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Benchmarking ensemble docking methods in D3R Grand Challenge 4

Gan, Jessie Low and Kumar, Dhruv and Chen, Cynthia and Taylor, Bryn C. and Jagger, Benjamin R. and Amaro, Rommie E. and Lee, Christopher T. (2022) Benchmarking ensemble docking methods in D3R Grand Challenge 4. Journal of Computer-Aided Molecular Design, 36 (2). pp. 87-99. ISSN 0920-654X. PMCID PMC8907095. doi:10.1007/s10822-021-00433-2. https://resolver.caltech.edu/CaltechAUTHORS:20201006-105653492

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Abstract

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/s10822-021-00433-2DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907095PubMed CentralArticle
https://doi.org/10.1101/2020.10.02.324343DOIDiscussion Paper
https://github.com/ctlee/bccgc4Related ItemData/Code
ORCID:
AuthorORCID
Gan, Jessie Low0000-0002-9053-2064
Kumar, Dhruv0000-0002-0791-0977
Chen, Cynthia0000-0003-4604-2241
Taylor, Bryn C.0000-0002-4957-5417
Jagger, Benjamin R.0000-0003-2958-3695
Amaro, Rommie E.0000-0002-9275-9553
Lee, Christopher T.0000-0002-0670-2308
Alternate Title:Benchmarking ensemble docking methods as a scientific outreach project
Additional Information:© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Received: 28 January 2021 / Accepted: 16 November 2021. This work is supported by the National Biomedical Computation Resource NIH Grant P41-GM103426, and the National Science Foundation through The Extreme Science and Engineering Discovery Environment (XSEDE) supercomputing resources provided via Award TG-CHE060073 to R.E.A. C.T.L. is funded by a Hartwell Foundation Postdoctoral Fellowship. We thank D3R and the organizers of Grand Challenge 4 for hosting the challenge and reporting results. We would also like to acknowledge Maven V. Holst, Gaurie Gunasekaran, Gray Thoron, and Jeffery R. Wagner for their contributions to preliminary work and/or helpful discussions. Contributions: Conceptualization, BCT, BRJ, CTL and REA; Software, JLG, DK, and CC; Investigation, JLG, DK, and CC; Resources, BCT, BRJ, CTL and REA; Writing—Original Draft, JLG, and DK; Writing—Review and Editing, JLG, DK, CC, BCT, BRJ, CTL and REA; Visualization, JLG, DK; Supervision and Project Administration, BCT, BRJ, CTL and REA; Funding Acquisition, REA. The authors declare that they have no conflict of interest.
Funders:
Funding AgencyGrant Number
NIHP41-GM103426
NSFTG-CHE060073
Hartwell FoundationUNSPECIFIED
Subject Keywords:Computational biophysics; Ensemble docking; Molecular dynamics; Drug discovery; Restrained docking
Issue or Number:2
PubMed Central ID:PMC8907095
DOI:10.1007/s10822-021-00433-2
Record Number:CaltechAUTHORS:20201006-105653492
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201006-105653492
Official Citation:Gan, J.L., Kumar, D., Chen, C. et al. Benchmarking ensemble docking methods in D3R Grand Challenge 4. J Comput Aided Mol Des 36, 87–99 (2022). https://doi.org/10.1007/s10822-021-00433-2
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105837
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Oct 2020 00:21
Last Modified:22 Mar 2022 18:49

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