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Article: Predicting protein complexes from PPI data: A core-attachment approach

TitlePredicting protein complexes from PPI data: A core-attachment approach
Authors
Issue Date2009
PublisherMary Ann Liebert, Inc Publishers. The Journal's web site is located at http://www.liebertpub.com/cmb
Citation
Journal Of Computational Biology, 2009, v. 16 n. 2, p. 133-144 How to Cite?
AbstractProtein complexes play a critical role in many biological processes. Identifying the component proteins in a protein complex is an important step in understanding the complex as well as the related biological activities. This paper addresses the problem of predicting protein complexes from the protein-protein interaction (PPI) network of one species using a computational approach. Most of the previous methods rely on the assumption that proteins within the same complex would have relatively more interactions. This translates into dense subgraphs in the PPI network. However, the existing software tools have limited success. Recently, Gavin et al. (2006) provided a detailed study on the organization of protein complexes and suggested that a complex consists of two parts: a core and an attachment. Based on this core-attachment concept, we developed a novel approach to identify complexes from the PPI network by identifying their cores and attachments separately. We evaluated the effectiveness of our proposed approach using three different datasets and compared the quality of our predicted complexes with three existing tools. The evaluation results show that we can predict many more complexes and with higher accuracy than these tools with an improvement of over 30%. To verify the cores we identified in each complex, we compared our cores with the mediators produced by Andreopoulos et al. (2007), which were claimed to be the cores, based on the benchmark result produced by Gavin et al. (2006). We found that the cores we produced are of much higher quality ranging from 10- to 30-fold more correctly predicted cores and with better accuracy. Availability: http://alse.cs.hku.hk/ complexes/. © Mary Ann Liebert, Inc. 2009.
Persistent Identifierhttp://hdl.handle.net/10722/60624
ISSN
2015 Impact Factor: 1.537
2015 SCImago Journal Rankings: 1.615
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong RGC711608E
Seed Funding Programme for Basic Research200611159001
Research Fund for the Doctoral Program of Higher Education of China4111279
Natural Science Foundation of Guangdong Province, China4203176
Funding Information:

This work was supported in part by Hong Kong RGC ( grant HKU 711608E) and Seed Funding Programme for Basic Research ( grant 200611159001) of the University of Hong Kong. The work of Q. X. was supported in part by Research Fund for the Doctoral Program of Higher Education of China ( grant 4111279) and Natural Science Foundation of Guangdong Province, China ( grant 4203176).

References

 

DC FieldValueLanguage
dc.contributor.authorLeung, HCMen_HK
dc.contributor.authorXiang, Qen_HK
dc.contributor.authorYiu, SMen_HK
dc.contributor.authorChin, FYLen_HK
dc.date.accessioned2010-05-31T04:15:12Z-
dc.date.available2010-05-31T04:15:12Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of Computational Biology, 2009, v. 16 n. 2, p. 133-144en_HK
dc.identifier.issn1066-5277en_HK
dc.identifier.urihttp://hdl.handle.net/10722/60624-
dc.description.abstractProtein complexes play a critical role in many biological processes. Identifying the component proteins in a protein complex is an important step in understanding the complex as well as the related biological activities. This paper addresses the problem of predicting protein complexes from the protein-protein interaction (PPI) network of one species using a computational approach. Most of the previous methods rely on the assumption that proteins within the same complex would have relatively more interactions. This translates into dense subgraphs in the PPI network. However, the existing software tools have limited success. Recently, Gavin et al. (2006) provided a detailed study on the organization of protein complexes and suggested that a complex consists of two parts: a core and an attachment. Based on this core-attachment concept, we developed a novel approach to identify complexes from the PPI network by identifying their cores and attachments separately. We evaluated the effectiveness of our proposed approach using three different datasets and compared the quality of our predicted complexes with three existing tools. The evaluation results show that we can predict many more complexes and with higher accuracy than these tools with an improvement of over 30%. To verify the cores we identified in each complex, we compared our cores with the mediators produced by Andreopoulos et al. (2007), which were claimed to be the cores, based on the benchmark result produced by Gavin et al. (2006). We found that the cores we produced are of much higher quality ranging from 10- to 30-fold more correctly predicted cores and with better accuracy. Availability: http://alse.cs.hku.hk/ complexes/. © Mary Ann Liebert, Inc. 2009.en_HK
dc.languageengen_HK
dc.publisherMary Ann Liebert, Inc Publishers. The Journal's web site is located at http://www.liebertpub.com/cmben_HK
dc.relation.ispartofJournal of Computational Biologyen_HK
dc.subject.meshMarkov Chainsen_HK
dc.subject.meshMathematicsen_HK
dc.subject.meshModels, Theoreticalen_HK
dc.subject.meshMultiprotein Complexes - chemistry - metabolismen_HK
dc.subject.meshProtein Interaction Mappingen_HK
dc.subject.meshProteins - chemistry - metabolismen_HK
dc.subject.meshSoftwareen_HK
dc.titlePredicting protein complexes from PPI data: A core-attachment approachen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1066-5277&volume=16&issue=2&spage=133&epage=144&date=2009&atitle=Predicting+Protein+Complexes+from+PPI+Data:+A+Core-Attachment+Approachen_HK
dc.identifier.emailLeung, HCM:cmleung2@cs.hku.hken_HK
dc.identifier.emailYiu, SM:smyiu@cs.hku.hken_HK
dc.identifier.emailChin, FYL:chin@cs.hku.hken_HK
dc.identifier.authorityLeung, HCM=rp00144en_HK
dc.identifier.authorityYiu, SM=rp00207en_HK
dc.identifier.authorityChin, FYL=rp00105en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1089/cmb.2008.01TTen_HK
dc.identifier.pmid19193141en_HK
dc.identifier.scopuseid_2-s2.0-59649099220en_HK
dc.identifier.hkuros154643en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-59649099220&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue2en_HK
dc.identifier.spage133en_HK
dc.identifier.epage144en_HK
dc.identifier.eissn1557-8666-
dc.identifier.isiWOS:000263057400001-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLeung, HCM=35233742700en_HK
dc.identifier.scopusauthoridXiang, Q=26027303500en_HK
dc.identifier.scopusauthoridYiu, SM=7003282240en_HK
dc.identifier.scopusauthoridChin, FYL=7005101915en_HK
dc.identifier.citeulike4021410-

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