KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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AbstractProtein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.
PubMed Central IDPMC3287466
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Except where otherwise noted, this item's license is described as 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.
- Backbone dependency further improves side chain prediction efficiency in the Energy-based Conformer Library (bEBL).
- Authors: Subramaniam S, Senes A
- Issue date: 2014 Nov
- A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions.
- Authors: Shapovalov MV, Dunbrack RL Jr
- Issue date: 2011 Jun 8
- Using information theory to discover side chain rotamer classes: analysis of the effects of local backbone structure.
- Authors: Fetrow JS, Berg G
- Issue date: 1999
- Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library.
- Authors: Peterson RW, Dutton PL, Wand AJ
- Issue date: 2004 Mar
- Design of a rotamer library for coarse-grained models in protein-folding simulations.
- Authors: Larriva M, Rey A
- Issue date: 2014 Jan 27
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