Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps

doi:10.1063/1.5100968

	Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps
	Tan, Qingzhe 4; Duan, Mojie 3,4; Li, Minghai 1,4; Han, Li 2; Huo, Shuanghong 4
刊名	JOURNAL OF CHEMICAL PHYSICS
	2019-09-14
卷号	151 期号:10 页码:11
ISSN号	0021-9606
DOI	10.1063/1.5100968
英文摘要	The diffusion map is a dimensionality reduction method. The reduction coordinates are associated with the leading eigenfunctions of the backward Fokker-Planck operator, providing a dynamic meaning for these coordinates. One of the key factors that affect the accuracy of diffusion map embedding is the dynamic measure implemented in the Gaussian kernel. A common practice in diffusion map study of molecular systems is to approximate dynamic proximity with RMSD (root-mean-square deviation). In this paper, we present a hybrid geometry-energy based kernel. Since high energy-barriers may exist between geometrically similar conformations, taking both RMSD and energy difference into account in the kernel can better describe conformational transitions between neighboring conformations and lead to accurate embedding. We applied our diffusion map method to the beta-hairpin of the B1 domain of streptococcal protein G and to Trp-cage. Our results in beta-hairpin show that the diffusion map embedding achieves better results with the hybrid kernel than that with the RMSD-based kernel in terms of free energy landscape characterization and a new correlation measure between the cluster center Euclidean distances in the reduced-dimension space and the reciprocals of the total net flow between these clusters. In addition, our diffusion map analysis of the ultralong molecular dynamics trajectory of Trp-cage has provided a unified view of its folding mechanism. These promising results demonstrate the effectiveness of our diffusion map approach in the analysis of the dynamics and thermodynamics of molecular systems. The hybrid geometry-energy criterion could be also useful as a general dynamic measure for other purposes.
资助项目	National Institutes of Health[RO1-GM088326] ; National Natural Science Foundation of China[21773298]
WOS关键词	MARKOV STATE MODELS ; TRP-CAGE ; UNFOLDED STATE ; FOLDING MECHANISMS ; HIDDEN COMPLEXITY ; SIMULATION ; EXPLORATION ; STABILITY ; PROTEINS ; KINETICS
WOS研究方向	Chemistry ; Physics
语种	英语
出版者	AMER INST PHYSICS
WOS记录号	WOS:000486007100027
资助机构	National Institutes of Health ; National Institutes of Health ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Institutes of Health ; National Institutes of Health ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Institutes of Health ; National Institutes of Health ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Institutes of Health ; National Institutes of Health ; National Natural Science Foundation of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.wipm.ac.cn/handle/112942/21496]
专题	中国科学院武汉物理与数学研究所
通讯作者	Duan, Mojie; Huo, Shuanghong
作者单位	1.NetBrain Tech Inc, 15 Network Dr, Burlington, MA 01803 USA 2.Clark Univ, Dept Math & Comp Sci, Worcester, MA 01610 USA 3.Chinese Acad Sci, Wuhan Inst Phys & Math, Key Lab Magnet Resonance Biol Syst, Natl Ctr Magnet Resonance Wuhan,State Key Lab Mag, Wuhan 430071, Hubei, Peoples R China 4.Clark Univ, Gustaf H Carlson Sch Chem & Biochem, 950 Main St, Worcester, MA 01610 USA
推荐引用方式 GB/T 7714	Tan, Qingzhe,Duan, Mojie,Li, Minghai,et al. Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps[J]. JOURNAL OF CHEMICAL PHYSICS,2019,151(10):11.
APA	Tan, Qingzhe,Duan, Mojie,Li, Minghai,Han, Li,&Huo, Shuanghong.(2019).Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps.JOURNAL OF CHEMICAL PHYSICS,151(10),11.
MLA	Tan, Qingzhe,et al."Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps".JOURNAL OF CHEMICAL PHYSICS 151.10(2019):11.