A similarity network approach for the analysis and comparison of protein sequence/structure sets

Valavanis, I; Spyrou, G; Nikita, K

dc.contributor.author	Valavanis, I	en
dc.contributor.author	Spyrou, G	en
dc.contributor.author	Nikita, K	en
dc.date.accessioned	2014-03-01T01:32:33Z
dc.date.available	2014-03-01T01:32:33Z
dc.date.issued	2010	en
dc.identifier.issn	1532-0464	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20174
dc.subject	Alignment	en
dc.subject	Betweenness	en
dc.subject	Evolution	en
dc.subject	Hub	en
dc.subject	Network	en
dc.subject	Protein sequence	en
dc.subject	Protein structure	en
dc.subject	Sequence-derived features	en
dc.subject	Small World Network	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Medical Informatics	en
dc.subject.other	Betweenness	en
dc.subject.other	Hub networks	en
dc.subject.other	Protein sequence	en
dc.subject.other	Protein sequences	en
dc.subject.other	Protein structures	en
dc.subject.other	Small world networks	en
dc.subject.other	Alignment	en
dc.subject.other	Large scale systems	en
dc.subject.other	Proteins	en
dc.subject.other	amino acid sequence	en
dc.subject.other	analysis	en
dc.subject.other	article	en
dc.subject.other	cluster analysis	en
dc.subject.other	comparative study	en
dc.subject.other	controlled study	en
dc.subject.other	measurement	en
dc.subject.other	methodology	en
dc.subject.other	molecular evolution	en
dc.subject.other	network learning	en
dc.subject.other	priority journal	en
dc.subject.other	protein analysis	en
dc.subject.other	protein localization	en
dc.subject.other	protein structure	en
dc.subject.other	sequence alignment	en
dc.subject.other	Amino Acid Sequence	en
dc.subject.other	Cluster Analysis	en
dc.subject.other	Databases, Protein	en
dc.subject.other	Models, Molecular	en
dc.subject.other	Protein Conformation	en
dc.subject.other	Proteins	en
dc.subject.other	Proteomics	en
dc.subject.other	Sequence Alignment	en
dc.subject.other	Sequence Analysis, Protein	en
dc.title	A similarity network approach for the analysis and comparison of protein sequence/structure sets	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jbi.2010.01.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jbi.2010.01.005	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	A set of proteins is a complex system whose elements are interrelated on the concept of sequence- and structure-based similarity. Here, we applied a similarity network-based methodology for the representation and analysis of protein sequences and structures sets using a non-redundant set of 311 proteins and three different information criteria based on sequence-derived features, sequence local alignment and structural alignment. A wide set of measurements, like network degree, clustering coefficient, characteristic path length and vertex centrality were utilized to characterize the networks' topology. Protein similarity networks were found medium or highly interconnected and the existence of both clusters and random edges classified their fully connected versions as Small World Networks (SWNs). The SWN architecture was able to host the continuous similarity transition among proteins and model the protein information flow during evolution. Recently reported ancestral elements, like the alpha/beta class and certain folds, were remarkably found to act as hubs in the networks. Additionally, the moderate information value of sequence-derived features when used for fold and class assignment was shown on a network basis. The methodology described here can be applied for the analysis of other complex systems which consist of interrelated elements and a certain information flow. (C) 2010 Elsevier Inc. All rights reserved.	en
heal.publisher	ACADEMIC PRESS INC ELSEVIER SCIENCE	en
heal.journalName	Journal of Biomedical Informatics	en
dc.identifier.doi	10.1016/j.jbi.2010.01.005	en
dc.identifier.isi	ISI:000276012800009	en
dc.identifier.volume	43	en
dc.identifier.issue	2	en
dc.identifier.spage	257	en
dc.identifier.epage	267	en