We report progress in GPU-accelerated molecular dynamics and free energy methods in Amber18. Of particular interest is the development of alchemical free energy algorithms, including free energy perturbation and thermodynamic integration methods with support for non-linear softcore potential and parameter interpolation transformation pathways. These methods can be used in conjunction with enhanced sampling techniques such as replica exchange, constant pH molecular dynamics and new 12-6-4 potentials for metal ions. Additional performance enhancements have been made that enable appreciable speed-up on GPUs relative to the previous software release.

Predicting protein–ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

We use the PBE0/6-31G* density functional method to perform ab initio quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations under periodic boundary conditions with rigorous electrostatics using the ambient potential composite Ewald method in order to test the convergence of MM→QM/MM free energy corrections for the prediction of 17 small-molecule solvation free energies and 8 ligand binding free energies to T4 lysozyme. The “indirect” thermodynamic cycle for calculating free energies is used to explore whether a series of reference potentials improve the statistical quality of the predictions. Specifically, we construct a series of reference potentials that optimizes a molecular mechanical (MM) force field’s parameters to reproduce the ab initio QM/MM forces from a QM/MM simulation. The optimizations form a systematic progression of successively expanded parameters that include bond, angle, dihedral and charge parameters. For each reference potential, we calculate benchmark quality reference values for the MM→QM/MM correction by performing the mixed MM and QM/MM Hamiltonians at 11 intermediate states, each for 200 ps. We then compare forward and reverse application of Zwanzig’s relation, thermodynamic integration, and Bennett’s acceptance ratio (BAR) methods as a function of reference potential, simulation time, and the number of simulated intermediate states. We find that Zwanzig’s equation is inadequate unless a large number of intermediate states are explicitly simulated. The TI and BAR mean signed errors are very small even when only the end-state simulations are considered, and the standard deviation of the TI and BAR errors are decreased by choosing a reference potential that optimizes the bond and angle parameters. We find a robust approach for the data sets of fairly rigid molecules considered here is to use bond+angle reference potential together with the end-state-only BAR analysis. This requires a QM/MM simulations to be performed in order to generate reference data to parameterize the bond+angle reference potential, and then this same simulation serves a dual purpose as the full QM/MM end-state. The convergence of the results with respect to time suggests that computational resources may be used more efficiently by running multiple simulations for no more than 50 ps, rather than running one long simulation.

Neglect of diatomic differential overlap (NDDO) and self-consistent density-functional tight-binding (SCC-DFTB) semiempirical models commonly employed in combined quantum mechanical/molecular mechanical simulations fail to adequately describe the deoxyribose and ribose sugar ring puckers. This failure limits the application of these methods to RNA and DNA systems. In this work, we provide benchmark ab initio gas-phase two-dimensional potential energy scans of the RNA and DNA sugar puckering. The benchmark calculations are compared with semiempirical models. Pucker corrections are introduced into the semiempirical models via B-spline interpolation of the potential energy difference surface relative to the benchmark data. The corrected semiempirical models are shown to well reproduce the ab initio puckering profiles. Furthermore, we demonstrate that the uncorrected semiempirical models do not usually produce a transition state between the A-form and B-form sugar puckers, but the ab initio transition state is reproduced when the B-spline correction is used.

The present work outlines a new method for treatment of charge-dependent polarizability in semiempirical quantum models for use in combined quantum-mechanical/molecular mechanical simulations of biological reactions. The method addresses a major shortcoming in the performance of conventional semiempirical models for these simulations that is tied to the use of a localized minimal atomic-orbital basis set. The present approach has the advantages that it uses a density basis that retains a set of linear-response equations, does not increase the atomic-orbital basis, and avoids the problem of artificial charge transfer and scaling of the polarizability seen in related models that allow atomic charges to fluctuate. The model introduces four new atom-based parameters and has been tested with the modified neglect of differential overlap d-orbital Hamiltonian against 1132 molecules and ions and shown to decrease the dipole moment and polarizability errors by factors of 2 and 10, respectively, with respect to density-functional results. The method performs impressively for a variety of charge states (from 2+ to 2-), and offers a potentially powerful extension in the design of next generation semiempirical quantum models for accurate simulations of highly charged biological reactions.