用于蛋白质高效构象采样的计算技术。
Computational techniques for efficient conformational sampling of proteins.
作者信息
Liwo Adam, Czaplewski Cezary, Ołdziej Stanisław, Scheraga Harold A
机构信息
Baker Laboratory of Chemistry, Cornell University, Ithaca, NY 14853-1301, United States.
出版信息
Curr Opin Struct Biol. 2008 Apr;18(2):134-9. doi: 10.1016/j.sbi.2007.12.001. Epub 2008 Jan 22.
Abstract
In this review, we summarize the computational methods for sampling the conformational space of biomacromolecules. We discuss the methods applicable to find only lowest energy conformations (global minimization of the potential-energy function) and to generate canonical ensembles (canonical Monte Carlo method and canonical molecular dynamics method and their extensions). Special attention is devoted to the use of coarse-grained models that enable simulations to be enhanced by several orders of magnitude.
摘要
在本综述中，我们总结了用于对生物大分子构象空间进行采样的计算方法。我们讨论了仅适用于寻找最低能量构象（势能函数的全局最小化）以及生成正则系综（正则蒙特卡罗方法和正则分子动力学方法及其扩展）的方法。我们特别关注粗粒度模型的使用，该模型能够将模拟提升几个数量级。
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本文引用的文献
1
Replica Exchange and Multicanonical Algorithms with the coarse-grained UNRES force field.
J Chem Theory Comput. 2006;2(3):513-528. doi: 10.1021/ct050253o.
2
High accuracy template based modeling by global optimization.
Proteins. 2007;69 Suppl 8:83-9. doi: 10.1002/prot.21628.
3
Folding pathway of the b1 domain of protein G explored by multiscale modeling.
Biophys J. 2008 Feb 1;94(3):726-36. doi: 10.1529/biophysj.107.116095. Epub 2007 Sep 21.
4
On searching in, sampling of, and dynamically moving through conformational space of biomolecular systems: A review.
J Comput Chem. 2008 Jan 30;29(2):157-66. doi: 10.1002/jcc.20725.
5
Improved sampling methods for molecular simulation.
Curr Opin Struct Biol. 2007 Apr;17(2):187-91. doi: 10.1016/j.sbi.2007.03.003. Epub 2007 Mar 26.
6
All-atom ab initio folding of a diverse set of proteins.
Structure. 2007 Jan;15(1):53-63. doi: 10.1016/j.str.2006.11.010.
7
Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins.
J Phys Chem B. 2007 Jan 11;111(1):260-85. doi: 10.1021/jp065380a.
8
A knowledge-based move set for protein folding.
Proteins. 2007 Feb 15;66(3):682-8. doi: 10.1002/prot.21237.
9
Protein-folding dynamics: overview of molecular simulation techniques.
Annu Rev Phys Chem. 2007;58:57-83. doi: 10.1146/annurev.physchem.58.032806.104614.
10
Hydrophobic aided replica exchange: an efficient algorithm for protein folding in explicit solvent.
J Phys Chem B. 2006 Sep 28;110(38):19018-22. doi: 10.1021/jp060365r.
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