = {Proteomics}, volume = {15}, number = {8}, pages = {1443-7}, abstract = {Mass spectrometry-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational mass spectrometry from the open-source software framework OpenMS into the workflow engine KNIME for the analysis of large data sets and production of high quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational mass spectrometry: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Mass spectrometry-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational mass spectrometry from the open-source software framework OpenMS into the workflow engine KNIME for the analysis of large data sets and production of high quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational mass spectrometry: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments. |
Schubert, Benjamin; Brachvogel, Hans-Philipp; Jürges, Christopher; Kohlbacher, Oliver EpiToolKit – A Web-based Workbench for Vaccine Design Bioinformatics, 31 (13), pp. 2211-3, 2015. @article{ETK2015, title = {EpiToolKit – A Web-based Workbench for Vaccine Design}, author = {Benjamin Schubert and Hans-Philipp Brachvogel and Christopher Jürges and Oliver Kohlbacher}, doi = {https://doi.org/10.1093/bioinformatics/btv116}, year = {2015}, date = {2015-01-01}, journal = {Bioinformatics}, volume = {31}, number = {13}, pages = {2211-3}, abstract = {EpiToolKit is a virtual workbench for immunological questions with a focus on vaccine design. It offers an array of immunoinformatics tools covering MHC genotyping, epitope and neo-epitope prediction, epitope selection for vaccine design, and epitope assembly. In its recently re-implemented version 2.0, EpiToolKit provides a range of new functionality and for the first time allows combining tools into complex workflows. For inexperienced users it offers simplified interfaces to guide the users through the analysis of complex immunological data sets.}, keywords = {}, pubstate = {published}, tppubtype = {article} } EpiToolKit is a virtual workbench for immunological questions with a focus on vaccine design. It offers an array of immunoinformatics tools covering MHC genotyping, epitope and neo-epitope prediction, epitope selection for vaccine design, and epitope assembly. In its recently re-implemented version 2.0, EpiToolKit provides a range of new functionality and for the first time allows combining tools into complex workflows. For inexperienced users it offers simplified interfaces to guide the users through the analysis of complex immunological data sets. |
Friedrich, Andreas; Kenar, Erhan; Kohlbacher, Oliver; Nahnsen, Sven Intuitive Web-based Experimental Design for High-throughput Biomedical Data BioMed Res Int, 2015 , pp. 958302, 2015. @article{articlereference.2015-03-10.0971887022, title = {Intuitive Web-based Experimental Design for High-throughput Biomedical Data}, author = {Andreas Friedrich and Erhan Kenar and Oliver Kohlbacher and Sven Nahnsen}, doi = {https://doi.org/10.1155/2015/958302}, year = {2015}, date = {2015-01-01}, journal = {BioMed Res Int}, volume = {2015}, pages = {958302}, abstract = {Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model. |
Martens, Lennart; Kohlbacher, Oliver; Weintraub, Susan T Managing Expectations when Publishing Tools and Methods for Computational Proteomics J. Proteome Res., 14 (5), pp. 2002-4, 2015. @article{JPR_Expectations_2015, title = {Managing Expectations when Publishing Tools and Methods for Computational Proteomics}, author = {Lennart Martens and Oliver Kohlbacher and Susan T Weintraub}, url = {http://pubs.acs.org/doi/abs/10.1021/pr501318d}, year = {2015}, date = {2015-01-01}, journal = {J. Proteome Res.}, volume = {14}, number = {5}, pages = {2002-4}, abstract = {Computational tools are pivotal in proteomics because they are crucial for identification, quantification, and statistical assessment of data. The gateway to finding the best choice of a tool or approach for a particular problem is frequently journal articles. Yet, there is often an overwhelming variety of options that makes it hard to decide on the best solution. This is particularly difficult for non-experts in bioinformatics. The maturity, reliability, and performance of tools can vary widely, since publications may appear at different stages of development. A novel idea might merit early publication despite only offering proof-of-principle, while it may take years before a tool can be considered mature, and by that time it might be difficult for a new publication to be accepted because of a perceived lack of novelty. After discussions with members of the computational mass spectrometry community, we describe here proposed recommendations for organization of informatics manuscripts as a way to set the expectations of readers (and reviewers) through three different manuscript types that are based on existing journal designations. Brief Communications are short reports describing novel computational approaches where the implementation is not necessarily production-ready. Research Articles present both a novel idea and mature implementation that has been suitably benchmarked. Application Notes focus on a mature and tested tool or concept, and need not be novel but should offer advancement from improved quality, ease of use and/or implementation. Organizing computational proteomics contributions into these three manuscript types will facilitate the review process and will also enable readers to identify the maturity and applicability of the tool for their own workflows.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Computational tools are pivotal in proteomics because they are crucial for identification, quantification, and statistical assessment of data. The gateway to finding the best choice of a tool or approach for a particular problem is frequently journal articles. Yet, there is often an overwhelming variety of options that makes it hard to decide on the best solution. This is particularly difficult for non-experts in bioinformatics. The maturity, reliability, and performance of tools can vary widely, since publications may appear at different stages of development. A novel idea might merit early publication despite only offering proof-of-principle, while it may take years before a tool can be considered mature, and by that time it might be difficult for a new publication to be accepted because of a perceived lack of novelty. After discussions with members of the computational mass spectrometry community, we describe here proposed recommendations for organization of informatics manuscripts as a way to set the expectations of readers (and reviewers) through three different manuscript types that are based on existing journal designations. Brief Communications are short reports describing novel computational approaches where the implementation is not necessarily production-ready. Research Articles present both a novel idea and mature implementation that has been suitably benchmarked. Application Notes focus on a mature and tested tool or concept, and need not be novel but should offer advancement from improved quality, ease of use and/or implementation. Organizing computational proteomics contributions into these three manuscript types will facilitate the review process and will also enable readers to identify the maturity and applicability of the tool for their own workflows. |
