Takahashi, Ryoji, Víctor A. Gil, and Victor Guallar Journal of Chemical Theory and Computation 2014, 10, 282−288.
Contributed by +Jan Jensen
This study uses Monte Carlo (MC) sampling, and a Markov state model analysis of the resulting trajectories, to compute absolute binding free energies for four benzamidine ligands binding to trypsin that are in good agreement with experiment. The measured binding free energies for the same ligand vary a bit and the mean absolute deviation ranges from 0.9 to 1.4 kcal/mol.
The binding free energy for each ligand is derived from a Markov state model analysis of 840 MC trajectories constructed using six different random initial ligand positions - all well away from the protein surface. Each MC trajectory is constructed using the protein energy landscape exploration (PELE) method. There are three kinds of PELE MC moves: (1) the ligand can be translated or rotated rigidly, (2) the internal ligand geometry can be changed using a ligand-specific rotamer library, and (3) all protein atoms are displaced along a randomly picked mode derived from an anisotropic network model followed by minimization of all all atoms except the $\alpha$-carbons.
After each move is made the side-chain orientations close to the ligands are sampled from a rotamer library followed my an OPLS-AA/SGB energy minimization of all atoms affected by the move. The resulting "super move" is accepted or rejected based on a Metropolis criterion.
The total simulation time for a ligand is about 1 week using 64 cores. However, the binding site of each ligand could be identified using only 20-30 trajectories in 5-10 CPU hours. In fact, such a binding site search can be performed using the PELE web server developed by the authors.
With its use of "super moves" with extensive energy minimization this method strikes me as an excellent way to generate snapshots for QM/MM calculations and it seems to me it could be easily adapted to look at enzyme catalysis.
This work is licensed under a Creative Commons Attribution 4.0 International License.
This study uses Monte Carlo (MC) sampling, and a Markov state model analysis of the resulting trajectories, to compute absolute binding free energies for four benzamidine ligands binding to trypsin that are in good agreement with experiment. The measured binding free energies for the same ligand vary a bit and the mean absolute deviation ranges from 0.9 to 1.4 kcal/mol.
The binding free energy for each ligand is derived from a Markov state model analysis of 840 MC trajectories constructed using six different random initial ligand positions - all well away from the protein surface. Each MC trajectory is constructed using the protein energy landscape exploration (PELE) method. There are three kinds of PELE MC moves: (1) the ligand can be translated or rotated rigidly, (2) the internal ligand geometry can be changed using a ligand-specific rotamer library, and (3) all protein atoms are displaced along a randomly picked mode derived from an anisotropic network model followed by minimization of all all atoms except the $\alpha$-carbons.
After each move is made the side-chain orientations close to the ligands are sampled from a rotamer library followed my an OPLS-AA/SGB energy minimization of all atoms affected by the move. The resulting "super move" is accepted or rejected based on a Metropolis criterion.
The total simulation time for a ligand is about 1 week using 64 cores. However, the binding site of each ligand could be identified using only 20-30 trajectories in 5-10 CPU hours. In fact, such a binding site search can be performed using the PELE web server developed by the authors.
With its use of "super moves" with extensive energy minimization this method strikes me as an excellent way to generate snapshots for QM/MM calculations and it seems to me it could be easily adapted to look at enzyme catalysis.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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