A Fast and High-quality Charge Model for the Next Generation General AMBER Force Field

Overview

Journal J Chem Phys

Publisher American Institute of Physics

Specialties Biophysics
Chemistry

Date 2020 Sep 23

PMID 32962378

Citations 164

Authors

Xibing He

Viet H Man

Wei Yang

Tai-Sung Lee

Junmei Wang

Affiliations

Soon will be listed here.

Abstract

The General AMBER Force Field (GAFF) has been broadly used by researchers all over the world to perform in silico simulations and modelings on diverse scientific topics, especially in the field of computer-aided drug design whose primary task is to accurately predict the affinity and selectivity of receptor-ligand binding. The atomic partial charges in GAFF and the second generation of GAFF (GAFF2) were originally developed with the quantum mechanics derived restrained electrostatic potential charge, but in practice, users usually adopt an efficient charge method, Austin Model 1-bond charge corrections (AM1-BCC), based on which, without expensive ab initio calculations, the atomic charges could be efficiently and conveniently obtained with the ANTECHAMBER module implemented in the AMBER software package. In this work, we developed a new set of BCC parameters specifically for GAFF2 using 442 neutral organic solutes covering diverse functional groups in aqueous solution. Compared to the original BCC parameter set, the new parameter set significantly reduced the mean unsigned error (MUE) of hydration free energies from 1.03 kcal/mol to 0.37 kcal/mol. More excitingly, this new AM1-BCC model also showed excellent performance in the solvation free energy (SFE) calculation on diverse solutes in various organic solvents across a range of different dielectric constants. In this large-scale test with totally 895 neutral organic solvent-solute systems, the new parameter set led to accurate SFE predictions with the MUE and the root-mean-square-error of 0.51 kcal/mol and 0.65 kcal/mol, respectively. This newly developed charge model, ABCG2, paved a promising path for the next generation GAFF development.

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References

Goel H, Yu W, Ustach V, Aytenfisu A, Sun D, MacKerell Jr A . Impact of electronic polarizability on protein-functional group interactions. Phys Chem Chem Phys. 2020; 22(13):6848-6860. PMC: 7194236. DOI: 10.1039/d0cp00088d. View

Vanommeslaeghe K, MacKerell Jr A . CHARMM additive and polarizable force fields for biophysics and computer-aided drug design. Biochim Biophys Acta. 2014; 1850(5):861-871. PMC: 4334745. DOI: 10.1016/j.bbagen.2014.08.004. View

Tian C, Kasavajhala K, Belfon K, Raguette L, Huang H, Migues A . ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J Chem Theory Comput. 2019; 16(1):528-552. DOI: 10.1021/acs.jctc.9b00591. View

Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J . CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem. 2009; 31(4):671-90. PMC: 2888302. DOI: 10.1002/jcc.21367. View

Mobley D, Dumont E, Chodera J, Dill K . Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J Phys Chem B. 2007; 111(9):2242-54. DOI: 10.1021/jp0667442. View

Mobley D, Guthrie J . FreeSolv: a database of experimental and calculated hydration free energies, with input files. J Comput Aided Mol Des. 2014; 28(7):711-20. PMC: 4113415. DOI: 10.1007/s10822-014-9747-x. View

Kaus J, Pierce L, Walker R, McCammont J . Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package. J Chem Theory Comput. 2013; 9(9). PMC: 3811123. DOI: 10.1021/ct400340s. View

Yu W, He X, Vanommeslaeghe K, MacKerell Jr A . Extension of the CHARMM General Force Field to sulfonyl-containing compounds and its utility in biomolecular simulations. J Comput Chem. 2012; 33(31):2451-68. PMC: 3477297. DOI: 10.1002/jcc.23067. View

Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang J . End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem Rev. 2019; 119(16):9478-9508. DOI: 10.1021/acs.chemrev.9b00055. View

10.

Lemkul J, Huang J, Roux B, MacKerell Jr A . An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications. Chem Rev. 2016; 116(9):4983-5013. PMC: 4865892. DOI: 10.1021/acs.chemrev.5b00505. View

11.

Lee T, Hu Y, Sherborne B, Guo Z, York D . Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration. J Chem Theory Comput. 2017; 13(7):3077-3084. PMC: 5843186. DOI: 10.1021/acs.jctc.7b00102. View

12.

Wang J, Cieplak P, Luo R, Duan Y . Development of Polarizable Gaussian Model for Molecular Mechanical Calculations I: Atomic Polarizability Parameterization To Reproduce ab Initio Anisotropy. J Chem Theory Comput. 2019; 15(2):1146-1158. PMC: 7197406. DOI: 10.1021/acs.jctc.8b00603. View

13.

Izadi S, Anandakrishnan R, Onufriev A . Building Water Models: A Different Approach. J Phys Chem Lett. 2014; 5(21):3863-3871. PMC: 4226301. DOI: 10.1021/jz501780a. View

14.

Wang J, Wolf R, Caldwell J, Kollman P, Case D . Development and testing of a general amber force field. J Comput Chem. 2004; 25(9):1157-74. DOI: 10.1002/jcc.20035. View

15.

Jorgensen W . Efficient drug lead discovery and optimization. Acc Chem Res. 2009; 42(6):724-33. PMC: 2727934. DOI: 10.1021/ar800236t. View

16.

Chodera J, Mobley D, Shirts M, Dixon R, Branson K, Pande V . Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol. 2011; 21(2):150-60. PMC: 3085996. DOI: 10.1016/j.sbi.2011.01.011. View

17.

Hansen N, van Gunsteren W . Practical Aspects of Free-Energy Calculations: A Review. J Chem Theory Comput. 2015; 10(7):2632-47. DOI: 10.1021/ct500161f. View

18.

Michel J, Verdonk M, Essex J . Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization?. J Med Chem. 2006; 49(25):7427-39. DOI: 10.1021/jm061021s. View

19.

Maier J, Martinez C, Kasavajhala K, Wickstrom L, Hauser K, Simmerling C . ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J Chem Theory Comput. 2015; 11(8):3696-713. PMC: 4821407. DOI: 10.1021/acs.jctc.5b00255. View

20.

Mobley D, Bayly C, Cooper M, Shirts M, Dill K . Correction to Small Molecule Hydration Free Energies in Explicit Solvent: An Extensive Test of Fixed-Charge Atomistic Simulations. J Chem Theory Comput. 2015; 11(3):1347. PMC: 4675652. DOI: 10.1021/acs.jctc.5b00154. View