Residue-specific side-chain polymorphismsvia particle belief propagation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number6646171
Pages (from-to)33-41
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume11
Issue number1
Publication statusPublished - Jan 2014

Abstract

Protein side chains populate diverse conformational ensembles in crystals. Despite much evidence that there is widespread conformational polymorphism in protein side chains, most of the X-ray crystallography data are modeled by single conformations in the Protein Data Bank. The ability to extract or to predict these conformational polymorphisms is of crucial importance, as it facilitates deeper understanding of protein dynamics and functionality. In this paper, we describe a computational strategy capable of predicting side-chain polymorphisms. Our approach extends a particular class of algorithms for side-chain prediction by modeling the side-chain dihedral angles more appropriately as continuous rather than discrete variables. Employing a new inferential technique known as particle belief propagation, we predict residue-specific distributions that encode information about side-chain polymorphisms. Our predicted polymorphisms are in relatively close agreement with results from a state-of-the-art approach based on X-ray crystallography data, which characterizes the conformational polymorphisms of side chains using electron density information, and has successfully discovered previously unmodeled conformations. © 2004-2012 IEEE.

Research Area(s)

  • Conformational ensemble, conformational polymorphism, mixture distribution, particle belief propagation, side-chain prediction, von-Mises distribution

Citation Format(s)

Residue-specific side-chain polymorphismsvia particle belief propagation. / Ghoraie, Laleh Soltan; Burkowski, Forbes; Li, Shuai Cheng et al.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 11, No. 1, 6646171, 01.2014, p. 33-41.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review