Automated Force Field Parameterization for Nonpolarizable and Polarizable Atomic Models Based on Ab Initio Target Data

By Lei Huang and Benoı̂t Roux.

Published in Journal of Chemical Theory and Computation [Epub ahead of print] on July 12, 2013;9(8). PMID: 24223528. PMCID:3819940. Link to publication page

Core Facility: Computational Modeling


Classical molecular dynamics (MD) simulations based on atomistic models are increasingly used to study a wide range of biological systems. A prerequisite for meaningful results from such simulations is an accurate molecular mechanical force field. Most biomolecular simulations are currently based on the widely used AMBER and CHARMM force fields, which were parametrized and optimized to cover a small set of basic compounds corresponding to the natural amino acids and nucleic acid bases. Atomic models of additional compounds are commonly generated by analogy to the parameter set of a given force field. While this procedure yields models that are internally consistent, the accuracy of the resulting models can be limited. In this work, we propose a method, general automated atomic model parameterization (GAAMP), for generating automatically the parameters of atomic models of small molecules using the results from ab initio quantum mechanical (QM) calculations as target data. Force fields that were previously developed for a wide range of model compounds serve as initial guesses, although any of the final parameter can be optimized. The electrostatic parameters (partial charges, polarizabilities, and shielding) are optimized on the basis of QM electrostatic potential (ESP) and, if applicable, the interaction energies between the compound and water molecules. The soft dihedrals are automatically identified and parametrized by targeting QM dihedral scans as well as the energies of stable conformers. To validate the approach, the solvation free energy is calculated for more than 200 small molecules and MD simulations of three different proteins are carried out.

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Figure 2. Strategy of GAAMP used for charge fitting which combines ESP fitting (shown in the left plot) and compound–water interaction fitting (shown in the right plot).

Figure 4. Snapshot of N-phenylbenzamide.

Figure 7. Calculated solvation free energies for 98 compounds without hydrogen-bond donor/acceptor compared with experimental values.

Figure 10. Calculated solvation free energies using the GAAMP Drude model for 217 compounds compared with experimental values. The AUE is 0.92 kcal/mol.

Figure 11. (left) Snapshots of three proteins with diverse structures. Their PDB IDs are 1ctf, 1mjc, and 1r69, respectively. The plots are generated by PyMol.(65) The traces of RMSD for all Cα atoms for four trajectories of 100 ns MD simulations are compared between using CHARMM 27 (middle) and using GAAMP FF (right) for 1ctf, 1mjc, and 1r69, respectively.