Residue-specific side-chain polymorphismsvia particle belief propagation
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Article number | 6646171 |
Pages (from-to) | 33-41 |
Journal / Publication | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 11 |
Issue number | 1 |
Publication status | Published - Jan 2014 |
Link(s)
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.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 11, No. 1, 6646171, 01.2014, p. 33-41.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review