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

Divya SitaniAlejandro GiorgettiMercedes Alfonso-PrietoPaolo Carloni
Published in: Proteins (2021)
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.
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