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Robust principal component analysis‐based prediction of protein‐protein interaction hot spots
Author(s) -
Sitani Divya,
Giorgetti Alejandro,
AlfonsoPrieto Mercedes,
Carloni Paolo
Publication year - 2021
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.26047
Subject(s) - principal component analysis , hot spot (computer programming) , pipeline (software) , computer science , protein–protein interaction , computational biology , robust principal component analysis , artificial intelligence , pattern recognition (psychology) , chemistry , biology , biochemistry , programming language , operating system
Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein‐protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein‐protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre‐processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method.