Abstract
The Independent-Trajectory Thermodynamic Integration (IT-TI) approach for free energy calculation with distributed computing is described. IT-TI utilizes diverse conformational sampling obtained from multiple, independent simulations to obtain more reliable free energy estimates compared to single TI predictions. The latter may significantly under- or over-estimate the binding free energy due to finite sampling. We exemplify the advantages of the IT-TI approach using two distinct cases of protein–ligand binding. In both cases, IT-TI yields distributions of absolute binding free energy estimates that are remarkably centered on the target experimental values. Alternative protocols for the practical and general application of IT-TI calculations are investigated. We highlight a protocol that maximizes predictive power and computational efficiency.
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Acknowledgement
The authors thank the members of the McCammon group for useful discussions. This work was supported, in part, by the National Institutes of Health, the National Science Foundation, the National Biomedical Computational Resource, and the Howard Hughes Medical Institute. We thank the Center for Theoretical Biological Physics (NSF Grant PHY-0822283), and the Texas Advanced Computer Center (grant TG-MCA93S013) for distributed computing resources. We also thank Dr. Ross C. Walker at the San Diego Supercomputing Center for additional computational resources.
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Lawrenz, M., Baron, R., Wang, Y., McCammon, J.A. (2012). Independent-Trajectory Thermodynamic Integration: A Practical Guide to Protein-Drug Binding Free Energy Calculations Using Distributed Computing. In: Baron, R. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 819. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-465-0_27
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