Network models for molecular kinetics and their initial applications to human health

被引:44
作者
Bowman, Gregory R. [1 ]
Huang, Xuhui [2 ,3 ]
Pande, Vijay S. [1 ,4 ]
机构
[1] Stanford Univ, Biophys Program, Stanford, CA 94305 USA
[2] Hong Kong Univ Sci & Technol, Dept Chem, Kowloon, Hong Kong, Peoples R China
[3] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA
[4] Stanford Univ, Dept Chem, Stanford, CA 94305 USA
关键词
Markov state models; molecular dynamics; simulations; protein folding; conformational change; Alzheimer's disease; PROTEIN-FOLDING KINETICS; PERRON CLUSTER-ANALYSIS; DYNAMICS SIMULATIONS; BETA-SHEET; CONFORMATIONAL DYNAMICS; FREE-ENERGY; EQUILIBRIUM; PATHWAYS; ENSEMBLE; STATE;
D O I
10.1038/cr.2010.57
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models (MSMs), are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex conformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer's disease.
引用
收藏
页码:622 / 630
页数:9
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