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PyRod: Tracing Water Molecules in Molecular Dynamics Simulations

Cite this: J. Chem. Inf. Model. 2019, 59, 6, 2818–2829
Publication Date (Web):May 22, 2019
https://doi.org/10.1021/acs.jcim.9b00281
Copyright © 2019 American Chemical Society

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    Abstract

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    Ligands entering a protein binding pocket essentially compete with water molecules for binding to the protein. Hence, the location and thermodynamic properties of water molecules in protein structures have gained increased attention in the drug design community. Including corresponding data into 3D pharmacophore modeling is essential for efficient high throughput virtual screening. Here, we present PyRod, a free and open-source Python software that allows for visualization of pharmacophoric binding pocket characteristics, identification of hot spots for ligand binding, and subsequent generation of pharmacophore features for virtual screening. The implemented routines analyze the protein environment of water molecules in molecular dynamics (MD) simulations and can differentiate between hydrogen bonded waters as well as waters in a protein environment of hydrophobic, charged, or aromatic atom groups. The gathered information is further processed to generate dynamic molecular interaction fields (dMIFs) for visualization and pharmacophoric features for virtual screening. The described software was applied to 5 therapeutically relevant drug targets, and generated pharmacophores were evaluated using DUD-E benchmarking sets. The best performing pharmacophore was found for the HIV1 protease with an early enrichment factor of 54.6. PyRod adds a new perspective to structure-based screening campaigns by providing easy-to-interpret dMIFs and purely protein-based pharmacophores that are solely based on tracing water molecules in MD simulations. Since structural information about cocrystallized ligands is not needed, screening campaigns can be followed, for which less or no ligand information is available. PyRod is freely available at https://github.com/schallerdavid/pyrod.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.9b00281.

    • Tables for feature definitions, scoring functions, combinatorial library parameters, and early enrichment factor comparison with DUD-E benchmark (PDF)

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    Cited By

    This article is cited by 13 publications.

    1. Somaya A. Abdel-Rahman, Valerij Talagayev, Szymon Pach, Gerhard Wolber, Moustafa T. Gabr. Discovery of Small-Molecule TIM-3 Inhibitors for Acute Myeloid Leukemia Using Pharmacophore-Based Virtual Screening. Journal of Medicinal Chemistry 2023, 66 (16) , 11464-11475. https://doi.org/10.1021/acs.jmedchem.3c00960
    2. Haoqi Wang, Nirmitee Mulgaonkar, Lisa M. Pérez, Sandun Fernando. ELIXIR-A: An Interactive Visualization Tool for Multi-Target Pharmacophore Refinement. ACS Omega 2022, 7 (15) , 12707-12715. https://doi.org/10.1021/acsomega.1c07144
    3. Szymon Pach, Tim M. Sarter, Rafe Yousef, David Schaller, Silke Bergemann, Christoph Arkona, Jörg Rademann, Christoph Nitsche, Gerhard Wolber. Catching a Moving Target: Comparative Modeling of Flaviviral NS2B-NS3 Reveals Small Molecule Zika Protease Inhibitors. ACS Medicinal Chemistry Letters 2020, 11 (4) , 514-520. https://doi.org/10.1021/acsmedchemlett.9b00629
    4. Davide Bassani, Stefano Moro. In Silico Insights Toward the Exploration of Adenosine Receptors Ligand Recognition. 2023https://doi.org/10.1007/7355_2023_164
    5. Qingbo Xu, Zhixiang Zhao, Peibo Liang, Simin Wang, Fang Li, Shuhui Jin, Jianjun Zhang. Identification of novel nematode succinate dehydrogenase inhibitors: Virtual screening based on ligand‐pocket interactions. Chemical Biology & Drug Design 2023, 101 (1) , 9-23. https://doi.org/10.1111/cbdd.14019
    6. Theresa Noonan, Katrin Denzinger, Valerij Talagayev, Yu Chen, Kristina Puls, Clemens Alexander Wolf, Sijie Liu, Trung Ngoc Nguyen, Gerhard Wolber. Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals 2022, 15 (11) , 1304. https://doi.org/10.3390/ph15111304
    7. Rashmi Tyagi, Amisha Singh, Kamal Kumar Chaudhary, Manoj Kumar Yadav. Pharmacophore modeling and its applications. 2022, 269-289. https://doi.org/10.1016/B978-0-323-89775-4.00009-2
    8. Matej Janežič, Katja Valjavec, Kaja Bergant Loboda, Barbara Herlah, Iza Ogris, Mirijam Kozorog, Marjetka Podobnik, Simona Golič Grdadolnik, Gerhard Wolber, Andrej Perdih. Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα. International Journal of Molecular Sciences 2021, 22 (24) , 13474. https://doi.org/10.3390/ijms222413474
    9. David Machalz, Szymon Pach, Marcel Bermudez, Matthias Bureik, Gerhard Wolber. Structural insights into understudied human cytochrome P450 enzymes. Drug Discovery Today 2021, 26 (10) , 2456-2464. https://doi.org/10.1016/j.drudis.2021.06.006
    10. Vladimir B. Sulimov, Danil C. Kutov, Anna S. Taschilova, Ivan S. Ilin, Eugene E. Tyrtyshnikov, Alexey V. Sulimov. Docking Paradigm in Drug Design. Current Topics in Medicinal Chemistry 2021, 21 (6) , 507-546. https://doi.org/10.2174/1568026620666201207095626
    11. David Schaller, Szymon Pach, Marcel Bermudez, Gerhard Wolber. Exploiting Water Dynamics for Pharmacophore Screening. 2021, 227-238. https://doi.org/10.1007/978-1-0716-1209-5_13
    12. David Schaller, Dora Šribar, Theresa Noonan, Lihua Deng, Trung Ngoc Nguyen, Szymon Pach, David Machalz, Marcel Bermudez, Gerhard Wolber. Next generation 3D pharmacophore modeling. WIREs Computational Molecular Science 2020, 10 (4) https://doi.org/10.1002/wcms.1468
    13. David Schaller, Gerhard Wolber. PyRod Enables Rational Homology Model‐based Virtual Screening Against MCHR1. Molecular Informatics 2020, 39 (6) https://doi.org/10.1002/minf.202000020

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