@inproceedings{NKK2002,
title = {Modelling the sugar-lectin interaction by computer simulated docking},
author = {Dirk Neumann and Andreas Kerzmann and Oliver Kohlbacher},
year = {2002},
date = {2002-01-01},
booktitle = {Abstracts of the 20th Interlec Meeting},
pages = {116},
publisher = {University of Copenhagen, Copenhagen, Denmark},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{SSEKKBL2002,
title = {BioMiner - modeling, analysing, and visualizing biochemical pathways and networks},
author = {Marite Sirava and Thomas Schäfer and Markus Eiglsperger and Michael Kaufmann and Oliver Kohlbacher and Erich Bornberg-Bauer and Hans-Peter Lenhof},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/18/suppl_2/S219},
year = {2002},
date = {2002-01-01},
journal = {Bioinformatics},
volume = {19},
number = {10},
pages = {S219-230},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{SVMHC,
title = {Prediction of MHC class I binding peptides, using SVMHC},
author = {Pierre Dönnes and Arne Elofsson},
url = {http://www.biomedcentral.com/1471-2105/3/25},
year = {2002},
date = {2002-01-01},
journal = {BMC Bioinformatics},
volume = {3},
number = {25},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{articleKBM2001,
title = {Structure prediction of protein complexes by a NMR-based protein docking algorithm},
author = {Oliver Kohlbacher and Andreas Burchardt and Andreas Moll and Andreas Hildebrandt and Peter Bayer and Hans-Peter Lenhof},
url = {https://link.springer.com/article/10.1023/A:1011216130486},
year = {2001},
date = {2001-01-01},
journal = {J. Biomol. NMR},
volume = {20},
pages = {15-21},
abstract = {Protein docking algorithms can be used to study the driving forces and reaction mechanisms of docking processes. They are also able to speed up the lengthy process of experimental structure elucidation of protein complexes by proposing potential structures. In this paper, we are discussing a variant of the protein-protein docking problem, where the input consists of the tertiary structures of proteins A and B plus an unassigned one-dimensional 1H-NMR spectrum of the complex AB. We present a new scoring function for evaluating and ranking potential complex structures produced by a docking algorithm. The scoring function computes a `theoretical' 1H-NMR spectrum for each tentative complex structure and subtracts the calculated spectrum from the experimental one. The absolute areas of the difference spectra are then used to rank the potential complex structures. In contrast to formerly published approaches (e.g. [Morelli et al. (2000) Biochemistry, 39, 2530–2537]) we do not use distance constraints (intermolecular NOE constraints). We have tested the approach with four protein complexes whose three-dimensional structures are stored in the PDB data bank [Bernstein et al. (1977)] and whose 1H-NMR shift assignments are available from the BMRB database. The best result was obtained for an example, where all standard scoring functions failed completely. Here, our new scoring function achieved an almost perfect separation between good approximations of the true complex structure and false positives.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Protein docking algorithms can be used to study the driving forces and reaction mechanisms of docking processes. They are also able to speed up the lengthy process of experimental structure elucidation of protein complexes by proposing potential structures. In this paper, we are discussing a variant of the protein-protein docking problem, where the input consists of the tertiary structures of proteins A and B plus an unassigned one-dimensional 1H-NMR spectrum of the complex AB. We present a new scoring function for evaluating and ranking potential complex structures produced by a docking algorithm. The scoring function computes a `theoretical' 1H-NMR spectrum for each tentative complex structure and subtracts the calculated spectrum from the experimental one. The absolute areas of the difference spectra are then used to rank the potential complex structures. In contrast to formerly published approaches (e.g. [Morelli et al. (2000) Biochemistry, 39, 2530–2537]) we do not use distance constraints (intermolecular NOE constraints). We have tested the approach with four protein complexes whose three-dimensional structures are stored in the PDB data bank [Bernstein et al. (1977)] and whose 1H-NMR shift assignments are available from the BMRB database. The best result was obtained for an example, where all standard scoring functions failed completely. Here, our new scoring function achieved an almost perfect separation between good approximations of the true complex structure and false positives.
