Perspective: Coarse-grained models for biomolecular systems

被引:658
|
作者
Noid, W. G. [1 ]
机构
[1] Penn State Univ, Dept Chem, University Pk, PA 16802 USA
来源
JOURNAL OF CHEMICAL PHYSICS | 2013年 / 139卷 / 09期
基金
美国国家科学基金会;
关键词
MOLECULAR-DYNAMICS SIMULATIONS; ELASTIC NETWORK MODELS; MONTE-CARLO-SIMULATION; PROTEIN-STRUCTURE SIMULATIONS; SECONDARY STRUCTURE FORMATION; STRUCTURE-DERIVED POTENTIALS; KNOWLEDGE-BASED POTENTIALS; POLARIZABLE FORCE-FIELD; AMINO-ACID-SEQUENCE; GREEN-YVON EQUATION;
D O I
10.1063/1.4818908
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems. (C) 2013 AIP Publishing LLC.
引用
收藏
页数:25
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