Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Accelerating Prediction of Chemical Shift of Protein Structures on GPUs

Eric Wright, Mauricio Ferrato, Alex Bryer, Robert Searles, Juan Perilla, Sunita Chandrasekaran
doi: https://doi.org/10.1101/2020.01.12.903468
Eric Wright
1Dept. of CIS, UDEL, Newark, DE, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mauricio Ferrato
1Dept. of CIS, UDEL, Newark, DE, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alex Bryer
2Dept of Chemistry, UDEL, Newark, DE, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert Searles
3NVIDIA, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Juan Perilla
2Dept of Chemistry, UDEL, Newark, DE, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sunita Chandrasekaran
1Dept. of CIS, UDEL, Newark, DE, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: schandra@udel.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Experimental chemical shifts (CS) from solution and solid state magic-angle-spinning nuclear magnetic resonance 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 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 the code refactoring and acceleration brought down the time taken of the software running on an NVIDIA V100 GPU to 46.71 seconds for our largest dataset of 11.3M 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.

Footnotes

  • https://github.com/UD-CRPL/ppmone

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 14, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Accelerating Prediction of Chemical Shift of Protein Structures on GPUs
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Accelerating Prediction of Chemical Shift of Protein Structures on GPUs
Eric Wright, Mauricio Ferrato, Alex Bryer, Robert Searles, Juan Perilla, Sunita Chandrasekaran
bioRxiv 2020.01.12.903468; doi: https://doi.org/10.1101/2020.01.12.903468
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Accelerating Prediction of Chemical Shift of Protein Structures on GPUs
Eric Wright, Mauricio Ferrato, Alex Bryer, Robert Searles, Juan Perilla, Sunita Chandrasekaran
bioRxiv 2020.01.12.903468; doi: https://doi.org/10.1101/2020.01.12.903468

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biophysics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3477)
  • Biochemistry (7315)
  • Bioengineering (5290)
  • Bioinformatics (20180)
  • Biophysics (9967)
  • Cancer Biology (7696)
  • Cell Biology (11242)
  • Clinical Trials (138)
  • Developmental Biology (6413)
  • Ecology (9910)
  • Epidemiology (2065)
  • Evolutionary Biology (13266)
  • Genetics (9346)
  • Genomics (12542)
  • Immunology (7665)
  • Microbiology (18919)
  • Molecular Biology (7413)
  • Neuroscience (40853)
  • Paleontology (298)
  • Pathology (1224)
  • Pharmacology and Toxicology (2124)
  • Physiology (3137)
  • Plant Biology (6833)
  • Scientific Communication and Education (1268)
  • Synthetic Biology (1890)
  • Systems Biology (5295)
  • Zoology (1083)