@inproceedings{procTur2001,
title = {Visualization Challenges for a New Cyberpharmaceutical Computing Paradigm},
author = {Russell J Turner and Kabir Chaturvedi and Nathan J Edwards and Daniel Fasulo and Aaron L Halpern and Daniel H Huson and Karin A Remington and Russell Schwartz and Brian Walenz and Shibu Yooseph and Sorin Istrail},
year = {2001},
date = {2001-01-01},
booktitle = {Proceedings of the IEEE Symposium on Parallel and Large-Data Visualization and Graphics (PVG2001)},
pages = {7--18},
address = {San Diego, USA,},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
A NMR-spectra-based scoring function for protein docking
Sankoff, David; Lengauer, Thomas (Ed.): RECOMB 2001, Proceedings of the Fifth Annual International Conference on Computational Molecular Biology, pp. 169-177, ACM press, 2001.
@inproceedings{KBHMBL2000,
title = {A NMR-spectra-based scoring function for protein docking},
author = {Oliver Kohlbacher and Andreas Burchardt and Andreas Hildebrandt and Andreas Moll and Peter Bayer and Hans-Peter Lenhof},
editor = {David Sankoff and Thomas Lengauer},
url = {http://portal.acm.org/citation.cfm?doid=369133.369194},
year = {2001},
date = {2001-01-01},
booktitle = {RECOMB 2001, Proceedings of the Fifth Annual International Conference on Computational Molecular Biology},
pages = {169-177},
publisher = {ACM press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{procNKH2000,
title = {Modeling the Sugar Lectin Interaction by Computational Chemistry Relevant to Drug Design},
author = {Dirk Neumann and Oliver Kohlbacher and Eleonore Haltner and Hans-Peter Lenhof and Claus-Michael Lehr},
year = {2000},
date = {2000-01-01},
booktitle = {Proc. 3rd World Meeting on Pharmaceutics, Biopharmaceutics and Pharmaceutical Technology},
pages = {233},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{articleKL2000,
title = {BALL - Rapid Software Prototyping in Computational Molecular Biology},
author = {Oliver Kohlbacher and Hans-Peter Lenhof},
url = {https://www.ncbi.nlm.nih.gov/pubmed/11108704},
year = {2000},
date = {2000-01-01},
journal = {Bioinformatics},
volume = {16},
number = {9,},
pages = {815--824},
abstract = {MOTIVATION:
Rapid software prototyping can significantly reduce development times in the field of computational molecular biology and molecular modeling. Biochemical Algorithms Library (BALL) is an application framework in C++ that has been specifically designed for this purpose.
RESULTS:
BALL provides an extensive set of data structures as well as classes for molecular mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility which results from an object-oriented and generic programming approach distinguishes it from other software packages. BALL is well suited to serve as a public repository for reliable data structures and algorithms. We show in an example that the implementation of complex methods is greatly simplified when using the data structures and functionality provided by BALL.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
MOTIVATION:
Rapid software prototyping can significantly reduce development times in the field of computational molecular biology and molecular modeling. Biochemical Algorithms Library (BALL) is an application framework in C++ that has been specifically designed for this purpose.
RESULTS:
BALL provides an extensive set of data structures as well as classes for molecular mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility which results from an object-oriented and generic programming approach distinguishes it from other software packages. BALL is well suited to serve as a public repository for reliable data structures and algorithms. We show in an example that the implementation of complex methods is greatly simplified when using the data structures and functionality provided by BALL.
