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

Journal of Chemical Information and Modeling2020Vol. 60(12), pp. 5832–5852

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Atanu Acharya, Rupesh Agarwal, Matthew Baker, Jérôme Baudry, Debsindhu Bhowmik, Stephanie Boehm, Kendall Byler, Sirui Chen, Leighton Coates, Connor J. Cooper, Omar Demerdash, Isabella Daidone, John D. Eblen, Sally R. Ellingson, Stefano Forli, Jens Gläser, James C. Gumbart, John A. Gunnels, Óscar Hernández, Stephan Irle, Daniel W. Kneller, Andrey Kovalevsky, Jeff Larkin, Travis J. Lawrence, Sara LeGrand, Shih‐Hsien Liu, John C. Mitchell, Geonsoo Park, Jerry M. Parks, Anna Pavlova, Loukas Petridis, David Poole, Line Pouchard, Arvind Ramanathan, David Rogers, Diogo Santos‐Martins, Aaron Scheinberg, Ada Sedova, Yue Shen, Jeremy C. Smith, Micholas Dean Smith, Carlos Soto, Aristeidis Tsaris, Mathialakan Thavappiragasam, Andreas F. Tillack, Josh V. Vermaas, Van‐Quan Vuong, Junqi Yin, Shinjae Yoo, Mohamed Zahran, Laura Zanetti‐Polzi

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.

Citations Over TimeTop 1% of 2020 papers

Abstract

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