Uszkoreit, Julian; Maerkens, Alexandra; Perez-Riverol, Yasset; Meyer, Helmut E; Marcus, Katrin; Stephan, Christian; Kohlbacher, Oliver; Eisenacher, Martin PIA - An intuitive protein inference engine with a web-based user interface J. Proteome Res., 14 (7), pp. 2988-97, 2015. @article{PIA_JPR_2015, title = {PIA - An intuitive protein inference engine with a web-based user interface}, author = {Julian Uszkoreit and Alexandra Maerkens and Yasset Perez-Riverol and Helmut E Meyer and Katrin Marcus and Christian Stephan and Oliver Kohlbacher and Martin Eisenacher}, doi = {https://doi.org/10.1021/acs.jproteome.5b00121}, year = {2015}, date = {2015-01-01}, journal = {J. Proteome Res.}, volume = {14}, number = {7}, pages = {2988-97}, abstract = {Protein inference connects the peptide-spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can either be run locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark datasets we can show that PIA can identify a larger number of proteins at the same protein FDR when compared to inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available from https://github.com/mpc-bioinformatics/pia.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Protein inference connects the peptide-spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can either be run locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark datasets we can show that PIA can identify a larger number of proteins at the same protein FDR when compared to inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available from https://github.com/mpc-bioinformatics/pia. |
Sharma, Kundan; Hrle, Ajla; Kramer, Katharina; Sachsenberg, Timo; Staals, Raymond H J; Randau, Lennart; Marchfelder, Anita; van der Oost, John; Kohlbacher, Oliver; Conti, Elena; Urlaub, Henning Analysis of protein-RNA interactions in CRISPR proteins and effector complexes by UV-induced cross-linking and mass spectrometry Methods, pp. S1046-2023(15)00246-7, 2015. @article{RNPxlMethods, title = {Analysis of protein-RNA interactions in CRISPR proteins and effector complexes by UV-induced cross-linking and mass spectrometry}, author = {Kundan Sharma and Ajla Hrle and Katharina Kramer and Timo Sachsenberg and Raymond H J Staals and Lennart Randau and Anita Marchfelder and John van der Oost and Oliver Kohlbacher and Elena Conti and Henning Urlaub}, doi = {https://doi.org/10.1016/j.ymeth.2015.06.005}, year = {2015}, date = {2015-01-01}, journal = {Methods}, pages = {S1046-2023(15)00246-7}, abstract = {Ribonucleoprotein (RNP) complexes play important roles in the cell by mediating basic cellular processes, including gene expression and its regulation. Understanding the molecular details of these processes requires the identification and characterization of protein-RNA interactions. Over the years various approaches have been used to investigate these interactions, including computational analyses to look for RNA binding domains, gel-shift mobility assays on recombinant and mutant proteins as well as co-crystallization and NMR studies for structure elucidation. Here we report a more specialized and direct approach using UV-induced cross-linking coupled with mass spectrometry. This approach permits the identification of cross-linked peptides and RNA moieties and can also pin-point exact RNA contact sites within the protein. The power of this method is illustrated by the application to different single- and multi-subunit RNP complexes belonging to the prokaryotic adaptive immune system, CRISPR-Cas (CRISPR: clustered regularly interspaced short palindromic repeats; Cas: CRISPR associated). In particular, we identified the RNA-binding sites within three Cas7 protein homologs and mapped the cross-linking results to reveal structurally conserved Cas7 - RNA binding interfaces. These results demonstrate the strong potential of UV-induced cross-linking coupled with mass spectrometry analysis to identify RNA interaction sites on the RNA binding proteins.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Ribonucleoprotein (RNP) complexes play important roles in the cell by mediating basic cellular processes, including gene expression and its regulation. Understanding the molecular details of these processes requires the identification and characterization of protein-RNA interactions. Over the years various approaches have been used to investigate these interactions, including computational analyses to look for RNA binding domains, gel-shift mobility assays on recombinant and mutant proteins as well as co-crystallization and NMR studies for structure elucidation. Here we report a more specialized and direct approach using UV-induced cross-linking coupled with mass spectrometry. This approach permits the identification of cross-linked peptides and RNA moieties and can also pin-point exact RNA contact sites within the protein. The power of this method is illustrated by the application to different single- and multi-subunit RNP complexes belonging to the prokaryotic adaptive immune system, CRISPR-Cas (CRISPR: clustered regularly interspaced short palindromic repeats; Cas: CRISPR associated). In particular, we identified the RNA-binding sites within three Cas7 protein homologs and mapped the cross-linking results to reveal structurally conserved Cas7 - RNA binding interfaces. These results demonstrate the strong potential of UV-induced cross-linking coupled with mass spectrometry analysis to identify RNA interaction sites on the RNA binding proteins. |
