Scoring ligand/protein interactions using a novel high performance implementation of DFT quantum mechanics

Quantum Simulation Technologies Inc., doing business as Qsimulate, received a $442,362 Project Grant from the National Institutes of Health's National Institute of General Medical Sciences to develop and validate a novel high-performance implementation of density functional theory quantum mechanics for scoring ligand-protein interactions. The award was made under the Biomedical Research and Research Training program (CFDA 93.859), which supports basic research that increases understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention.

Through this six-month project concluding in August 2021, Qsimulate will characterize and validate its unique distributed memory approach for applying high-accuracy quantum mechanics calculations to entire ligand-protein systems using commodity cloud computing resources. The company will utilize a benchmark set of experimentally-measured binding data for various proteins to test and improve its density functional theory method. A portion of the work will focus on identifying optimal approaches for very large protein systems.

Abstract

In the past several years, dramatic advances in available compute resources have finally enabled computational chemistry to significantly impact HIT-to-lead discovery. Most significantly, free energy perturbation (FEP) calculations have demonstrated high value for rank-ordering the binding of multiple ligands to a common protein receptor, thereby allowing acceptably accurate, cost-effective predictions with reasonable turnaround time. Use of this type of approach has been credited for speeding the HIT-to-lead process by a factor of more. (e.g. the computationally-driven nimbus / schrodinger collaboration that yielded an acetyl-COA carboxylase inhibitor clinical candidate in 18 months and that was sold to gilead in a deal worth as much as $1.2 billion). But, while approaches like FEP are demonstrably useful, they are limited in application to certain types of systems. This is because they rely on the use of a classical energy function, which attempts to mimic quantum effects with an approximate form that needs to be parameterized. Ideally, one could, instead, simply use quantum mechanics (QM), and avoid the significant limitations (in domain of applicability and accuracy) that arise from using a fitted force field. However, until very recently, high accuracy quantum mechanics calculations could not be performed on full ligand/protein systems. We have now demonstrated, for the first time, how a unique distributed memory implementation of high accuracy quantum mechanics (density functional theory (DFT)) allows ligand/protein binding to be evaluated entirely in the quantum domain. These calculations can be carried out on commodity hardware (cloud computers), can finish in less than an hour and at a cost of less than roughly $10. Having removed the limitations of the classical force field, these quantum calculations promise to be more broadly applicable, faster, and cheaper than the FEP equivalents. In this grant, we describe a set of efforts that are required to more fully characterize and validate this approach, and to move from the potential energies that are directly determined in quantum mechanics to free energy estimates that are more directly comparable to experimental measurements. We will utilize a benchmark set of consistently-measured experimental binding data for ligand/protein interactions for a variety of proteins to test and improve our DFT method. Part of our work will focus on identifying the optimal approach for very large protein systems. While the entire ligand/protein complex can be treated quantum mechanically with our approach for proteins of size