Simplified Method for Predicting a Functional Class of Proteins in Transcription Factor Complexes

Handle URI:
http://hdl.handle.net/10754/325320
Title:
Simplified Method for Predicting a Functional Class of Proteins in Transcription Factor Complexes
Authors:
Piatek, Marek J.; Schramm, Michael C.; Burra, Dharani Dhar; BinShbreen, Abdulaziz; Jankovic, Boris R.; Chowdhary, Rajesh; Archer, John A.C. ( 0000-0002-3302-3933 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
Background:Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs). TcoFs are important since they modulate the transcription initiation process through interaction with TFs. In eukaryotes, transcription requires that TFs form different protein complexes with various nuclear proteins. To better understand transcription regulation, it is important to know the functional class of proteins interacting with TFs during transcription initiation. Such information is not fully available, since not all proteins that act as TFs or TcoFs are yet annotated as such, due to generally partial functional annotation of proteins. In this study we have developed a method to predict, using only sequence composition of the interacting proteins, the functional class of human TF binding partners to be (i) TF, (ii) TcoF, or (iii) other nuclear protein. This allows for complementing the annotation of the currently known pool of nuclear proteins. Since only the knowledge of protein sequences is required in addition to protein interaction, the method should be easily applicable to many species.Results:Based on experimentally validated interactions between human TFs with different TFs, TcoFs and other nuclear proteins, our two classification systems (implemented as a web-based application) achieve high accuracies in distinguishing TFs and TcoFs from other nuclear proteins, and TFs from TcoFs respectively.Conclusion:As demonstrated, given the fact that two proteins are capable of forming direct physical interactions and using only information about their sequence composition, we have developed a completely new method for predicting a functional class of TF interacting protein partners with high precision and accuracy. © 2013 Piatek et al.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Piatek MJ, Schramm MC, Burra DD, binShbreen A, Jankovic BR, et al. (2013) Simplified Method for Predicting a Functional Class of Proteins in Transcription Factor Complexes. PLoS ONE 8: e68857. doi:10.1371/journal.pone.0068857.
Publisher:
Public Library of Science
Journal:
PLoS ONE
Issue Date:
12-Jul-2013
DOI:
10.1371/journal.pone.0068857
PubMed ID:
23874789
PubMed Central ID:
PMC3709904
Type:
Article
ISSN:
19326203
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorPiatek, Marek J.en
dc.contributor.authorSchramm, Michael C.en
dc.contributor.authorBurra, Dharani Dharen
dc.contributor.authorBinShbreen, Abdulazizen
dc.contributor.authorJankovic, Boris R.en
dc.contributor.authorChowdhary, Rajeshen
dc.contributor.authorArcher, John A.C.en
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2014-08-27T09:46:49Zen
dc.date.available2014-08-27T09:46:49Zen
dc.date.issued2013-07-12en
dc.identifier.citationPiatek MJ, Schramm MC, Burra DD, binShbreen A, Jankovic BR, et al. (2013) Simplified Method for Predicting a Functional Class of Proteins in Transcription Factor Complexes. PLoS ONE 8: e68857. doi:10.1371/journal.pone.0068857.en
dc.identifier.issn19326203en
dc.identifier.pmid23874789en
dc.identifier.doi10.1371/journal.pone.0068857en
dc.identifier.urihttp://hdl.handle.net/10754/325320en
dc.description.abstractBackground:Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs). TcoFs are important since they modulate the transcription initiation process through interaction with TFs. In eukaryotes, transcription requires that TFs form different protein complexes with various nuclear proteins. To better understand transcription regulation, it is important to know the functional class of proteins interacting with TFs during transcription initiation. Such information is not fully available, since not all proteins that act as TFs or TcoFs are yet annotated as such, due to generally partial functional annotation of proteins. In this study we have developed a method to predict, using only sequence composition of the interacting proteins, the functional class of human TF binding partners to be (i) TF, (ii) TcoF, or (iii) other nuclear protein. This allows for complementing the annotation of the currently known pool of nuclear proteins. Since only the knowledge of protein sequences is required in addition to protein interaction, the method should be easily applicable to many species.Results:Based on experimentally validated interactions between human TFs with different TFs, TcoFs and other nuclear proteins, our two classification systems (implemented as a web-based application) achieve high accuracies in distinguishing TFs and TcoFs from other nuclear proteins, and TFs from TcoFs respectively.Conclusion:As demonstrated, given the fact that two proteins are capable of forming direct physical interactions and using only information about their sequence composition, we have developed a completely new method for predicting a functional class of TF interacting protein partners with high precision and accuracy. © 2013 Piatek et al.en
dc.language.isoenen
dc.publisherPublic Library of Scienceen
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
dc.rightsArchived with thanks to PLoS ONEen
dc.subjectnuclear proteinen
dc.subjectregulator proteinen
dc.subjecttranscription cofactoren
dc.subjecttranscription factoren
dc.subjectunclassified drugen
dc.subjectaccuracyen
dc.subjectbinding affinityen
dc.subjectcomplex formationen
dc.subjectphysical chemistryen
dc.subjectprotein functionen
dc.subjectprotein protein interactionen
dc.subjectsequence analysisen
dc.subjectComputational Biologyen
dc.subjectDatabases, Proteinen
dc.subjectMultiprotein Complexesen
dc.subjectProtein Bindingen
dc.subjectTranscription Factorsen
dc.titleSimplified Method for Predicting a Functional Class of Proteins in Transcription Factor Complexesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalPLoS ONEen
dc.identifier.pmcidPMC3709904en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionBiomedical Informatics Research Center, MCRF, Marshfield Clinic, Marshfield, WI, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorArcher, John A.C.en
kaust.authorBajic, Vladimir B.en
kaust.authorPiatek, Marek J.en
kaust.authorSchramm, Michael C.en
kaust.authorBurra, Dharani Dharen
kaust.authorBinShbreen, Abdulazizen
kaust.authorJankovic, Boris R.en
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