Ranninger, Christina; Rurik, Marc; Limonciel, Alice; Ruzek, Silke; Reischl, Roland; Wilmes, Anja; Jennings, Paul; Hewitt, Philip; Dekant, Wolfgang; Kohlbacher, Oliver; Huber, Christian G Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High-Performance Liquid Chromatography-Mass Spectrometry-Based Experimental and Computational Pipeline J. Biol. Chem., 290 (31), pp. 19121-32, 2015. @article{JBCNephronTox2015, title = {Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High-Performance Liquid Chromatography-Mass Spectrometry-Based Experimental and Computational Pipeline}, author = {Christina Ranninger and Marc Rurik and Alice Limonciel and Silke Ruzek and Roland Reischl and Anja Wilmes and Paul Jennings and Philip Hewitt and Wolfgang Dekant and Oliver Kohlbacher and Christian G Huber}, doi = {https://doi.org/10.1074/jbc.M115.644146}, year = {2015}, date = {2015-01-01}, journal = {J. Biol. Chem.}, volume = {290}, number = {31}, pages = {19121-32}, abstract = {Untargeted metabolomics has the potential to improve the predictivity of in vitro toxicity models and therefore may aid the replacement of expensive and laborious animal models. Here we describe a long-term repeat dose nephrotoxicity study conducted on the human renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 µmol.L-1 and 35 µmol.L-1 of chloroacetaldehyde - a metabolite of the anti-cancer drug ifosfamide. Our study outlines the establishment of an automated and easy to use untargeted metabolomics workflow for HPLC-HRMS data. Automated data analysis workflows based on open-source software (OpenMS, KNIME) enabled a comprehensive and reproducible analysis of the complex and voluminous metabolomics data produced by the profiling approach. Time- and concentration dependent responses were clearly evident in the metabolomic profiles. In order to obtain a more comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated. For toxicity profiling of chloroacetaldehyde, 428 and 317 metabolite features were detectable in positive and negative mode, respectively, after stringent removal of chemical noise and unstable signals. Changes upon treatment were explored using principal component analysis (PCA) and statistically significant differences were identified using linear models (LIMMA). The analysis revealed toxic effects only for the treatment with 35 µmol.L-1 for 3 and 14 days. The most regulated metabolites were glutathione and metabolites related to the oxidative stress response of the cells. These findings are corroborated by proteomics and transcriptomics data, which show, amongst others, an activation of the Nrf2 and ATF4 pathways.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Untargeted metabolomics has the potential to improve the predictivity of in vitro toxicity models and therefore may aid the replacement of expensive and laborious animal models. Here we describe a long-term repeat dose nephrotoxicity study conducted on the human renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 µmol.L-1 and 35 µmol.L-1 of chloroacetaldehyde - a metabolite of the anti-cancer drug ifosfamide. Our study outlines the establishment of an automated and easy to use untargeted metabolomics workflow for HPLC-HRMS data. Automated data analysis workflows based on open-source software (OpenMS, KNIME) enabled a comprehensive and reproducible analysis of the complex and voluminous metabolomics data produced by the profiling approach. Time- and concentration dependent responses were clearly evident in the metabolomic profiles. In order to obtain a more comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated. For toxicity profiling of chloroacetaldehyde, 428 and 317 metabolite features were detectable in positive and negative mode, respectively, after stringent removal of chemical noise and unstable signals. Changes upon treatment were explored using principal component analysis (PCA) and statistically significant differences were identified using linear models (LIMMA). The analysis revealed toxic effects only for the treatment with 35 µmol.L-1 for 3 and 14 days. The most regulated metabolites were glutathione and metabolites related to the oxidative stress response of the cells. These findings are corroborated by proteomics and transcriptomics data, which show, amongst others, an activation of the Nrf2 and ATF4 pathways. |
Simha, Ramanuja; Briesemeister, Sebastian; Kohlbacher, Oliver; Shatkay, Hagit Protein (Multi-)Location Prediction: Utilizing Interdependencies via a Generative Model Bioinformatics, 31 (12), pp. i365-i374, 2015. @article{GenModelISMB2015, title = {Protein (Multi-)Location Prediction: Utilizing Interdependencies via a Generative Model}, author = {Ramanuja Simha and Sebastian Briesemeister and Oliver Kohlbacher and Hagit Shatkay}, url = {http://bioinformatics.oxfordjournals.org/content/31/12/i365.abstract?sid=eeb20637-6479-4328-afe6-8916faadafd8}, year = {2015}, date = {2015-01-01}, journal = {Bioinformatics}, volume = {31}, number = {12}, pages = {i365-i374}, abstract = {Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein’s function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins. Results: We introduce a probabilistic generative model for protein localization, and develop a system based on it—which we call MDLoc—that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems. Availability and implementation: MDLoc is available at: http://www.eecis.udel.edu/∼compbio/mdloc.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein’s function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins. Results: We introduce a probabilistic generative model for protein localization, and develop a system based on it—which we call MDLoc—that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems. Availability and implementation: MDLoc is available at: http://www.eecis.udel.edu/∼compbio/mdloc. |
Walz, Simon; Stickel, Juliane Sarah; Kowalewski, Daniel Johannes; Schuster, Heiko; Weisel, Katja; Backert, Linus; Kahn, Stefan; Nelde, Annika; Stroh, Tatjana; Handel, Martina; Kohlbacher, Oliver; Kanz, Lothar; Salih, Helmut Rainer; Rammensee, Hans-Georg; Stevanovic, Stefan The antigenic landscape of multiple myeloma: mass spectrometry (re-)defines targets for T-cell based immunotherapy Blood, 126 (10), pp. 1203-13, 2015. @article{BloodMyeloma2015, title = {The antigenic landscape of multiple myeloma: mass spectrometry (re-)defines targets for T-cell based immunotherapy}, author = {Simon Walz and Juliane Sarah Stickel and Daniel Johannes Kowalewski and Heiko Schuster and Katja Weisel and Linus Backert and Stefan Kahn and Annika Nelde and Tatjana Stroh and Martina Handel and Oliver Kohlbacher and Lothar Kanz and Helmut Rainer Salih and Hans-Georg Rammensee and Stefan Stevanovic}, doi = {https://doi.org/10.1182/blood-2015-04-640532}, year = {2015}, date = {2015-01-01}, journal = {Blood}, volume = {126}, number = {10}, pages = {1203-13}, abstract = {Direct analysis of HLA presented antigens by mass spectrometry provides a comprehensive view on the antigenic landscape of different tissues/malignancies and enables the identification of novel, pathophysiologically relevant T-cell epitopes. Here we present a systematic and comparative study of the HLA class I and II presented, non-mutant antigenome of multiple myeloma (MM). Quantification of HLA surface expression revealed elevated HLA molecule counts on malignant plasma cells compared to normal B cells, excluding relevant HLA down-regulation in MM. Analyzing the presentation of established myeloma-associated T-cell antigens on the HLA ligandome level, we found a substantial proportion of antigens to be only infrequently presented on primary myelomas or to display suboptimal degrees of myeloma-specificity. However, unsupervised analysis of our extensive HLA ligand dataset delineated a panel of 58 highly specific myeloma-associated antigens -including MMSET- which are characterized by frequent and exclusive presentation on myeloma samples. Functional characterization of these target antigens revealed peptide-specific, pre-existing CD8+ T-cell responses exclusively in myeloma patients, which is indicative of pathophysiological relevance. Furthermore, in vitro priming experiments revealed that peptide-specific T-cell responses can be induced in response-naïve myeloma patients. Together, our results serve to guide antigen selection for T-cell based immunotherapy of MM.