Alchemical free energy calculations (AFE) using molecular dynamics (MD) simulations are excellent tools to not only enable our understanding of a wide variety of biological processes, but also as part of the pharmaceutical lead discovery process. However, computing power and theory have long been insufficient. One of the major difficulties in performing AFE calculations is the length of time required for calculations to converge to an ensemble average. CPU implementations of MD based free energy algorithms have only been capable of reaching on the order of tens of nanoseconds per day for systems on the order of 50,000 atoms, even running on massively parallel supercomputers. Therefore, free energy calculations of long time scale events with AMBER have generally been untenable at scale, preventing researchers from gaining crucial insight into molecular recognition, potential druggability, and other crucial areas of interest. Graphics cards (GPUs) can change this. We present here a GPU implementation of thermodynamic integration capable of reaching speeds of 120 ns/day for a 45,000 atom system, with accuracy and precision identical to the existing CPU implementation in AMBER.<br/><br/>While speed is the key component missing from the common use of alchemical free energy, biological and chemical accuracy is missing in certain respects. pH, a well-known moderator of enzymatic activity, is often neglected as a thermodynamic variable in molecular dynamics simulations. However, work in the McCammon lab has demonstrated that failure to account for protonation state can result in errors of up to 8 kcal/mol. This is significant when considering that the expected free energy of binding of a lead compound before optimization (when AFE would be applied) is only -12 kcal/mol. The primary reasons for the neglect of pH in molecular dynamics simulations are 1) computational cost, and 2) the lack of a theoretical approach with which to calculate pH dependent perturbations. Recently, such a theoretical method has been developed in the McCammon group to enable the calculation of pH perturbations to binding free energies. Simultaneously, a graphics processor version of a new explicit solvent version of CpHMD has been developed in the Walker lab. We have demonstrated this combined method for the high-throughput calculation of the binding free energy of two inhibitors of beta-secretase 1, the primary target in rational drug design in the fight against Alzheimer's disease.

Fast, accurate pH dependent alchemical free energy calculations towards rational drug design

254th National Meeting (2017)

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