Semi-empirical chemical shift prediction, where molecular coordinates and empirical data are reduced to backbone and sidechain chemical shifts through input to differentiable functions, offers a unique opportunity to validate and refine protein structure during largescale molecular dynamics (MD) simulation but poses the challenge of optimizing performance to levels comparable to modern GPU-enabled MD engines. Semi empirical methods, can predict backbone and sidechain chemical shifts with competitive accuracy to purely data-driven counterparts while obviating the need for deep network processing and homology assignment. To poise semi empirial approaches for practical application in largescale biomolecular modeling, a newly optimized GPU-accelerated version of PPM_One was developed. Code refactoring and parallel GPU-processing through the directive-based OpenACC API provide a 67-fold speedup between unaccelerated and Nvidia V100 accelerated PPM_One test-cases, comparatively, in a system of 21 million protein atoms. The core functions that comprise the prediction model and compute perturbation due to Hydrogen bonding, magnetic anisotropy and ring currents were profiled at 271x, 322x, and 211x speedups, respectively. Remaining components of the predictive model saw similar individual speedups through profiling. Implementing the newly optimized functional core into NAMD is NMRForces, a global force module that enables users to compute backbone and sidechain chemical shifts at each timestep and force atoms along the prediction model’s gradient with a magnitude proportional to deviance from experimental chemical shift data.

GPU accelerated computation of isotropic chemical shifts offers new dimension of structure refinement in largescale molecular dynamics simulation

2019 Spring National Meeting

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