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Direct analysis of HLA presented antigens by mass spectrometry provides a comprehensive view on the antigenic landscape of different tissues/malignancies and enables the identification of novel, pathophysiologically relevant T-cell epitopes. Here we present a systematic and comparative study of the HLA class I and II presented, non-mutant antigenome of multiple myeloma (MM). Quantification of HLA surface expression revealed elevated HLA molecule counts on malignant plasma cells compared to normal B cells, excluding relevant HLA down-regulation in MM. Analyzing the presentation of established myeloma-associated T-cell antigens on the HLA ligandome level, we found a substantial proportion of antigens to be only infrequently presented on primary myelomas or to display suboptimal degrees of myeloma-specificity. However, unsupervised analysis of our extensive HLA ligand dataset delineated a panel of 58 highly specific myeloma-associated antigens -including MMSET- which are characterized by frequent and exclusive presentation on myeloma samples. Functional characterization of these target antigens revealed peptide-specific, pre-existing CD8+ T-cell responses exclusively in myeloma patients, which is indicative of pathophysiological relevance. Furthermore, in vitro priming experiments revealed that peptide-specific T-cell responses can be induced in response-naïve myeloma patients. Together, our results serve to guide antigen selection for T-cell based immunotherapy of MM. |
Aicheler, Fabian; Li, Jia; Lehmann, Rainer; Xu, Guowang; Kohlbacher, Oliver Retention Time Prediction Improves Identification in Non-Targeted Lipidomics Approaches Anal. Chem., 87 (15), pp. 7698-704, 2015. @article{LipidRT2015, title = {Retention Time Prediction Improves Identification in Non-Targeted Lipidomics Approaches}, author = {Fabian Aicheler and Jia Li and Rainer Lehmann and Guowang Xu and Oliver Kohlbacher}, doi = {https://doi.org/10.1021/acs.analchem.5b01139}, year = {2015}, date = {2015-01-01}, journal = {Anal. Chem.}, volume = {87}, number = {15}, pages = {7698-704}, abstract = {Identification of lipids in non-targeted lipidomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) is still a major issue. While both accurate mass and fragment spectra contain valuable information, retention time (RT) information can be used to augment this data. We present a retention time model based on machine learning approaches which enables an improved assignment of lipid structures and automated annotation of lipidomics data. In contrast to common approaches we used a complex mixture of 201 lipids originating from fat tissue instead of a standard mixture to train a support vector regression (SVR) model including molecular structural features. The cross-validated model achieves correlation coefficients between predicted and experimental retention times of r = 0.989. Of note, as few as 50 reference lipids of different classes are sufficient to adapt to different chromatographic setups. Combining our retention time model with identification via accurate mass search (AMS) of lipids against the comprehensive LIPID MAPS database, retention time filtering can significantly reduce the rate of false positives in complex data sets like adipose tissue extracts. In our case, filtering with retention time information removed more than half of the potential identifications, while retaining 95 % of the correct identifications. Combination of high-precision retention time prediction and accurate mass can thus significantly narrow down the number of hypotheses to be assessed for lipid identification in complex lipid pattern like tissue profiles.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Identification of lipids in non-targeted lipidomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) is still a major issue. While both accurate mass and fragment spectra contain valuable information, retention time (RT) information can be used to augment this data. We present a retention time model based on machine learning approaches which enables an improved assignment of lipid structures and automated annotation of lipidomics data. In contrast to common approaches we used a complex mixture of 201 lipids originating from fat tissue instead of a standard mixture to train a support vector regression (SVR) model including molecular structural features. The cross-validated model achieves correlation coefficients between predicted and experimental retention times of r = 0.989. Of note, as few as 50 reference lipids of different classes are sufficient to adapt to different chromatographic setups. Combining our retention time model with identification via accurate mass search (AMS) of lipids against the comprehensive LIPID MAPS database, retention time filtering can significantly reduce the rate of false positives in complex data sets like adipose tissue extracts. In our case, filtering with retention time information removed more than half of the potential identifications, while retaining 95 % of the correct identifications. Combination of high-precision retention time prediction and accurate mass can thus significantly narrow down the number of hypotheses to be assessed for lipid identification in complex lipid pattern like tissue profiles. |