@article{articleBKL2000,
title = {Rapid software prototyping in molecular modeling using the biochemical algorithms library (BALL)},
author = {N P Boghossian and O Kohlbacher and H P Lenhof},
year = {2000},
date = {2000-01-01},
journal = {J. Exp. Algorithmics},
volume = {5},
pages = {16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Althaus, Ernst; Kohlbacher, Oliver; Lenhof, Hans-Peter; Müller, Peter
A Combinatorial Approach to Protein Docking with Flexible Side-Chains
Shamir, Ron; Miyano, Satoru; Istrail, Sorin; Pevzner, Pavel; Waterman, Michael (Ed.): RECOMB 2000 -- Proceedings of the Fourth Annual International Conference on Computational Molecular Biology, pp. 15–24, ACM press, 2000.
@inproceedings{procAKL2000,
title = {A Combinatorial Approach to Protein Docking with Flexible Side-Chains},
author = {Ernst Althaus and Oliver Kohlbacher and Hans-Peter Lenhof and Peter Müller},
editor = {Ron Shamir and Satoru Miyano and Sorin Istrail and Pavel Pevzner and Michael Waterman},
year = {2000},
date = {2000-01-01},
booktitle = {RECOMB 2000 -- Proceedings of the Fourth Annual International Conference on Computational Molecular Biology},
pages = {15--24},
publisher = {ACM press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@techreport{AKLM2000,
title = {A branch and cut algorithm for the optimal solution of the side-chain placement problem},
author = {Ernst Althaus and Oliver Kohlbacher and Hans-Peter Lenhof and Peter Müller},
url = {http://domino.mpi-sb.mpg.de/internet/reports.nsf/NumberView/2000-1-001},
year = {2000},
date = {2000-01-01},
number = {MPI-I-2000-1-001},
address = {Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany},
institution = {Max-Planck-Institute für Informatik},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
@inproceedings{procKL1999,
title = {Rapid Software Prototyping in Computational Molecular Biology},
author = {O Kohlbacher and H -P Lenhof},
year = {1999},
date = {1999-01-01},
booktitle = {Proceedings of the German Conference on Bioinformatics (GCB'99)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{procNHL1998,
title = {Investigating the Sugar-Lectin Interaction by Computational Chemistry: Tunneling the Epithelial Barrier},
author = {Dirk Neumann and Elleonore Haltner and Claus-Michael Lehr and Oliver Kohlbacher and Hans-Peter Lenhof},
year = {1998},
date = {1998-01-01},
booktitle = {Abstracts of the 18th Interlec Meeting},
pages = {549},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Immel_Comms_Bio_2021,
title = {Genome-wide study of a Neolithic Wartberg grave community reveals distinct HLA variation and hunter-gatherer ancestry},
author = {Alexander Immel and Federicia Perini and Christoph Rinne and John Meadows and Rodrigo Barquera and Andras Szolek and Julian SUsat and Lisa Böhme and Janina Dose and Joanna Bonczarowska and Clara Drummer and Katharina Fuchs and David Ellinghaus and Jan-Christian Kässens and Martin Furholt and Oliver Kohlbacher and Sabine Schade-Lindig and Andre Franke and Stefan Schreiber and Johannes Krause and Johannes Müller and Tobias L. Lenz and Almut Nebel and Ben Krause-Kyora},
journal = {Communications Biology},
abstract = {The Wartberg culture (WBC, 3500-2800 BCE) dates to the Late Neolithic period, a time of
important demographic and cultural transformations in western Europe. We performed
genome-wide analyses of 42 individuals who were interred in a WBC collective burial in
Niedertiefenbach, Germany (3300-3200 cal. BCE). The results showed that the farming
population of Niedertiefenbach carried a surprisingly large hunter-gatherer ancestry component
(34–58%). This component was most likely introduced during the cultural transformation
that led to the WBC. In addition, the Niedertiefenbach individuals exhibited a distinct
human leukocyte antigen gene pool, possibly reflecting an immune response that was geared
towards detecting viral infections.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
The Wartberg culture (WBC, 3500-2800 BCE) dates to the Late Neolithic period, a time of
important demographic and cultural transformations in western Europe. We performed
genome-wide analyses of 42 individuals who were interred in a WBC collective burial in
Niedertiefenbach, Germany (3300-3200 cal. BCE). The results showed that the farming
population of Niedertiefenbach carried a surprisingly large hunter-gatherer ancestry component
(34–58%). This component was most likely introduced during the cultural transformation
that led to the WBC. In addition, the Niedertiefenbach individuals exhibited a distinct
human leukocyte antigen gene pool, possibly reflecting an immune response that was geared
towards detecting viral infections.
