Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

A. Acharya; R. Agarwal; M. B. Baker; J. Baudry; D. Bhowmik; S. Boehm; K. G. Byler; S. Y. Chen; L. Coates; C. J. Cooper; O. Demerdash; I. Daidone; J. D. Eblen; S. Ellingson; S. Forli; J. Glaser; J. C. Gumbart; J. Gunnels; O. Hernandez; S. Irle; D. W. Kneller; A. Kovalevsky; J. Larkin; T. J. Lawrence; S. Legrand; S. H. Liu; J. C. Mitchell; G. Park; J. M. Parks; A. Pavlova; L. Petridis; D. Poole; L. Pouchard; A. Ramanathan; D. M. Rogers; D. Santos-Martins; A. Scheinberg; A. Sedova; Y. Shen; J. C. Smith; M. D. Smith; C. Soto; A. Tsaris; M. Thavappiragasam; A. F. Tillack; J. V. Vermaas; V. Q. Vuong; J. Yin; S. Yoo; M. Zahran; L. Zanetti-Polzi

doi:10.1021/acs.jcim.0c01010

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

A. Acharya
, R. Agarwal
, M. B. Baker
, J. Baudry
, D. Bhowmik
, S. Boehm
, K. G. Byler
, S. Y. Chen
, L. Coates
, C. J. Cooper
, O. Demerdash
, I. Daidone
, J. D. Eblen
, S. Ellingson
, S. Forli
, J. Glaser
, J. C. Gumbart
, J. Gunnels
, O. Hernandez
, S. Irle

D. W. Kneller, A. Kovalevsky, J. Larkin, T. J. Lawrence, S. Legrand, S. H. Liu, J. C. Mitchell, G. Park, J. M. Parks, A. Pavlova, L. Petridis, D. Poole, L. Pouchard, A. Ramanathan, D. M. Rogers, D. Santos-Martins, A. Scheinberg, A. Sedova, Y. Shen, J. C. Smith, M. D. Smith, C. Soto, A. Tsaris, M. Thavappiragasam, A. F. Tillack, J. V. Vermaas, V. Q. Vuong, J. Yin, S. Yoo, M. Zahran, L. Zanetti-Polzi

Internal Medicine

Producción científica: Article › revisión exhaustiva

169 Citas (Scopus)

Resumen

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

Idioma original	English
Páginas (desde-hasta)	5832-5852
Número de páginas	21
Publicación	Journal of Chemical Information and Modeling
Volumen	60
N.º	12
DOI	https://doi.org/10.1021/acs.jcim.0c01010
Estado	Published - dic 28 2020

Nota bibliográfica

Publisher Copyright:
© 2020 American Chemical Society. All rights reserved.

Financiación

This work was made possible in part by a grant of high-performance computing resources and technical support from the Alabama Supercomputer Authority to J.B. and K.B. J.C.G. was supported by the National Institute of Health under Grant No. NIH R01-AI148740. C.J.C. was supported by a National Science Foundation Graduate Research Fellowship under Grant No. 2017219379. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 and National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. This research was supported by the Cancer Research Informatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558) and the University of Kentucky’s Center for Computational Sciences (CCS) high-performance computing resources. Computer time on Summit was granted by the HPC Covid-19 Consortium. This work was made possible in part by a grant of high-performance computing resources and technical support from the Alabama Supercomputer Authority to J.B. and K.B. J.C.G. was supported by the National Institute of Health under Grant No. NIH R01-AI148740. C.J.C. was supported by a National Science Foundation Graduate Research Fellowship under Grant No. 2017219379. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 and National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. This research was supported by the Cancer Research Informatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558) and the University of Kentucky's Center for Computational Sciences (CCS) high-performance computing resources. Computer time on Summit was granted by the HPC Covid-19 Consortium.

Financiadores	Número del financiador
National Energy Research Scientific Computing Center
University of Kentucky
National Institutes of Health (NIH)
Office of Science Programs
HPC Covid-19 Consortium
National Science Foundation Arctic Social Science Program	2017219379
University of Kentucky Markey Comprehensive Cancer Center	P30CA177558
U.S. Department of Energy	DE-AC05-00OR22725, DE-AC02-05CH11231
National Institute of Allergy and Infectious F32-AI286447 Cydney N. Johnson Diseases National Institute of Allergy and Infectious R01AI168214 Jason W. Rosch Diseases National Institute of Allergy and Infectious P30 Cydney N. Johnson Diseases National Institute of Allergy and Infectious R00-AI166116 Christopher D. Radka Diseases National Institute of Allergy and Infectious T32-AI106700 Cydney N. Johnson Diseases National Institute of Allergy and Infectious R01AI192221 Jason W. Rosch Diseases National Inst...	R01AI148740
National Childhood Cancer Registry – National Cancer Institute	P30CA177558

