Computational learning on specificity-determining residue-nucleotide interactions

Handle URI:
http://hdl.handle.net/10754/592815
Title:
Computational learning on specificity-determining residue-nucleotide interactions
Authors:
Wong, Ka-Chun; Li, Yue; Peng, Chengbin ( 0000-0002-7445-2638 ) ; Moses, Alan M.; Zhang, Zhaolei
Abstract:
The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Computational learning on specificity-determining residue-nucleotide interactions 2015:gkv1134 Nucleic Acids Research
Publisher:
Oxford University Press (OUP)
Journal:
Nucleic Acids Research
Issue Date:
2-Nov-2015
DOI:
10.1093/nar/gkv1134
Type:
Article
ISSN:
0305-1048; 1362-4962
Additional Links:
http://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkv1134
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWong, Ka-Chunen
dc.contributor.authorLi, Yueen
dc.contributor.authorPeng, Chengbinen
dc.contributor.authorMoses, Alan M.en
dc.contributor.authorZhang, Zhaoleien
dc.date.accessioned2016-01-05T07:18:42Zen
dc.date.available2016-01-05T07:18:42Zen
dc.date.issued2015-11-02en
dc.identifier.citationComputational learning on specificity-determining residue-nucleotide interactions 2015:gkv1134 Nucleic Acids Researchen
dc.identifier.issn0305-1048en
dc.identifier.issn1362-4962en
dc.identifier.doi10.1093/nar/gkv1134en
dc.identifier.urihttp://hdl.handle.net/10754/592815en
dc.description.abstractThe protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkv1134en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.titleComputational learning on specificity-determining residue-nucleotide interactionsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNucleic Acids Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kongen
dc.contributor.institutionTerrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canadaen
dc.contributor.institutionCSAIL, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USAen
dc.contributor.institutionDepartment of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canadaen
dc.contributor.institutionDepartment of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canadaen
dc.contributor.institutionBanting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canadaen
dc.contributor.institutionDepartment of Molecular Genetics, University of Toronto, Toronto, Ontario, Canadaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorPeng, Chengbinen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.