Guidelines for the analysis of free energy calculations
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Free energy calculations based on molecular dynamics simulations show considerable promise for applications ranging from drug discovery to prediction of physical properties and structure-function studies. But these calculations are still difficult and tedious to analyze, and best practices for analysis are not well defined or propagated. Essentially, each group analyzing these calculations needs to decide how to conduct the analysis and, usually, develop its own analysis tools. Here, we review and recommend best practices for analysis yielding reliable free energies from molecular simulations. Additionally, we provide a Python tool, alchemical-analysis.py, freely available on GitHub as part of the pymbar package (located at http://github.com/choderalab/pymbar), that implements the analysis practices reviewed here for several reference simulation packages, which can be adapted to handle data from other packages. Both this review and the tool covers analysis of alchemical calculations generally, including free energy estimates via both thermodynamic integration and free energy perturbation-based estimators. Our Python tool also handles output from multiple types of free energy calculations, including expanded ensemble and Hamiltonian replica exchange, as well as standard fixed ensemble calculations. We also survey a range of statistical and graphical ways of assessing the quality of the data and free energy estimates, and provide prototypes of these in our tool. We hope this tool and discussion will serve as a foundation for more standardization of and agreement on best practices for analysis of free energy calculations.
KeywordsHydration free energy Transfer free energy Free energy calculation Analysis tool Binding free energy Alchemical
We acknowledge the financial support of the National Institutes of Health (1R15GM096257-01A1, 1R01GM108889-01) and the National Science Foundation (CHE 1352608) and computing support from the UCI GreenPlanet cluster, supported in part by NSF Grant CHE-0840513. We thank Shuai Liu (UCI), Hannes Loeffler (STFC), Stefano Bosisio (University of Edinburgh), and Shun Zhu (Fudan University) for providing data to test the script, Nathan Lim (UCI) and Adam van Wart (UCI) for valuable comments on the draft, and Kyle Beauchamp (Memorial Sloan Kettering Cancer Center) for maintaining the PyMBAR project.
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