A Structural-Based Strategy for Recognition of Transcription Factor Binding Sites

PLoS One

Yongmei Wang¹  Haojun Liang²  Guohui Li³  Dustin E. Schones⁴  Beisi Xu⁵ 

[1] Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America;Department of Cancer Biology, Beckman Research Institute, City of Hope, Duarte, California, United States of America;Department of Chemistry, University of Memphis, Memphis, Tennessee, United States of America;Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America;Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian, Liaoning, China

关键词: DNA structure;    Protein structure prediction;    Protein structure;    Protein structure databases;    Sequence motif analysis;    Transcription factors;    Crystal structure;    Protein structure determination;   

DOI  :  10.1371/journal.pone.0052460

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Scanning through genomes for potential transcription factor binding sites (TFBSs) is becoming increasingly important in this post-genomic era. The position weight matrix (PWM) is the standard representation of TFBSs utilized when scanning through sequences for potential binding sites. However, many transcription factor (TF) motifs are short and highly degenerate, and methods utilizing PWMs to scan for sites are plagued by false positives. Furthermore, many important TFs do not have well-characterized PWMs, making identification of potential binding sites even more difficult. One approach to the identification of sites for these TFs has been to use the 3D structure of the TF to predict the DNA structure around the TF and then to generate a PWM from the predicted 3D complex structure. However, this approach is dependent on the similarity of the predicted structure to the native structure. We introduce here a novel approach to identify TFBSs utilizing structure information that can be applied to TFs without characterized PWMs, as long as a 3D complex structure (TF/DNA) exists. This approach utilizes an energy function that is uniquely trained on each structure. Our approach leads to increased prediction accuracy and robustness compared with those using a more general energy function. The software is freely available upon request.

【 授权许可】

CC BY