de Lange, Orlando; Wolf, Christina; Thiel, Philipp; Krüger, Jens; Kleusch, Christian; Kohlbacher, Oliver; Lahaye, Thomas DNA-binding proteins from marine bacteria expand the sequence diversity of known TALE-like repeats Nucl. Acids Res, 43 (20), pp. 10065-80, 2015. @article{MOrTLs-NAR-2015, title = {DNA-binding proteins from marine bacteria expand the sequence diversity of known TALE-like repeats}, author = {Orlando de Lange and Christina Wolf and Philipp Thiel and Jens Krüger and Christian Kleusch and Oliver Kohlbacher and Thomas Lahaye}, url = {http://nar.oxfordjournals.org/content/early/2015/10/19/nar.gkv1053.abstract}, year = {2015}, date = {2015-01-01}, journal = {Nucl. Acids Res}, volume = {43}, number = {20}, pages = {10065-80}, abstract = {Transcription Activator-Like Effectors (TALEs) of Xanthomonas bacteria are programmable DNA binding proteins with unprecedented target specificity. Comparative studies into TALE repeat structure and function are hindered by the limited sequence variation among TALE repeats. More sequence-diverse TALE-like proteins are known from Ralstonia solanacearum (RipTALs) and Burkholderia rhizoxinica (Bats), but RipTAL and Bat repeats are conserved with those of TALEs around the DNA-binding residue. We study two novel marine-organism TALE-like proteins (MOrTL1 and MOrTL2), the first to date of non-terrestrial origin. We have assessed their DNA-binding properties and modelled repeat structures. We found that repeats from these proteins mediate sequence specific DNA binding conforming to the TALE code, despite low sequence similarity to TALE repeats, and with novel residues around the BSR. However, MOrTL1 repeats show greater sequence discriminating power than MOrTL2 repeats. Sequence alignments show that there are only three residues conserved between repeats of all TALE-like proteins including the two new additions. This conserved motif could prove useful as an identifier for future TALE-likes. Additionally, comparing MOrTL repeats with those of other TALE-likes suggests a common evolutionary origin for the TALEs, RipTALs and Bats.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Transcription Activator-Like Effectors (TALEs) of Xanthomonas bacteria are programmable DNA binding proteins with unprecedented target specificity. Comparative studies into TALE repeat structure and function are hindered by the limited sequence variation among TALE repeats. More sequence-diverse TALE-like proteins are known from Ralstonia solanacearum (RipTALs) and Burkholderia rhizoxinica (Bats), but RipTAL and Bat repeats are conserved with those of TALEs around the DNA-binding residue. We study two novel marine-organism TALE-like proteins (MOrTL1 and MOrTL2), the first to date of non-terrestrial origin. We have assessed their DNA-binding properties and modelled repeat structures. We found that repeats from these proteins mediate sequence specific DNA binding conforming to the TALE code, despite low sequence similarity to TALE repeats, and with novel residues around the BSR. However, MOrTL1 repeats show greater sequence discriminating power than MOrTL2 repeats. Sequence alignments show that there are only three residues conserved between repeats of all TALE-like proteins including the two new additions. This conserved motif could prove useful as an identifier for future TALE-likes. Additionally, comparing MOrTL repeats with those of other TALE-likes suggests a common evolutionary origin for the TALEs, RipTALs and Bats. |
Venturelli, Sascha; Belz, Regina G; Kämper, Andreas; Berger, Alexandra; von Horn, Kyra; Wegner, André; Böcker, Alexander; Zabulon, Gérald; Langenecker, Tobias; Kohlbacher, Oliver; Barneche, Fredy; Weigel, Detlef; Lauer, Ulrich M; Bitzer, Michael; Becker, Claude Plants release precursors of potent histone deacetylase inhibitors to suppress growth of competitors Plant Cell, 27 (11), pp. 3175-89., 2015. @article{AllelopathyPlantCell, title = {Plants release precursors of potent histone deacetylase inhibitors to suppress growth of competitors}, author = {Sascha Venturelli and Regina G Belz and Andreas Kämper and Alexandra Berger and Kyra von Horn and André Wegner and Alexander Böcker and Gérald Zabulon and Tobias Langenecker and Oliver Kohlbacher and Fredy Barneche and Detlef Weigel and Ulrich M Lauer and Michael Bitzer and Claude Becker}, doi = {https://doi.org/10.1105/tpc.15.00585}, year = {2015}, date = {2015-01-01}, journal = {Plant Cell}, volume = {27}, number = {11}, pages = {3175-89.}, abstract = {To secure their access to water, light and nutrients, many plant species have developed allelopathic strategies to suppress competitors. To this end, they release phytotoxic substances that inhibit the germination and growth of neighbors. Despite the importance of allelopathy in shaping natural plant communities and for agricultural production, the underlying molecular mechanisms are largely unknown. Here we report that allelochemicals derived from the common class of hydroxamic acid root exudates directly affect the chromatin remodeling machinery in the target plant. These substances act as inhibitors of histone deacetylases both in vitro and in vivo and exert their activity through locus-specific alterations of histone acetylation and associated gene expression. Our data thus collectively show how plant-plant interactions interfere with a fundamental cellular process, histone acetylation, by targeting an evolutionarily highly conserved class of enzymes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } To secure their access to water, light and nutrients, many plant species have developed allelopathic strategies to suppress competitors. To this end, they release phytotoxic substances that inhibit the germination and growth of neighbors. Despite the importance of allelopathy in shaping natural plant communities and for agricultural production, the underlying molecular mechanisms are largely unknown. Here we report that allelochemicals derived from the common class of hydroxamic acid root exudates directly affect the chromatin remodeling machinery in the target plant. These substances act as inhibitors of histone deacetylases both in vitro and in vivo and exert their activity through locus-specific alterations of histone acetylation and associated gene expression. Our data thus collectively show how plant-plant interactions interfere with a fundamental cellular process, histone acetylation, by targeting an evolutionarily highly conserved class of enzymes. |
Zaman, Uzma; Richter, Florian M; Hofele, Romina; Kramer, Katharina; Sachsenberg, Timo; Lenz, Christof; Urlaub, Henning Dithiothreitol (DTT) acts as a specific, UV-inducible cross-linker in elucidation of protein-RNA interactions Mol. Cell. Prot., 14 (12), pp. 3196-210, 2015. @article{DTTMCP2015, title = {Dithiothreitol (DTT) acts as a specific, UV-inducible cross-linker in elucidation of protein-RNA interactions}, author = {Uzma Zaman and Florian M Richter and Romina Hofele and Katharina Kramer and Timo Sachsenberg and Christof Lenz and Henning Urlaub}, doi = {https://doi.org/10.1074/mcp.M115.052795}, year = {2015}, date = {2015-01-01}, journal = {Mol. Cell. Prot.