Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks
- PMID: 23780996
- DOI: 10.1093/bib/bbt039
Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks
Abstract
Cellular processes are typically carried out by protein complexes and functional modules. Identifying them plays an important role for our attempt to reveal principles of cellular organizations and functions. In this article, we review computational algorithms for identifying protein complexes and/or functional modules from protein-protein interaction (PPI) networks. We first describe issues and pitfalls when interpreting PPI networks. Then based on types of data used and main ideas involved, we briefly describe protein complex and/or functional module identification algorithms in four categories: (i) those based on topological structures of unweighted PPI networks; (ii) those based on characters of weighted PPI networks; (iii) those based on multiple data integrations; and (iv) those based on dynamic PPI networks. The PPI networks are modelled increasingly precise when integrating more types of data, and the study of protein complexes would benefit by shifting from static to dynamic PPI networks.
Keywords: dynamic network; functional module; protein complex; protein–protein interaction; static network.
Similar articles
-
A degree-distribution based hierarchical agglomerative clustering algorithm for protein complexes identification.Comput Biol Chem. 2011 Oct 12;35(5):298-307. doi: 10.1016/j.compbiolchem.2011.07.005. Epub 2011 Jul 20. Comput Biol Chem. 2011. PMID: 22000801
-
Identifying protein complexes based on multiple topological structures in PPI networks.IEEE Trans Nanobioscience. 2013 Sep;12(3):165-72. doi: 10.1109/TNB.2013.2264097. Epub 2013 Aug 21. IEEE Trans Nanobioscience. 2013. PMID: 23974659
-
Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data.BMC Bioinformatics. 2012 May 23;13:109. doi: 10.1186/1471-2105-13-109. BMC Bioinformatics. 2012. PMID: 22621308 Free PMC article.
-
Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes.FEBS Lett. 2015 Sep 14;589(19 Pt A):2590-602. doi: 10.1016/j.febslet.2015.04.026. Epub 2015 Apr 23. FEBS Lett. 2015. PMID: 25913176 Review.
-
A survey of computational methods for protein complex prediction from protein interaction networks.J Bioinform Comput Biol. 2013 Apr;11(2):1230002. doi: 10.1142/S021972001230002X. Epub 2012 Nov 7. J Bioinform Comput Biol. 2013. PMID: 23600810 Review.
Cited by
-
Protein complexes identification based on go attributed network embedding.BMC Bioinformatics. 2018 Dec 20;19(1):535. doi: 10.1186/s12859-018-2555-x. BMC Bioinformatics. 2018. PMID: 30572820 Free PMC article.
-
Effects of gastrodin on the expression of brain aging-related genes in SAM/P-8 mice based on network pharmacology.Ibrain. 2022 Nov 6;9(2):157-170. doi: 10.1002/ibra.12076. eCollection 2023 Summer. Ibrain. 2022. PMID: 37786545 Free PMC article.
-
An uncertain model-based approach for identifying dynamic protein complexes in uncertain protein-protein interaction networks.BMC Genomics. 2017 Oct 16;18(Suppl 7):743. doi: 10.1186/s12864-017-4131-6. BMC Genomics. 2017. PMID: 29513194 Free PMC article.
-
A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks.Entropy (Basel). 2023 Apr 15;25(4):665. doi: 10.3390/e25040665. Entropy (Basel). 2023. PMID: 37190452 Free PMC article.
-
Identification of protein complexes and functional modules in E. coli PPI networks.BMC Microbiol. 2020 Aug 6;20(1):243. doi: 10.1186/s12866-020-01904-6. BMC Microbiol. 2020. PMID: 32762711 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources