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

• Published in: PLoS computational biology Vol. 16; no. 5; p. e1007877
• Main Authors: Wright, Eric, Ferrato, Mauricio H, Bryer, Alexander J, Searles, Robert, Perilla, Juan R, Chandrasekaran, Sunita
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-05-2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Amino acids; Applications software; Biochemistry; Biology and Life Sciences; Chemical equilibrium; Chemistry; Complexity; Computer and Information Sciences; Computer programming; Computer programs; Datasets; Graphics processing units; Information science; Macromolecules; Medicine and Health Sciences; Methods; NMR; Nuclear magnetic resonance; Observations; Optimization; Physical Sciences; Protein research; Protein structure; Protein structure prediction; Proteins; Research and Analysis Methods; Software; United States; Delaware; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007877

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.