}, volume = {14}, number = {12}, pages = {3196-210}, abstract = {Protein-RNA cross-linking by UV irradiation at 254 nm wavelength has been established as an unbiased method to identify proteins in direct contact with RNA, and has been successfully applied to investigate the spatial arrangement of protein and RNA in large macromolecular assemblies, e.g. ribonucleoprotein particles (RNPs). The mass spectrometric analysis of such peptide-RNA cross-links provides high resolution structural data to the point of mapping protein-RNA interactions to specific peptides or even amino acids. However, the approach suffers from the low yield of cross-linking products, which can be addressed by improving enrichment and analysis methods. In the present paper, we introduce dithiothreitol (DTT) as a potent protein-RNA cross-linker. In order to evaluate the efficiency and specificity of DTT, we used two systems, a small synthetic peptide from smB protein incubated with U1 snRNA oligonucleotide and native ribonucleoprotein-complexes (RNPs) from S. cerevisiae. Our results unambiguously show that DTT covalently participates in cysteine-uracil crosslinks which is observable as a mass increment of 151.9966 Da, (C4H8S2O2) upon mass spectrometric analysis. DTT presents advantages for cross-linking of cysteine containing regions of proteins. This is evidenced by comparison to experiments where (tris(2-carboxyethyl)phosphine) (TCEP) is used as reducing agent, and significantly less cross-links encompassing cysteine residues are found. We further propose insertion of DTT between the cysteine and uracil reactive sites as the most probable structure of the cross-linking products.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Protein-RNA cross-linking by UV irradiation at 254 nm wavelength has been established as an unbiased method to identify proteins in direct contact with RNA, and has been successfully applied to investigate the spatial arrangement of protein and RNA in large macromolecular assemblies, e.g. ribonucleoprotein particles (RNPs). The mass spectrometric analysis of such peptide-RNA cross-links provides high resolution structural data to the point of mapping protein-RNA interactions to specific peptides or even amino acids. However, the approach suffers from the low yield of cross-linking products, which can be addressed by improving enrichment and analysis methods. In the present paper, we introduce dithiothreitol (DTT) as a potent protein-RNA cross-linker. In order to evaluate the efficiency and specificity of DTT, we used two systems, a small synthetic peptide from smB protein incubated with U1 snRNA oligonucleotide and native ribonucleoprotein-complexes (RNPs) from S. cerevisiae. Our results unambiguously show that DTT covalently participates in cysteine-uracil crosslinks which is observable as a mass increment of 151.9966 Da, (C4H8S2O2) upon mass spectrometric analysis. DTT presents advantages for cross-linking of cysteine containing regions of proteins. This is evidenced by comparison to experiments where (tris(2-carboxyethyl)phosphine) (TCEP) is used as reducing agent, and significantly less cross-links encompassing cysteine residues are found. We further propose insertion of DTT between the cysteine and uracil reactive sites as the most probable structure of the cross-linking products. |
Backert, Linus; Kohlbacher, Oliver Immunoinformatics and epitope prediction in the age of genomic medicine Genome Med., 7 (1), pp. 119, 2015. @article{GenomeMedReview2015, title = {Immunoinformatics and epitope prediction in the age of genomic medicine}, author = {Linus Backert and Oliver Kohlbacher}, doi = {https://doi.org/10.1186/s13073-015-0245-0}, year = {2015}, date = {2015-01-01}, journal = {Genome Med.}, volume = {7}, number = {1}, pages = {119}, abstract = {Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual's human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual's human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction. |
Kowalewski, Daniel J; Schuster, Heiko; Backert, Linus; Berlin, Claudia; Kahn, Stefan; Kanz, Lothar; Salih, Helmut R; Rammensee, Hans-Georg; Stevanovic, Stefan; Stickel, Juliane Sarah HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL) Proceedings of the National Academy of Sciences of the United States of America, 112 (2), pp. E166–E175, 2015. @article{cite-key, title = {HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL)}, author = {Daniel J Kowalewski and Heiko Schuster and Linus Backert and Claudia Berlin and Stefan Kahn and Lothar Kanz and Helmut R Salih and Hans-Georg Rammensee and Stefan Stevanovic and Juliane Sarah Stickel}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299203/}, year = {2015}, date = {2015-01-01}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, number = {2}, pages = {E166--E175}, abstract = {Effective cancer immunotherapy relies on specific immune recognition of tumor-associated and tumor-specific antigens. In chronic lymphocytic leukemia (CLL), the highly variable course of disease implicates an underlying mechanism of tumor control by the immune system. In this study, we directly analyzed the landscape of naturally presented CLL antigens and identified targets conveying immune protection. These novel antigens might be valuable both for patient stratification and for inducing therapeutic antitumor immunity. Taken together, we demonstrate that CLL is subject to spontaneous T-cell responses targeting nonmutated antigens, which are associated with improved patient survival and provide novel options for the immunotherapy of CLL.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Effective cancer immunotherapy relies on specific immune recognition of tumor-associated and tumor-specific antigens. In chronic lymphocytic leukemia (CLL), the highly variable course of disease implicates an underlying mechanism of tumor control by the immune system. In this study, we directly analyzed the landscape of naturally presented CLL antigens and identified targets conveying immune protection. These novel antigens might be valuable both for patient stratification and for inducing therapeutic antitumor immunity. Taken together, we demonstrate that CLL is subject to spontaneous T-cell responses targeting nonmutated antigens, which are associated with improved patient survival and provide novel options for the immunotherapy of CLL. |
Bier, David; Thiel, Philipp; Briels, Jeroen; Ottmann, Christian Stabilization of Protein-Protein Interactions in chemical biology and drug discovery. Prog Biophys Mol Biol, 119 (1), pp. 10-9, 2015, (automatic medline import). @article{BierEtAl2015, title = {Stabilization of Protein-Protein Interactions in chemical biology and drug discovery.}, author = {David Bier and Philipp Thiel and Jeroen Briels and Christian Ottmann}, doi = {https://doi.org/10.1016/j.pbiomolbio.2015.05.002}, year = {2015}, date = {2015-01-01}, journal = {Prog Biophys Mol Biol}, volume = {119}, number = {1}, pages = {10-9}, abstract = {More than 300,000 Protein-Protein Interactions (PPIs) can be found in human cells. This number is significantly larger than the number of single proteins, which are the classical targets for pharmacological intervention. Hence, specific and potent modulation of PPIs by small, drug-like molecules would tremendously enlarge the "druggable genome" enabling novel ways of drug discovery for essentially every human disease. This strategy is especially promising in diseases with difficult targets like intrinsically disordered proteins or transcription factors, for example neurodegeneration or metabolic diseases. Whereas the potential of PPI modulation has been recognized in terms of the development of inhibitors that disrupt or prevent a binary protein complex, the opposite (or complementary) strategy to stabilize PPIs has not yet been realizedin a systematic manner. This fact is rather surprising given the number of impressive natural product examples that confer their activity by stabilizing specific PPIs. In addition, in recent years more and more examples of synthetic molecules are being published that work as PPI stabilizers, despite the fact that in the majority they initially have not been designed as such. Here, we describeexamples from both the natural products as well as the synthetic molecules advocating for a stronger consideration of the PPI stabilization approach in chemical biology and drug discovery.