%0 Journal Article
%A Sitani, Divya
%A Giorgetti, Alejandro
%A Alfonso‐Prieto, Mercedes
%A Carloni, Paolo
%T Robust Principal Component Analysis‐based Prediction of Protein‐Protein Interaction Hot spots ( RBHS )
%J Proteins
%V 89
%N 6
%@ 1097-0134
%C New York, NY
%I Wiley-Liss
%M FZJ-2021-00849
%P 639-647
%D 2021
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 33458895
%U <Go to ISI:>//WOS:000613869900001
%R 10.1002/prot.26047
%U https://juser.fz-juelich.de/record/890265