Nucleic Acids Research · 2019 · 385 citations · 52 references
Life SciencesBioinformaticsMolecular BiologyComputational BiologyProtein-protein InteractionsStructural BioinformaticsBinding Affinity PredictionMolecular Interaction NetworksProtein-protein Interaction AnalysisProtein-protein Interaction ModelingProtein-protein Interaction NetworksComplex Network AnalysisComplex Network TheoryMissense MutationsMutation Effect PredictionProtein Interaction PredictionProtein Mutation Effect PredictionProtein-protein Interaction EvolutionProtein-protein Interaction Prediction
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/.
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