Journal Article

FZJ-2021-00849

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Robust Principal Component Analysis‐based Prediction of Protein‐Protein Interaction Hot spots ( RBHS )

Sitani, D. (Corresponding author)FZJ* ; Giorgetti, A.FZJ* ; Alfonso‐Prieto, M.FZJ* ; Carloni, P. (Corresponding author)FZJ*

2021
Wiley-Liss New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/28740  doi:10.1002/prot.26047

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.

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Contributing Institute(s):

1. Computational Biomedicine (IAS-5)
2. Computational Biomedicine (INM-9)

Research Program(s):

1. 525 - Decoding Brain Organization and Dysfunction (POF4-525) (POF4-525)
2. 5241 - Molecular Information Processing in Cellular Systems (POF4-524) (POF4-524)

Appears in the scientific report 2021

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DEAL Wiley ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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