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Greedy Algorithms for Fast Discovery of Macrostates from Molecular Dynamics Simulations

Haoyun Feng1, Geoffrey Siwo2, Jesus A. Izaguirre1 , Douglas Thain1, and Badi Abdul-Wahid1
1.Department of Computer Science and Engineering, Notre Dame, US
2.Institute for Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, US
Abstract—With development of distributed computing systems, it is possible to significantly accelerate long-term molecular dynamics simulations by using ensemble algorithms, such as Markov State Models (MSM) and Weighted Ensemble (WE). Decomposing the conformational space of molecule into macrostates is an important step of both methods. To ensure efficiency and accuracy of ensemble methods, it is necessary that the macro states are defined according to certain kinetic properties. Monte Carlo simulated annealing (MCSA) has been widely applied to define macro states with optimal metastability of the dynamical system. This article proposes two greedy algorithms, G1 and G2, based on different definitions of local search space to improve efficiency and scalability of MCSA on distributed computing system. Numerical experiments are conducted on two biological systems, alanine dipeptide and WW domain. The numerical experiments demonstrate that G1 is the most efficient of the three on a single core machine and distributed computing system. Sequential version of G2 is the slowest but it gains the most speed up on distributed computing systems.

Index Terms—molecular dynamics, metastable states, unsupervised clustering, greedy algorithm

Cite: Haoyun Feng, Geoffrey Siwo, Jesus A. Izaguirre, Douglas Thain, and Badi Abdul-Wahid, "Greedy Algorithms for Fast Discovery of Macrostates from Molecular Dynamics Simulations," Lecture Notes on Information Theory, Vol. 2, No. 4, pp. 302-309, December 2014. doi: 10.12720/lnit.2.4.302-309
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