| Literature DB >> 25392414 |
Matthew J Betts1, Qianhao Lu1, YingYing Jiang1, Armin Drusko1, Oliver Wichmann1, Mathias Utz1, Ilse A Valtierra-Gutiérrez1, Matthias Schlesner2, Natalie Jaeger2, David T Jones2, Stefan Pfister2, Peter Lichter2, Roland Eils3, Reiner Siebert4, Peer Bork5, Gordana Apic6, Anne-Claude Gavin5, Robert B Russell7.
Abstract
Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein-protein, protein-nucleic acid and a subset of protein-chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.Entities:
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Year: 2014 PMID: 25392414 PMCID: PMC4333368 DOI: 10.1093/nar/gku1094
Source DB: PubMed Journal: Nucleic Acids Res ISSN: 0305-1048 Impact factor: 16.971