Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient
Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein co...
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doaj-f5797053a4854c6599d2f5073df12f9a2021-09-27T04:24:50ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011952555263Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficientSara Omranian0Angela Angeleska1Zoran Nikoloski2Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, GermanyMathematics Department, University of Tampa, Tampa, FL, USABioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany; Corresponding author at: Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of ∼35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes.http://www.sciencedirect.com/science/article/pii/S2001037021003998Protein complexesProtein-protein interactionNetwork clusteringSpecies comparison |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sara Omranian Angela Angeleska Zoran Nikoloski |
spellingShingle |
Sara Omranian Angela Angeleska Zoran Nikoloski Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient Computational and Structural Biotechnology Journal Protein complexes Protein-protein interaction Network clustering Species comparison |
author_facet |
Sara Omranian Angela Angeleska Zoran Nikoloski |
author_sort |
Sara Omranian |
title |
Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_short |
Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_full |
Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_fullStr |
Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_full_unstemmed |
Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_sort |
efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
publisher |
Elsevier |
series |
Computational and Structural Biotechnology Journal |
issn |
2001-0370 |
publishDate |
2021-01-01 |
description |
Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of ∼35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes. |
topic |
Protein complexes Protein-protein interaction Network clustering Species comparison |
url |
http://www.sciencedirect.com/science/article/pii/S2001037021003998 |
work_keys_str_mv |
AT saraomranian efficientandaccurateidentificationofproteincomplexesfromproteinproteininteractionnetworksbasedontheclusteringcoefficient AT angelaangeleska efficientandaccurateidentificationofproteincomplexesfromproteinproteininteractionnetworksbasedontheclusteringcoefficient AT zorannikoloski efficientandaccurateidentificationofproteincomplexesfromproteinproteininteractionnetworksbasedontheclusteringcoefficient |
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1716867288200642560 |