Computing conformational free energy differences in explicit solvent: An efficient thermodynamic cycle using an auxiliary potential and a free energy functional constructed from the end points
Robert C. Harris
Department of Chemistry and Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
Search for more papers by this authorNanjie Deng
Department of Chemistry and Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
Department of Chemistry and Physical Sciences, Dyson College of Arts and Sciences, Pace University, New York, New York, 10038
Search for more papers by this authorCorresponding Author
Ronald M. Levy
Department of Chemistry and Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
E-mail: [email protected] or [email protected]Search for more papers by this authorRyosuke Ishizuka
Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531 Japan
Search for more papers by this authorCorresponding Author
Nobuyuki Matubayasi
Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531 Japan
Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto, 615-8520 Japan
E-mail: [email protected] or [email protected]Search for more papers by this authorRobert C. Harris
Department of Chemistry and Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
Search for more papers by this authorNanjie Deng
Department of Chemistry and Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
Department of Chemistry and Physical Sciences, Dyson College of Arts and Sciences, Pace University, New York, New York, 10038
Search for more papers by this authorCorresponding Author
Ronald M. Levy
Department of Chemistry and Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
E-mail: [email protected] or [email protected]Search for more papers by this authorRyosuke Ishizuka
Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531 Japan
Search for more papers by this authorCorresponding Author
Nobuyuki Matubayasi
Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531 Japan
Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto, 615-8520 Japan
E-mail: [email protected] or [email protected]Search for more papers by this authorAbstract
Many biomolecules undergo conformational changes associated with allostery or ligand binding. Observing these changes in computer simulations is difficult if their timescales are long. These calculations can be accelerated by observing the transition on an auxiliary free energy surface with a simpler Hamiltonian and connecting this free energy surface to the target free energy surface with free energy calculations. Here, we show that the free energy legs of the cycle can be replaced with energy representation (ER) density functional approximations. We compute: (1) The conformational free energy changes for alanine dipeptide transitioning from the right-handed free energy basin to the left-handed basin and (2) the free energy difference between the open and closed conformations of β-cyclodextrin, a “host” molecule that serves as a model for molecular recognition in host-guest binding. β-cyclodextrin contains 147 atoms compared to 22 atoms for alanine dipeptide, making β-cyclodextrin a large molecule for which to compute solvation free energies by free energy perturbation or integration methods and the largest system for which the ER method has been compared to exact free energy methods. The ER method replaced the 28 simulations to compute each coupling free energy with two endpoint simulations, reducing the computational time for the alanine dipeptide calculation by about 70% and for the β-cyclodextrin by > 95%. The method works even when the distribution of conformations on the auxiliary free energy surface differs substantially from that on the target free energy surface, although some degree of overlap between the two surfaces is required. © 2016 Wiley Periodicals, Inc.
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