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Constant pH Molecular Dynamics in Explicit Solvent with Enveloping Distribution Sampling and Hamiltonian Exchange

Cited 66 time in Web of Science Cited 68 time in Scopus
Authors

Lee, Ju Yong; Miller, Benjamin T.; Damjanovic, Ana; Brooks, Bernard R.

Issue Date
2014-07
Publisher
American Chemical Society
Citation
Journal of Chemical Theory and Computation, Vol.10 No.7, pp.2738-2750
Abstract
We present a new computational approach for constant pH simulations in explicit solvent based on the combination of the enveloping distribution sampling (EDS) and Hamiltonian replica exchange (HREX) methods. Unlike constant pH methods based on variable and continuous charge models, our method is based on discrete protonation states. EDS generates a hybrid Hamiltonian of different protonation states. A smoothness parameter s is used to control the heights of energy barriers of the hybrid-state energy landscape. A small s value facilitates state transitions by lowering energy barriers. Replica exchange between EDS potentials with different s values allows us to readily obtain a thermodynamically accurate ensemble of multiple protonation states with frequent state transitions. The analysis is performed with an ensemble obtained from an EDS Hamiltonian without smoothing, s = infinity, which strictly follows the minimum energy surface of the end states. The accuracy and efficiency of this method is tested on aspartic acid, lysine, and glutamic acid, which have two protonation states, a histidine with three states, a four-residue peptide with four states, and snake cardiotoxin with eight states. The pK(a) values estimated with the EDS-HREX method agree well with the experimental pK(a) values. The mean absolute errors of small benchmark systems range from 0.03 to 0.17 pK(a) units, and those of three titratable groups of snake cardiotoxin range from 0.2 to 1.6 pK(a) units. This study demonstrates that EDS-HREX is a potent theoretical framework, which gives the correct description of multiple protonation states and good calculated pK(a) values.
ISSN
1549-9618
URI
https://hdl.handle.net/10371/201537
DOI
https://doi.org/10.1021/ct500175m
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

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