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Title: High-throughput binding affinity calculations at extreme scales

Journal Article · · BMC Bioinformatics
 [1];  [2];  [3];  [3];  [2];  [3];  [3];  [4]
  1. Rutgers Univ., Piscataway, NJ (United States). Dept. of Electrical and Computer Engineering; DOE/OSTI
  2. Rutgers Univ., Piscataway, NJ (United States). Dept. of Electrical and Computer Engineering
  3. Univ. College London (United Kingdom). Centre for Computational Sciences
  4. Rutgers Univ., Piscataway, NJ (United States). Dept. of Electrical and Computer Engineering; Stony Brook Univ., NY (United States). Inst. for Advanced Computational Sciences; Brookhaven National Lab. (BNL), Upton, NY (United States). Computational Science Inst.
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Background Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. Results We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. Conclusions As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
SC0012704
OSTI ID:
1626773
Journal Information:
BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: S18 Vol. 19; ISSN 1471-2105
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English

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