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Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions.

Authors
  • Betts, Matthew J
  • Lu, Qianhao
  • Jiang, YingYing
  • Drusko, Armin
  • Wichmann, Oliver
  • Utz, Mathias
  • Valtierra-Gutiérrez, Ilse A
  • Schlesner, Matthias
  • Jaeger, Natalie
  • Jones, David T
  • Pfister, Stefan
  • Lichter, Peter
  • Eils, Roland
  • Siebert, Reiner
  • Bork, Peer
  • Apic, Gordana
  • Gavin, Anne-Claude
  • Russell, Robert B
Type
Published Article
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Publication Date
Jan 01, 2015
Volume
43
Issue
2
Identifiers
DOI: 10.1093/nar/gku1094
PMID: 25392414
Source
Medline
License
Unknown

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.

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