Accelerating prediction of chemical shift of protein structures on GPUs: Using OpenACC

Abstract

Experimental chemical shifts (CS) from solution and solid state magic-angle-spinning nuclear magnetic resonance (NMR) spectra provide atomic level information for each amino acid within a protein or protein complex. However, structure determination of large complexes and assemblies based on NMR data alone remains challenging due to the complexity of the calculations. Here, we present a hardware accelerated strategy for the estimation of NMR chemical-shifts of large macromolecular complexes based on the previously published PPM_One software. The original code was not viable for computing large complexes, with our largest dataset taking approximately 14 hours to complete. Our results show that serial code refactoring and parallel acceleration brought down the time taken of the software running on an NVIDIA Volta 100 (V100) Graphic Processing Unit (GPU) to 46.71 seconds for our largest dataset of 11.3 million atoms. We use OpenACC, a directive-based programming model for porting the application to a heterogeneous system consisting of x86 processors and NVIDIA GPUs. Finally, we demonstrate the feasibility of our approach in systems of increasing complexity ranging from 100K to 11.3M atoms.

A novel acceleration method for molecular dynamics of crystal silicon on GPUs using OpenACC

“…21 Directives strive to offer portability without losing performance and are one of the most portable and productive programming models. 22 Ope-nACC 23 is a directive-based programming model for acceleration devices, which has been widely supported by the industry. At present, there are researches on using OpenACC for application optimization on GPUs.…”

“…Ami Marowka points out that the 3P challenges of high‐performance programming—performance, portability, and productivity—have become more difficult than ever in the era of heterogeneous computing 21 . Directives strive to offer portability without losing performance and are one of the most portable and productive programming models 22 . OpenACC 23 is a directive‐based programming model for acceleration devices, which has been widely supported by the industry.…”

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

“…Moving these operation on the GPU could further reduce the need for costly memory transfers, allowing all computations to "reside" on the device for 10 computational diffusion steps (with size ∆t diff ∼ 0.01 min) without need for memory transfer. Second, cell mechanics operations are governed by biased random migration and interaction potentials, which are well-suited to GPU computations [43]. Furthermore, there are 60 mechanics steps (with step size ∆tmech ∼ 0.1 min) for every cell step (with size ∆t cell ∼ 6 min).…”

EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

“…GNNs have also been applied to various protein‐related tasks, such as protein‐ligand binding prediction (Jiang et al, 2022 ), protein contact prediction (Jha et al, 2022 ), and mutations on protein stability (Wang et al, 2023 ). Large complexes' structure determination based on experimental NMR data is challenging because of the complexity of calculations, inspiring Wright et al ( 2020 ) to present a GPU‐accelerated approach to estimate and calculate chemical shift prediction named PPM_One. Han et al ( 2022 ) proposed a scalable GNN sparsifying the graph representation of molecules by only considering heavy atoms as nodes and the relevant chemical bonds as edges.…”