Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 10, Issue 7, Pages 2677-2689Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ct500090q
Keywords
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Funding
- NSF [MCB1020765, SI2-1148276]
- National Institutes of Health (NIH) [GM31749]
- Howard Hughes Medical Institute
- Center for Theoretical Biological Physics (CTBP)
- NVIDIA Inc.
- National Biomedical Computation Resource (NBCR)
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [1020765] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1148276] Funding Source: National Science Foundation
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Accelerated molecular dynamics (aMD) simulations greatly improve the efficiency of conventional molecular dynamics (cMD) for sampling biomolecular conformations, but they require proper reweighting for free energy calculation. In this work, we systematically compare the accuracy of different reweighting algorithms including the exponential average, Maclaurin series, and cumulant expansion on three model systems: alanine dipeptide, chignolin, and Trp-cage. Exponential average reweighting can recover the original free energy profiles easily only when the distribution of the boost potential is narrow (e.g., the range <= 20k(B)T) as found in dihedral-boost aMD simulation of alanine dipeptide. In dual-boost aMD simulations of the studied systems, exponential average generally leads to high energetic fluctuations, largely due to the fact that the Boltzmann reweighting factors are dominated by a very few high boost potential frames. In comparison, reweighting based on Maclaurin series expansion (equivalent to cumulant expansion on the first order) greatly suppresses the energetic noise but often gives incorrect energy minimum positions and significant errors at the energy barriers (similar to 2-3k(B)T). Finally, reweighting using cumulant expansion to the second order is able to recover the most accurate free energy profiles within statistical errors of similar to KBT particularly when the distribution of the boost potential exhibits low anharmonicity (i.e., near-Gaussian distribution), and should be of wide applicability. A toolkit of Python scripts for aMD reweighting PyReweighting is distributed free of charge at http://mccammon.ucsd.edu/computing/amdReweighting/.
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