Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCBB.2013.130 | IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM |
Keywords | Field | DocType |
algorithms,biology and genetics,conformational ensemble,conformational polymorphism,design,experimentation,measurement,mixture distribution,nonmonotonic reasoning and belief revision,particle belief propagation,performance,side-chain prediction,von-mises distribution | ENCODE,Computer science,Protein dynamics,Conformational ensembles,Bioinformatics,Protein Data Bank,Dihedral angle,Belief propagation,Protein structure,Side chain | Journal |
Volume | Issue | ISSN |
11 | 1 | 1557-9964 |
Citations | PageRank | References |
1 | 0.37 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laleh Soltan Ghoraie | 1 | 7 | 1.85 |
| F. J. Burkowski | 2 | 255 | 88.69 |
| Shuai Cheng Li | 3 | 184 | 30.25 |
| Mu Zhu | 4 | 114 | 7.72 |