Beyond rotamers: a generative, probabilistic model of side chains in proteins

BMC Bioinformatics

Research Article

Wouter Boomsma¹  Kristoffer E Johansson²  Tim Harder³  Martin Paluszewski³  Thomas Hamelryck³  Jes Frellsen³ 

[1] DTU Elektro, Technical University of Denmark, Lyngby, Denmark;Department of Chemistry, University of Cambridge, Cambridge, UK;DTU Elektro, Technical University of Denmark, Lyngby, Denmark;Section for Biomolecular Sciences, Department of Biology, University of Copenhagen, Copenhagen, Denmark;The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark;

关键词: Markov Chain Monte Carlo;    Hide Node;    Conformational Space;    Amino Acid Side Chain;    Amino Acid Type;   

DOI  :  10.1186/1471-2105-11-306

 received in 2010-03-03, accepted in 2010-06-05,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAccurately covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. Today, the most common way to capture this conformational space is through rotamer libraries - discrete collections of side chain conformations derived from experimentally determined protein structures. The discretization can be exploited to efficiently search the conformational space. However, discretizing this naturally continuous space comes at the cost of losing detailed information that is crucial for certain applications. For example, rigorously combining rotamers with physical force fields is associated with numerous problems.ResultsIn this work we present BASILISK: a generative, probabilistic model of the conformational space of side chains that makes it possible to sample in continuous space. In addition, sampling can be conditional upon the protein's detailed backbone conformation, again in continuous space - without involving discretization.ConclusionsA careful analysis of the model and a comparison with various rotamer libraries indicates that the model forms an excellent, fully continuous model of side chain conformational space. We also illustrate how the model can be used for rigorous, unbiased sampling with a physical force field, and how it improves side chain prediction when used as a pseudo-energy term. In conclusion, BASILISK is an important step forward on the way to a rigorous probabilistic description of protein structure in continuous space and in atomic detail.

【 授权许可】

Unknown   
© Harder et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.