mCSM-PPI2: predicting the effects of mutations on protein-protein interactions

Nucleic Acids Res. 2019 Jul 2;47(W1):W338-W344. doi: 10.1093/nar/gkz383.

Abstract

Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Benchmarking
  • Binding Sites
  • Crystallography, X-Ray
  • Datasets as Topic
  • Humans
  • Internet
  • Machine Learning*
  • Mutation, Missense*
  • Protein Binding
  • Protein Conformation, alpha-Helical
  • Protein Conformation, beta-Strand
  • Protein Interaction Domains and Motifs
  • Protein Interaction Mapping
  • Proteins / chemistry*
  • Proteins / genetics
  • Proteins / metabolism
  • Software*
  • Thermodynamics

Substances

  • Proteins