TY - JOUR
T1 - Residue-specific side-chain polymorphismsvia particle belief propagation
AU - Ghoraie, Laleh Soltan
AU - Burkowski, Forbes
AU - Li, Shuai Cheng
AU - Zhu, Mu
PY - 2014/1
Y1 - 2014/1
N2 - 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.
AB - 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.
KW - Conformational ensemble
KW - conformational polymorphism
KW - mixture distribution
KW - particle belief propagation
KW - side-chain prediction
KW - von-Mises distribution
UR - http://www.scopus.com/inward/record.url?scp=84901311458&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84901311458&origin=recordpage
U2 - 10.1109/TCBB.2013.130
DO - 10.1109/TCBB.2013.130
M3 - RGC 21 - Publication in refereed journal
SN - 1545-5963
VL - 11
SP - 33
EP - 41
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 1
M1 - 6646171
ER -