mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.
Journal:
Nucleic acids research
Published Date:
Jul 2, 2019
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/.
Authors
Keywords
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
Software
Thermodynamics