ASJC Scopus subject areas

General Chemistry
General Chemical Engineering
Computer Science Applications
Library and Information Sciences

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10.1021/acs.jcim.0c01010

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Acharya, A., Agarwal, R., Baker, M. B., Baudry, J., Bhowmik, D., Boehm, S., Byler, K. G., Chen, S. Y., Coates, L., Cooper, C. J., Demerdash, O., Daidone, I., Eblen, J. D., Ellingson, S., Forli, S., Glaser, J., Gumbart, J. C., Gunnels, J., Hernandez, O., ... Zanetti-Polzi, L. (2020). Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. Journal of Chemical Information and Modeling, 60(12), 5832-5852. https://doi.org/10.1021/acs.jcim.0c01010

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title = "Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19",

abstract = "We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.",

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year = "2020",

month = dec,

day = "28",

doi = "10.1021/acs.jcim.0c01010",

language = "English",

volume = "60",

pages = "5832--5852",

number = "12",

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Acharya, A, Agarwal, R, Baker, MB, Baudry, J, Bhowmik, D, Boehm, S, Byler, KG, Chen, SY, Coates, L, Cooper, CJ, Demerdash, O, Daidone, I, Eblen, JD, Ellingson, S, Forli, S, Glaser, J, Gumbart, JC, Gunnels, J, Hernandez, O, Irle, S, Kneller, DW, Kovalevsky, A, Larkin, J, Lawrence, TJ, Legrand, S, Liu, SH, Mitchell, JC, Park, G, Parks, JM, Pavlova, A, Petridis, L, Poole, D, Pouchard, L, Ramanathan, A, Rogers, DM, Santos-Martins, D, Scheinberg, A, Sedova, A, Shen, Y, Smith, JC, Smith, MD, Soto, C, Tsaris, A, Thavappiragasam, M, Tillack, AF, Vermaas, JV, Vuong, VQ, Yin, J, Yoo, S, Zahran, M & Zanetti-Polzi, L 2020, 'Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19', Journal of Chemical Information and Modeling, vol. 60, n.º 12, pp. 5832-5852. https://doi.org/10.1021/acs.jcim.0c01010

TY - JOUR

T1 - Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

AU - Acharya, A.

AU - Agarwal, R.

AU - Baker, M. B.

AU - Baudry, J.

AU - Bhowmik, D.

AU - Boehm, S.

AU - Byler, K. G.

AU - Chen, S. Y.

AU - Coates, L.

AU - Cooper, C. J.

AU - Demerdash, O.

AU - Daidone, I.

AU - Eblen, J. D.

AU - Ellingson, S.

AU - Forli, S.

AU - Glaser, J.

AU - Gumbart, J. C.

AU - Gunnels, J.

AU - Hernandez, O.

AU - Irle, S.

AU - Kneller, D. W.

AU - Kovalevsky, A.

AU - Larkin, J.

AU - Lawrence, T. J.

AU - Legrand, S.

AU - Liu, S. H.

AU - Mitchell, J. C.

AU - Park, G.

AU - Parks, J. M.

AU - Pavlova, A.

AU - Petridis, L.

AU - Poole, D.

AU - Pouchard, L.

AU - Ramanathan, A.

AU - Rogers, D. M.

AU - Santos-Martins, D.

AU - Scheinberg, A.

AU - Sedova, A.

AU - Shen, Y.

AU - Smith, J. C.

AU - Smith, M. D.

AU - Soto, C.

AU - Tsaris, A.

AU - Thavappiragasam, M.

AU - Tillack, A. F.

AU - Vermaas, J. V.

AU - Vuong, V. Q.

AU - Yin, J.

AU - Yoo, S.

AU - Zahran, M.

AU - Zanetti-Polzi, L.

PY - 2020/12/28

Y1 - 2020/12/28

N2 - We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

AB - We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

UR - https://www.scopus.com/pages/publications/85098796595

UR - https://www.scopus.com/pages/publications/85098796595#tab=citedBy

U2 - 10.1021/acs.jcim.0c01010

DO - 10.1021/acs.jcim.0c01010

M3 - Article

C2 - 33326239

AN - SCOPUS:85098796595

SN - 1549-9596

VL - 60

SP - 5832

EP - 5852

JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

IS - 12

ER -

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

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ASJC Scopus subject areas

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