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Improved Reweighting of Accelerated Molecular Dynamics Simulations for Free Energy Calculation
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Abstract 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 ≤20kBT) 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 (∼2–3kBT). Finally, reweighting using cumulant expansion to the second order is able to recover the most accurate free energy profiles within statistical errors of ∼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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Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 41,298
Molecular dynamics with coupling to an external bath
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,700
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 22,514
Ryckaert, J.-P., Ciccotti, G., & Berendsen, H. J. C. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 21,239
THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130812 · Cited by 6,808
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
Torrie, G. M., & Valleau, J. P. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90121-8 · Cited by 6,091
Nonequilibrium Equality for Free Energy Differences
Jarzynski, C. (1997) · Physical Review Letters · doi:10.1103/physrevlett.78.2690 · Cited by 5,293
Salomon-Ferrer, R., Götz, A. W., Poole, D., Le Grand, S., & Walker, R. C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400314y · Cited by 3,372
Self-citationAn overview of the Amber biomolecular simulation package
Salomon‐Ferrer, R., Case, D. A., & Walker, R. C. (2013) · WIREs Computational Molecular Science · doi:10.1002/wcms.1121 · Cited by 2,623
Self-citationRoutine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born
Götz, A. W., Williamson, M. J., Xu, D., Poole, D., Le Grand, S., & Walker, R. C. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct200909j · Cited by 2,046
Self-citationHow fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980
Laio, A., & Gervasio, F. L. (2008) · Reports on Progress in Physics · doi:10.1088/0034-4885/71/12/126601 · Cited by 1,619
Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules
Hamelberg, D., Mongan, J., & McCammon, J. A. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1755656 · Cited by 1,528
Self-citationEquilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Jarzynski, C. (1997) · Physical Review E · doi:10.1103/physreve.56.5018 · Cited by 1,365
Calculating free energies using average force
Darve, E., & Pohorille, A. (2001) · The Journal of Chemical Physics · doi:10.1063/1.1410978 · Cited by 1,097
Le Grand, S., Götz, A. W., & Walker, R. C. (2013) · Computer Physics Communications · doi:10.1016/j.cpc.2012.09.022 · Cited by 1,075
Self-citationAdaptive biasing force method for scalar and vector free energy calculations
Darve, E., Rodríguez-Gómez, D., & Pohorille, A. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2829861 · Cited by 871
Free energy calculation from steered molecular dynamics simulations using Jarzynski’s equality
Park, S., Khalili-Araghi, F., Tajkhorshid, E., & Schulten, K. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1590311 · Cited by 812
Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics
Pierce, L. C. T., Salomon-Ferrer, R., Augusto F. de Oliveira, C., McCammon, J. A., & Walker, R. C. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300284c · Cited by 513
Self-citationRandom walk in orthogonal space to achieve efficient free-energy simulation of complex systems
Zheng, L., Chen, M., & Yang, W. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810631106 · Cited by 310
Using generalized ensemble simulations and Markov state models to identify conformational states
Bowman, G. R., Huang, X., & Pande, V. S. (2009) · Methods · doi:10.1016/j.ymeth.2009.04.013 · Cited by 302
Fast-growth thermodynamic integration: Error and efficiency analysis
Hummer, G. (2001) · The Journal of Chemical Physics · doi:10.1063/1.1363668 · Cited by 251
Sampling of slow diffusive conformational transitions with accelerated molecular dynamics
Hamelberg, D., de Oliveira, C. A. F., & McCammon, J. A. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2789432 · Cited by 251
Self-citationActivation and dynamic network of the M2 muscarinic receptor
Miao, Y., Nichols, S. E., Gasper, P. M., Metzger, V. T., & McCammon, J. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1309755110 · Cited by 217
Self-citationThe Trp-cage: optimizing the stability of a globular miniprotein
Barua, B., Lin, J. C., Williams, V. D., Kummler, P., Neidigh, J. W., & Andersen, N. H. (2008) · Protein Engineering Design and Selection · doi:10.1093/protein/gzm082 · Cited by 195
Studying functional dynamics in bio-molecules using accelerated molecular dynamics
Markwick, P. R. L., & McCammon, J. A. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp22100k · Cited by 168
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Gasper, P. M., Fuglestad, B., Komives, E. A., Markwick, P. R. L., & McCammon, J. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218414109 · Cited by 156
Self-citationImplementation of accelerated molecular dynamics in NAMD
Wang, Y., Harrison, C. B., Schulten, K. J., & McCammon, J. A. (2011) · Computational Science & Discovery · doi:10.1088/1749-4699/4/1/015002 · Cited by 156
Self-citationFull correlation analysis of conformational protein dynamics
Lange, O. F., & Grubmüller, H. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21618 · Cited by 140
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Shen, T., & Hamelberg, D. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2944250 · Cited by 126
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Bucher, D., Grant, B. J., Markwick, P. R. L., & McCammon, J. A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002034 · Cited by 124
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Self-citationEastwood, M. P., Hardin, C., Luthey-Schulten, Z. A., & Wolynes, P. G. (2002) · The Journal of Chemical Physics · doi:10.1063/1.1494417 · Cited by 56
Miao, Y., Nichols, S. E., & McCammon, J. A. (2013) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12233 · Cited by 53
Self-citationArrar, M., de Oliveira, C. A. F., Fajer, M., Sinko, W., & McCammon, J. A. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300896h · Cited by 44
Self-citationClassical and Quantum Simulations of Tryptophan in Solution
Simonson, T., Wong, C. F., & Brünger, A. T. (1997) · The Journal of Physical Chemistry A · doi:10.1021/jp962810w · Cited by 27
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Metadata
- Authors
- Miao, Y., Sinko, W., Pierce, L., Bucher, D., Walker, R. C., & McCammon, J. A.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-05-09
- Volume
- 10
- Issue
- 7
- Pages
- 2677-2689
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2335200606
- Cited by
- 455
- References
- 42
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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