Scoring protein relationships in functional interaction networks predicted from sequence data

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dc.contributor.authorMazandu, Gaston Ken_ZA
dc.contributor.authorMulder, Nicola Jen_ZA
dc.date.accessioned2015-12-28T06:47:38Z
dc.date.available2015-12-28T06:47:38Z
dc.date.issued2011en_ZA
dc.description.abstractThe abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. Availability Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes .en_ZA
dc.identifier.apacitationMazandu, G. K., & Mulder, N. J. (2011). Scoring protein relationships in functional interaction networks predicted from sequence data. <i>PLoS One</i>, http://hdl.handle.net/11427/16040en_ZA
dc.identifier.chicagocitationMazandu, Gaston K, and Nicola J Mulder "Scoring protein relationships in functional interaction networks predicted from sequence data." <i>PLoS One</i> (2011) http://hdl.handle.net/11427/16040en_ZA
dc.identifier.citationMazandu, G. K., & Mulder, N. J. (2011). Scoring protein relationships in functional interaction networks predicted from sequence data. PLoS One, 6(4), e18607. doi:10.1371/journal.pone.0018607en_ZA
dc.identifier.ris TY - Journal Article AU - Mazandu, Gaston K AU - Mulder, Nicola J AB - The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. Availability Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes . DA - 2011 DB - OpenUCT DO - 10.1371/journal.pone.0018607 DP - University of Cape Town J1 - PLoS One LK - https://open.uct.ac.za PB - University of Cape Town PY - 2011 T1 - Scoring protein relationships in functional interaction networks predicted from sequence data TI - Scoring protein relationships in functional interaction networks predicted from sequence data UR - http://hdl.handle.net/11427/16040 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16040
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0018607
dc.identifier.vancouvercitationMazandu GK, Mulder NJ. Scoring protein relationships in functional interaction networks predicted from sequence data. PLoS One. 2011; http://hdl.handle.net/11427/16040.en_ZA
dc.language.isoengen_ZA
dc.publisherPublic Library of Scienceen_ZA
dc.publisher.departmentInstitute of Infectious Disease and Molecular Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_ZA
dc.rights.holder© 2011 Mazandu, Mulderen_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourcePLoS Oneen_ZA
dc.source.urihttp://journals.plos.org/plosoneen_ZA
dc.subject.otherProtein domainsen_ZA
dc.subject.otherProtein interaction networksen_ZA
dc.subject.otherSequence databasesen_ZA
dc.subject.otherSequence alignmenten_ZA
dc.subject.otherMycobacterium tuberculosisen_ZA
dc.subject.otherSequence similarity searchingen_ZA
dc.subject.otherProtein interactionsen_ZA
dc.subject.otherLipid metabolismen_ZA
dc.titleScoring protein relationships in functional interaction networks predicted from sequence dataen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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