@article{EVComplex_NatComm_2020,
title = {Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences},
author = {Anna Green and Hadeer Elhabashy and Kelly Brock and Rohan Maddamsetti and Oliver Kohlbacher and Debora S. Marks},
journal = {Nat. Commun.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
@article{ProtPrivacyMCP2021,
title = {Data management of sensitive human proteomics data: current practices, recommendations and perspectives for the future},
author = {Nuno Bandeira and Eric W Deutsch and Oliver Kohlbacher and Lennart Martens and Juan Antonio Vizcaíno},
doi = {10.1016/j.mcpro.2021.100071},
journal = {Mol. Cell. Prot.},
abstract = {Today it is the norm that all relevant proteomics data that support the conclusions in scientific publications are made available in public proteomics data repositories. However, given the increase in the number of clinical proteomics studies, an important emerging topic is the management and dissemination of clinical, and thus potentially sensitive, human proteomics data. Both in the United States and in the European Union there are legal frameworks protecting the privacy of individuals. Implementing privacy standards for publicly released research data in genomics and transcriptomics has led to processes to control who may access the data, so called "controlled access" data. In parallel with the technological developments in the field it is clear that the privacy risks of sharing proteomics data need to be properly assessed and managed. As the proteome is directly derived from genome data, proteomics data can potentially reveal similarly sensitive data as nucleotide sequencing data. In this manuscript, we summarize the conclusions about this topic that have emerged from two meetings held in 2019 and some follow-up discussions, with a primary focus on data management practices. In our view, the proteomics community must be proactive in addressing these issues. Yet a careful balance must be kept. On the one hand, neglecting to address the potential of identifiability in human proteomics data could lead to reputational damage of the field, while on the other hand, erecting barriers to open access to clinical proteomics data will inevitably reduce re-use of proteomics data and could substantially delay critical discoveries in biomedical research. In order to balance these apparently conflicting requirements for data privacy and efficient use and re-use of research efforts through the sharing of clinical proteomics data, development efforts will be needed at different levels including bioinformatics infrastructure, policy making and mechanisms of oversight.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Today it is the norm that all relevant proteomics data that support the conclusions in scientific publications are made available in public proteomics data repositories. However, given the increase in the number of clinical proteomics studies, an important emerging topic is the management and dissemination of clinical, and thus potentially sensitive, human proteomics data. Both in the United States and in the European Union there are legal frameworks protecting the privacy of individuals. Implementing privacy standards for publicly released research data in genomics and transcriptomics has led to processes to control who may access the data, so called "controlled access" data. In parallel with the technological developments in the field it is clear that the privacy risks of sharing proteomics data need to be properly assessed and managed. As the proteome is directly derived from genome data, proteomics data can potentially reveal similarly sensitive data as nucleotide sequencing data. In this manuscript, we summarize the conclusions about this topic that have emerged from two meetings held in 2019 and some follow-up discussions, with a primary focus on data management practices. In our view, the proteomics community must be proactive in addressing these issues. Yet a careful balance must be kept. On the one hand, neglecting to address the potential of identifiability in human proteomics data could lead to reputational damage of the field, while on the other hand, erecting barriers to open access to clinical proteomics data will inevitably reduce re-use of proteomics data and could substantially delay critical discoveries in biomedical research. In order to balance these apparently conflicting requirements for data privacy and efficient use and re-use of research efforts through the sharing of clinical proteomics data, development efforts will be needed at different levels including bioinformatics infrastructure, policy making and mechanisms of oversight.
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