Three enhancements to the inference of statistical protein-DNA potentials

被引:6
作者
AlQuraishi, Mohammed [1 ]
McAdams, Harley H. [1 ]
机构
[1] Stanford Univ, Dept Dev Biol, Beckman Ctr B300, Sch Med, Stanford, CA 94305 USA
关键词
protein-DNA binding; energy potentials; structural biology; DNA binding sites; DNA motifs; machine learning; biophysics; STRUCTURE-BASED PREDICTION; NUCLEIC-ACID STRUCTURES; FACTOR-BINDING PROFILES; TRANSCRIPTION FACTORS; BACILLUS-SUBTILIS; ENERGY FUNCTION; GENE-REGULATION; REPLICATION TERMINATORS; CAENORHABDITIS-ELEGANS; REGULATORY PROTEINS;
D O I
10.1002/prot.24201
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The energetics of protein-DNA interactions are often modeled using so-called statistical potentials, that is, energy models derived from the atomic structures of protein-DNA complexes. Many statistical protein-DNA potentials based on differing theoretical assumptions have been investigated, but little attention has been paid to the types of data and the parameter estimation process used in deriving the statistical potentials. We describe three enhancements to statistical potential inference that significantly improve the accuracy of predicted protein-DNA interactions: (i) incorporation of binding energy data of protein-DNA complexes, in conjunction with their X-ray crystal structures, (ii) use of spatially-aware parameter fitting, and (iii) use of ensemble-based parameter fitting. We apply these enhancements to three widely-used statistical potentials and use the resulting enhanced potentials in a structure-based prediction of the DNA binding sites of proteins. These enhancements are directly applicable to all statistical potentials used in protein-DNA modeling, and we show that they can improve the accuracy of predicted DNA binding sites by up to 21%. Proteins 2013. (C) 2012 Wiley Periodicals, Inc.
引用
收藏
页码:426 / 442
页数:17
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