Robust principal component analysis-based prediction of protein-protein interaction hot spots.

AbstractProteins 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

Robust Principal Component Analysis-based Prediction of Protein-Protein Interaction Hot spots ( {RBHS} )

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