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.

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

Alejandro Giorgetti;
2021-01-01

Abstract

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.
F1-score
feature selection
hot spot residues
imbalanced datasets
machine learning
noiseless data matrices
protein-protein interactions
robust PCA (principal component analysis)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1039242
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