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

Abstract

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.

DOI: 10.1016/j.jmr.2009.11.008
05010020102011201220132014201520162017
Citations per Year

313 Citations

Semantic Scholar estimates that this publication has 313 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Cheung2010DANGLEAB, title={DANGLE: A Bayesian inferential method for predicting protein backbone dihedral angles and secondary structure.}, author={M. Cheung and Mahon L. Maguire and Tim J. Stevens and R. William Broadhurst}, journal={Journal of magnetic resonance}, year={2010}, volume={202 2}, pages={223-33} }