DANGLE: A Bayesian inferential method for predicting protein backbone dihedral angles and secondary structure

被引:193
|
作者
Cheung, Ming-Sin [1 ]
Maguire, Mahon L. [1 ]
Stevens, Tim J. [1 ]
Broadhurst, R. William [1 ]
机构
[1] Univ Cambridge, Dept Biochem, Cambridge CB2 1GA, England
基金
英国生物技术与生命科学研究理事会;
关键词
Bioinformatics; Chemical shifts; Dihedral angle prediction; NMR spectroscopy; Protein structure; Secondary structure prediction; NMR CHEMICAL-SHIFTS; SEQUENCE HOMOLOGY; TORSION ANGLES; ACCURATE CALCULATION; STRUCTURE GENERATION; BINDING DOMAIN; SOLID-STATE; WEB SERVER; T-ANTIGEN; DATA-BANK;
D O I
10.1016/j.jmr.2009.11.008
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
This paper introduces DANGLE, a new algorithm that employs Bayesian inference to estimate the likelihood of all possible values of the backbone dihedral angles phi and psi for each residue in a query protein, based on observed chemical shifts and the conformational preferences of each amino acid type. The method provides robust estimates of phi and psi within realistic boundary ranges, an indication of the degeneracy in the relationship between shift measurements and conformation at each site, and faithful secondary Structure state assignments. When a simple degeneracy-based filtering procedure is applied, DANGLE offers an ideal compromise between accuracy and coverage when compared with other shift-based dihedral angle prediction methods. In addition, per residue analysis of shift/structure degeneracy has potential to be a useful new approach for Studying the properties of unfolded proteins, with sufficient sensitivity to identify regions of residual structure in the acid denatured state of apomyoglobin. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:223 / 233
页数:11
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