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tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking

  • Darren J. Hsu
    Darren J. Hsu
    National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
  • Russell B. Davidson
    Russell B. Davidson
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
  • Ada Sedova
    Ada Sedova
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
    More by Ada Sedova
  • , and 
  • Jens Glaser*
    Jens Glaser
    National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
    *E-mail: [email protected]
    More by Jens Glaser
Cite this: J. Chem. Inf. Model. 2023, 63, 11, 3438–3447
Publication Date (Web):May 19, 2023
https://doi.org/10.1021/acs.jcim.2c01530
Copyright © 2023 American Chemical Society

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    Abstract

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    A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. We present the development of a high-throughput and flexible ligand pose refinement workflow, called “tinyIFD”. The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (Mpro) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.2c01530.

    • (1) List of software and versions used in this paper, (2) detailed explanation of features extracted from MD snapshots for the classifiers, (3) list of cross-docking cases used in the model zoo, (4) list of cross-docking cases in the training set but excluded from the model zoo, (5) test set refinement results, and (6) list of PDB entries used for the Mpro data set (PDF)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 4 publications.

    1. Susanta Das, Kenneth M. Merz, Jr.. Molecular Gas-Phase Conformational Ensembles. Journal of Chemical Information and Modeling 2024, 64 (3) , 749-760. https://doi.org/10.1021/acs.jcim.3c01309
    2. David A. Case, Hasan Metin Aktulga, Kellon Belfon, David S. Cerutti, G. Andrés Cisneros, Vinícius Wilian D. Cruzeiro, Negin Forouzesh, Timothy J. Giese, Andreas W. Götz, Holger Gohlke, Saeed Izadi, Koushik Kasavajhala, Mehmet C. Kaymak, Edward King, Tom Kurtzman, Tai-Sung Lee, Pengfei Li, Jian Liu, Tyler Luchko, Ray Luo, Madushanka Manathunga, Matias R. Machado, Hai Minh Nguyen, Kurt A. O’Hearn, Alexey V. Onufriev, Feng Pan, Sergio Pantano, Ruxi Qi, Ali Rahnamoun, Ali Risheh, Stephan Schott-Verdugo, Akhil Shajan, Jason Swails, Junmei Wang, Haixin Wei, Xiongwu Wu, Yongxian Wu, Shi Zhang, Shiji Zhao, Qiang Zhu, Thomas E. Cheatham, III, Daniel R. Roe, Adrian Roitberg, Carlos Simmerling, Darrin M. York, Maria C. Nagan, Kenneth M. Merz, Jr.. AmberTools. Journal of Chemical Information and Modeling 2023, 63 (20) , 6183-6191. https://doi.org/10.1021/acs.jcim.3c01153
    3. Hugo Guterres, Wonpil Im. CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein–Ligand Binding Modes. Journal of Chemical Information and Modeling 2023, 63 (15) , 4772-4779. https://doi.org/10.1021/acs.jcim.3c00416
    4. Ryunosuke Yoshino, Nobuaki Yasuo, Yohsuke Hagiwara, Takashi Ishida, Daniel Ken Inaoka, Yasushi Amano, Yukihiro Tateishi, Kazuki Ohno, Ichiji Namatame, Tatsuya Niimi, Masaya Orita, Kiyoshi Kita, Yutaka Akiyama, Masakazu Sekijima. Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography. ACS Omega 2023, 8 (29) , 25850-25860. https://doi.org/10.1021/acsomega.3c01314

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