Functional centrality: detecting lethality of proteins in protein interaction networks
- PMID: 18546514
Functional centrality: detecting lethality of proteins in protein interaction networks
Abstract
Identifying lethal proteins is important for understanding the intricate mechanism governing life. Researchers have shown that the lethality of a protein can be computed based on its topological position in the protein-protein interaction (PPI) network. Performance of current approaches has been less than satisfactory as the lethality of a protein is a functional characteristic that cannot be determined solely by network topology. Furthermore, a significant number of lethal proteins have low connectivity in the interaction networks but are overlooked by most current methods. Our work reveals that a protein's lethality correlates more strongly with its "functional centrality" than pure topological centrality. We define functional centrality as the topological centrality within a subnetwork of proteins with similar functions. Evaluation experiments on four Saccharomyces cerevisiae PPI datasets showed that NFC performed significantly better than all the other existing computational techniques. Our method was able to detect low connectivity lethal proteins that were previously undetected by conventional methods. The results and an online version of NFC is available at http://lethalproteins.i2r.a-star.edu.sg.
Similar articles
-
AVID: an integrative framework for discovering functional relationships among proteins.BMC Bioinformatics. 2005 Jun 1;6:136. doi: 10.1186/1471-2105-6-136. BMC Bioinformatics. 2005. PMID: 15929793 Free PMC article.
-
Identification of functional modules in a PPI network by clique percolation clustering.Comput Biol Chem. 2006 Dec;30(6):445-51. doi: 10.1016/j.compbiolchem.2006.10.001. Epub 2006 Nov 13. Comput Biol Chem. 2006. PMID: 17098476
-
A local average connectivity-based method for identifying essential proteins from the network level.Comput Biol Chem. 2011 Jun;35(3):143-50. doi: 10.1016/j.compbiolchem.2011.04.002. Epub 2011 Apr 30. Comput Biol Chem. 2011. PMID: 21704260
-
The Cartographers toolbox: building bigger and better human protein interaction networks.Brief Funct Genomic Proteomic. 2009 Jan;8(1):1-11. doi: 10.1093/bfgp/elp003. Epub 2009 Mar 12. Brief Funct Genomic Proteomic. 2009. PMID: 19282470 Review.
-
Explorations in topology-delving underneath the surface of genetic interaction maps.Mol Biosyst. 2009 Dec;5(12):1473-81. doi: 10.1039/b907076c. Epub 2009 Sep 8. Mol Biosyst. 2009. PMID: 19763324 Review.
Cited by
-
Elucidating the network features and evolutionary attributes of intra- and interspecific protein-protein interactions between human and pathogenic bacteria.Sci Rep. 2021 Jan 8;11(1):190. doi: 10.1038/s41598-020-80549-x. Sci Rep. 2021. PMID: 33420198 Free PMC article.
-
An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks.Front Big Data. 2022 Feb 16;5:797584. doi: 10.3389/fdata.2022.797584. eCollection 2022. Front Big Data. 2022. PMID: 35252851 Free PMC article.
-
A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.Front Genet. 2021 Aug 6;12:709660. doi: 10.3389/fgene.2021.709660. eCollection 2021. Front Genet. 2021. PMID: 34422014 Free PMC article.
-
Nodes and biological processes identified on the basis of network analysis in the brain of the senescence accelerated mice as an Alzheimer's disease animal model.Front Aging Neurosci. 2013 Oct 29;5:65. doi: 10.3389/fnagi.2013.00065. eCollection 2013. Front Aging Neurosci. 2013. PMID: 24194717 Free PMC article.
-
Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.Front Physiol. 2016 Aug 26;7:375. doi: 10.3389/fphys.2016.00375. eCollection 2016. Front Physiol. 2016. PMID: 27616995 Free PMC article. No abstract available.
MeSH terms
Substances
LinkOut - more resources
Molecular Biology Databases