}, note = {automatic medline import}, keywords = {}, pubstate = {published}, tppubtype = {article} } More than 300,000 Protein-Protein Interactions (PPIs) can be found in human cells. This number is significantly larger than the number of single proteins, which are the classical targets for pharmacological intervention. Hence, specific and potent modulation of PPIs by small, drug-like molecules would tremendously enlarge the "druggable genome" enabling novel ways of drug discovery for essentially every human disease. This strategy is especially promising in diseases with difficult targets like intrinsically disordered proteins or transcription factors, for example neurodegeneration or metabolic diseases. Whereas the potential of PPI modulation has been recognized in terms of the development of inhibitors that disrupt or prevent a binary protein complex, the opposite (or complementary) strategy to stabilize PPIs has not yet been realizedin a systematic manner. This fact is rather surprising given the number of impressive natural product examples that confer their activity by stabilizing specific PPIs. In addition, in recent years more and more examples of synthetic molecules are being published that work as PPI stabilizers, despite the fact that in the majority they initially have not been designed as such. Here, we describeexamples from both the natural products as well as the synthetic molecules advocating for a stronger consideration of the PPI stabilization approach in chemical biology and drug discovery. |
Nicoludis, John M; Lau, Sze-Yi; Schärfe, Charlotta P.I; Marks, Debora S; Weihofen, Wilhelm A; Gaudet, Rachelle Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity Structure, 23 (11), pp. 2087 - 2098, 2015. @article{Nicoludis20152087, title = {Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity}, author = {John M Nicoludis and Sze-Yi Lau and Charlotta P.I Schärfe and Debora S Marks and Wilhelm A Weihofen and Rachelle Gaudet}, url = {http://www.sciencedirect.com/science/article/pii/S0969212615003755}, year = {2015}, date = {2015-01-01}, journal = {Structure}, volume = {23}, number = {11}, pages = {2087 - 2098}, abstract = {Summary Clustered protocadherin (Pcdh) proteins mediate dendritic self-avoidance in neurons via specific homophilic interactions in their extracellular cadherin (EC) domains. We determined crystal structures of EC1–EC3, containing the homophilic specificity-determining region, of two mouse clustered Pcdh isoforms (PcdhγA1 and PcdhγC3) to investigate the nature of the homophilic interaction. Within the crystal lattices, we observe antiparallel interfaces consistent with a role in trans cell-cell contact. Antiparallel dimerization is supported by evolutionary correlations. Two interfaces, located primarily on EC2-EC3, involve distinctive clustered Pcdh structure and sequence motifs, lack predicted glycosylation sites, and contain residues highly conserved in orthologs but not paralogs, pointing toward their biological significance as homophilic interaction interfaces. These two interfaces are similar yet distinct, reflecting a possible difference in interaction architecture between clustered Pcdh subfamilies. These structures initiate a molecular understanding of clustered Pcdh assemblies that are required to produce functional neuronal networks.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Summary Clustered protocadherin (Pcdh) proteins mediate dendritic self-avoidance in neurons via specific homophilic interactions in their extracellular cadherin (EC) domains. We determined crystal structures of EC1–EC3, containing the homophilic specificity-determining region, of two mouse clustered Pcdh isoforms (PcdhγA1 and PcdhγC3) to investigate the nature of the homophilic interaction. Within the crystal lattices, we observe antiparallel interfaces consistent with a role in trans cell-cell contact. Antiparallel dimerization is supported by evolutionary correlations. Two interfaces, located primarily on EC2-EC3, involve distinctive clustered Pcdh structure and sequence motifs, lack predicted glycosylation sites, and contain residues highly conserved in orthologs but not paralogs, pointing toward their biological significance as homophilic interaction interfaces. These two interfaces are similar yet distinct, reflecting a possible difference in interaction architecture between clustered Pcdh subfamilies. These structures initiate a molecular understanding of clustered Pcdh assemblies that are required to produce functional neuronal networks. |
Kenar, Erhan; Franken, Holger; Forcisi, Sara; Wörmann, Kilian; Häring, Hans-Ulrich; Lehmann, Rainer; Schmitt-Kopplin, Philippe; Zell, Andreas; Kohlbacher, Oliver Automated Label-Free Quantification of Metabolites from LC-MS Data Mol. Cell. Prot., 13 (1), pp. 348-59, 2014. @article{FeaFiMetaboMCP13, title = {Automated Label-Free Quantification of Metabolites from LC-MS Data}, author = {Erhan Kenar and Holger Franken and Sara Forcisi and Kilian Wörmann and Hans-Ulrich Häring and Rainer Lehmann and Philippe Schmitt-Kopplin and Andreas Zell and Oliver Kohlbacher}, doi = {https://doi.org/10.1074/mcp.M113.031278}, year = {2014}, date = {2014-01-01}, journal = {Mol. Cell. Prot.}, volume = {13}, number = {1}, pages = {348-59}, abstract = {Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and is thus easily amenable to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e., all signals caused by the same analyte species) that is computationally efficient, sensitive, and leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine (SVM)-based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g., lipidomics, peptidomics) as well as to other separation technologies (e.g., CE-MS). We assess the algorithm’s robustness to varying noise levels on synthetic data and then validate the approach on experimental data investigating human plasma samples. We obtain excellent results in a fully automated data processing pipeline both with respect to accuracy and reproducibility. Compared to state-of-the art algorithms, we can demonstrate increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS (www.OpenMS.de) and runs on all major operating systems.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and is thus easily amenable to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e., all signals caused by the same analyte species) that is computationally efficient, sensitive, and leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine (SVM)-based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g., lipidomics, peptidomics) as well as to other separation technologies (e.g., CE-MS). We assess the algorithm’s robustness to varying noise levels on synthetic data and then validate the approach on experimental data investigating human plasma samples. We obtain excellent results in a fully automated data processing pipeline both with respect to accuracy and reproducibility. Compared to state-of-the art algorithms, we can demonstrate increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS (www.OpenMS.de) and runs on all major operating systems. |
