title: Computational learning on specificity-determining residue-nucleotide interactions.
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.
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong kc.w@cityu.edu.hk.
authors.forename: Ka-Chun
authors.initials: KC
authors.lastname: Wong
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada CSAIL, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Li
authors.affiliation: CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, Saudi Arabia.
authors.forename: Chengbin
authors.initials: C
authors.lastname: Peng
authors.affiliation: Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Alan M
authors.initials: AM
authors.lastname: Moses
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada zhaolei.zhang@utoronto.ca.
authors.forename: Zhaolei
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D004247:DNA / Q000737:chemistry* / Q000378:metabolism; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D000069550:Machine Learning; D008958:Models, Molecular; D059372:Nucleotide Motifs; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D017422:Sequence Analysis, DNA; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D004268:DNA-Binding Proteins; D004247:DNA