Structure-based prediction of transcription factor binding specificity using an integrative energy function.
Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this article, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function. The new energy function combines a multibody (MB) knowledge-based potential and two atomic energy terms (hydrogen bond and π interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. We applied the new energy function to the TFBS prediction using a non-redundant dataset that consists of TFs from 12 different families. Our results show that the new IE function improves the prediction accuracy over the knowledge-based, statistical potentials, especially for homeodomain TFs, the second largest TF family in mammals.



jguo4@uncc.edu



Supplementary data are available at Bioinformatics online.
Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Alvin
A
Farrel
Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Jonathan
J
Murphy
Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Jun-Tao
JT
Guo
D000818:Animals; D001665:Binding Sites; D019295:Computational Biology; D004268:DNA-Binding Proteins; D005786:Gene Expression Regulation; D011485:Protein Binding; D014157:Transcription Factors / Q000737:chemistry*
D004268:DNA-Binding Proteins; D014157:Transcription Factors