Beck, Florian; Geiger, Jörg; Gambaryan, Stepan; Veit, Johannes; Vaudel, Marc; Nollau, Peter; Kohlbacher, Oliver; Martens, Lennart; Walter, Ulrich; Sickmann, Albert; Zahedi, René P Time-resolved characterization of cAMP/PKA-dependant signaling reveals that platelet inhibition is a concerted process involving multiple signaling pathways Blood, 123 (4), pp. e1-e10, 2014. @article{PlateletsBlood2013, title = {Time-resolved characterization of cAMP/PKA-dependant signaling reveals that platelet inhibition is a concerted process involving multiple signaling pathways}, author = {Florian Beck and Jörg Geiger and Stepan Gambaryan and Johannes Veit and Marc Vaudel and Peter Nollau and Oliver Kohlbacher and Lennart Martens and Ulrich Walter and Albert Sickmann and René P Zahedi}, doi = {https://doi.org/10.1182/blood-2013-07-512384}, year = {2014}, date = {2014-01-01}, journal = {Blood}, volume = {123}, number = {4}, pages = {e1-e10}, abstract = {One of the most important physiological platelet inhibitors is endothelium-derived prostacyclin which stimulates the platelet cAMP/PKA signaling cascade and inhibits virtually all platelet activating key mechanisms. Using quantitative mass spectrometry, we analyzed time resolved phosphorylation patterns in human platelets after treatment with Iloprost, a stable prostacyclin analogue, for 0, 10, 30 and 60 seconds to characterize key mediators of platelet inhibition and activation in three independent biological replicates. We quantified over 2,700 different phosphorylated peptides of which 360 were significantly regulated upon stimulation. This comprehensive and time-resolved analysis indicates that platelet inhibition is a multi-pronged process involving different kinases and phosphatases as well as many previously unanticipated proteins and pathways.}, keywords = {}, pubstate = {published}, tppubtype = {article} } One of the most important physiological platelet inhibitors is endothelium-derived prostacyclin which stimulates the platelet cAMP/PKA signaling cascade and inhibits virtually all platelet activating key mechanisms. Using quantitative mass spectrometry, we analyzed time resolved phosphorylation patterns in human platelets after treatment with Iloprost, a stable prostacyclin analogue, for 0, 10, 30 and 60 seconds to characterize key mediators of platelet inhibition and activation in three independent biological replicates. We quantified over 2,700 different phosphorylated peptides of which 360 were significantly regulated upon stimulation. This comprehensive and time-resolved analysis indicates that platelet inhibition is a multi-pronged process involving different kinases and phosphatases as well as many previously unanticipated proteins and pathways. |
Gerasch, Andreas; Kaufmann, Michael; Kohlbacher, Oliver Rebuilding KEGG Maps - An integrative approach for visual analytics of metabolic networks Pacific Visualization Symposium (PacificVis), 2014 IEEE, pp. 97-104, IEEE 2014. @inproceedings{RebuildingKEGGMaps, title = {Rebuilding KEGG Maps - An integrative approach for visual analytics of metabolic networks}, author = {Andreas Gerasch and Michael Kaufmann and Oliver Kohlbacher}, url = {http://dx.doi.org/10.1109/PacificVis.2014.45}, year = {2014}, date = {2014-01-01}, booktitle = {Pacific Visualization Symposium (PacificVis), 2014 IEEE}, pages = {97-104}, organization = {IEEE}, abstract = {Static drawings of biological pathways are still an important research tool for biologists. Gerhard Michal created his seminal drawings of metabolic networks in the 1960s and thus defined canonical representations of some key pathways. The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides the most popular static drawings of biological networks of different types, used in a huge number of publications. These drawings are so widely known that they are immediately recognizable to most biologists. This enables collaborative work and simplifies the communication of analysis results. Automatic layout of these pathway maps is complicated by the fact that the information available from KEGG does not contain the entire layout information of the reference maps. Here we present a fully automated algorithm for interactive KEGG layout construction. The algorithm conserves the original KEGG layout to the extent possible while improving readability by removing unnecessary elements (in organism-specific maps). Multiple pathway maps can be laid out simultaneously to facilitate the navigation of larger networks. The algorithm supports the hierarchical layout of sub networks and thus supports interactive exploration of large datasets.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Static drawings of biological pathways are still an important research tool for biologists. Gerhard Michal created his seminal drawings of metabolic networks in the 1960s and thus defined canonical representations of some key pathways. The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides the most popular static drawings of biological networks of different types, used in a huge number of publications. These drawings are so widely known that they are immediately recognizable to most biologists. This enables collaborative work and simplifies the communication of analysis results. Automatic layout of these pathway maps is complicated by the fact that the information available from KEGG does not contain the entire layout information of the reference maps. Here we present a fully automated algorithm for interactive KEGG layout construction. The algorithm conserves the original KEGG layout to the extent possible while improving readability by removing unnecessary elements (in organism-specific maps). Multiple pathway maps can be laid out simultaneously to facilitate the navigation of larger networks. The algorithm supports the hierarchical layout of sub networks and thus supports interactive exploration of large datasets. |