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Open AccessArticle

A Computational Framework for Predicting Direct Contacts and Substructures within Protein Complexes

by Suyu Mei 1,* and Kun Zhang 2,*
1
Software College, Shenyang Normal University, Shenyang 110034, China
2
Bioinformatics Core of Xavier RCMI Center for Cancer Research, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
*
Authors to whom correspondence should be addressed.
Biomolecules 2019, 9(11), 656; https://doi.org/10.3390/biom9110656
Received: 8 September 2019 / Revised: 20 October 2019 / Accepted: 23 October 2019 / Published: 25 October 2019
(This article belongs to the Section Molecular Structure and Dynamics)
Understanding the physical arrangement of subunits within protein complexes potentially provides valuable clues about how the subunits work together and how the complexes function. The majority of recent research focuses on identifying protein complexes as a whole and seldom studies the inner structures within complexes. In this study, we propose a computational framework to predict direct contacts and substructures within protein complexes. In this framework, we first train a supervised learning model of l2-regularized logistic regression to learn the patterns of direct and indirect interactions within complexes, from where physical subunit interaction networks are predicted. Then, to infer substructures within complexes, we apply a graph clustering method (i.e., maximum modularity clustering (MMC)) and a gene ontology (GO) semantic similarity based functional clustering on partially- and fully-connected networks, respectively. Computational results show that the proposed framework achieves fairly good performance of cross validation and independent test in terms of detecting direct contacts between subunits. Functional analyses further demonstrate the rationality of partitioning the subunits into substructures via the MMC algorithm and functional clustering. View Full-Text
Keywords: protein complexes; complex substructure; machine learning; l2-regularized logistic regression; graph clustering; functional clustering protein complexes; complex substructure; machine learning; l2-regularized logistic regression; graph clustering; functional clustering
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Mei, S.; Zhang, K. A Computational Framework for Predicting Direct Contacts and Substructures within Protein Complexes. Biomolecules 2019, 9, 656.

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