Cargando…

Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks

The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In rec...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Guangming, Liu, Bo, Li, Aimin, Wang, Xiaofan, Yu, Jian, Zhou, Xuezhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430217/
https://www.ncbi.nlm.nih.gov/pubmed/34512712
http://dx.doi.org/10.3389/fgene.2021.664786
_version_ 1783750657926234112
author Liu, Guangming
Liu, Bo
Li, Aimin
Wang, Xiaofan
Yu, Jian
Zhou, Xuezhong
author_facet Liu, Guangming
Liu, Bo
Li, Aimin
Wang, Xiaofan
Yu, Jian
Zhou, Xuezhong
author_sort Liu, Guangming
collection PubMed
description The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks.
format Online
Article
Text
id pubmed-8430217
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-84302172021-09-11 Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks Liu, Guangming Liu, Bo Li, Aimin Wang, Xiaofan Yu, Jian Zhou, Xuezhong Front Genet Genetics The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks. Frontiers Media S.A. 2021-08-27 /pmc/articles/PMC8430217/ /pubmed/34512712 http://dx.doi.org/10.3389/fgene.2021.664786 Text en Copyright © 2021 Liu, Liu, Li, Wang, Yu and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Liu, Guangming
Liu, Bo
Li, Aimin
Wang, Xiaofan
Yu, Jian
Zhou, Xuezhong
Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
title Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
title_full Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
title_fullStr Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
title_full_unstemmed Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
title_short Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
title_sort identifying protein complexes with clear module structure using pairwise constraints in protein interaction networks
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430217/
https://www.ncbi.nlm.nih.gov/pubmed/34512712
http://dx.doi.org/10.3389/fgene.2021.664786
work_keys_str_mv AT liuguangming identifyingproteincomplexeswithclearmodulestructureusingpairwiseconstraintsinproteininteractionnetworks
AT liubo identifyingproteincomplexeswithclearmodulestructureusingpairwiseconstraintsinproteininteractionnetworks
AT liaimin identifyingproteincomplexeswithclearmodulestructureusingpairwiseconstraintsinproteininteractionnetworks
AT wangxiaofan identifyingproteincomplexeswithclearmodulestructureusingpairwiseconstraintsinproteininteractionnetworks
AT yujian identifyingproteincomplexeswithclearmodulestructureusingpairwiseconstraintsinproteininteractionnetworks
AT zhouxuezhong identifyingproteincomplexeswithclearmodulestructureusingpairwiseconstraintsinproteininteractionnetworks