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An Accurate Metalloprotein-Specific Scoring Function and Molecular Docking Program Devised by a Dynamic Sampling and Iteration Optimization Strategy

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Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning 116023, China
Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
§ State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
*Address: School of Pharmacy, East China University of Science and Technology 130 Mei Long Road, Shanghai 200237, China. Phone: +86-21-64250213. Fax: +86-21-64250213. E-mail: [email protected]
Cite this: J. Chem. Inf. Model. 2015, 55, 4, 833–847
Publication Date (Web):March 6, 2015
https://doi.org/10.1021/ci500647f
Copyright © 2015 American Chemical Society

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    Abstract

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    Metalloproteins, particularly zinc metalloproteins, are promising therapeutic targets, and recent efforts have focused on the identification of potent and selective inhibitors of these proteins. However, the ability of current drug discovery and design technologies, such as molecular docking and molecular dynamics simulations, to probe metal–ligand interactions remains limited because of their complicated coordination geometries and rough treatment in current force fields. Herein we introduce a robust, multiobjective optimization algorithm-driven metalloprotein-specific docking program named MpSDock, which runs on a scheme similar to consensus scoring consisting of a force-field-based scoring function and a knowledge-based scoring function. For this purpose, in this study, an effective knowledge-based zinc metalloprotein-specific scoring function based on the inverse Boltzmann law was designed and optimized using a dynamic sampling and iteration optimization strategy. This optimization strategy can dynamically sample and regenerate decoy poses used in each iteration step of refining the scoring function, thus dramatically improving both the effectiveness of the exploration of the binding conformational space and the sensitivity of the ranking of the native binding poses. To validate the zinc metalloprotein-specific scoring function and its special built-in docking program, denoted MpSDockZn, an extensive comparison was performed against six universal, popular docking programs: Glide XP mode, Glide SP mode, Gold, AutoDock, AutoDock4Zn, and EADock DSS. The zinc metalloprotein-specific knowledge-based scoring function exhibited prominent performance in accurately describing the geometries and interactions of the coordination bonds between the zinc ions and chelating agents of the ligands. In addition, MpSDockZn had a competitive ability to sample and identify native binding poses with a higher success rate than the other six docking programs.

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    All of the PDB information on the training sets and test sets used in this study and the detailed docking results of MpSDockZn on the test set of 106 zinc metalloproteins. This material is available free of charge via the Internet at http://pubs.acs.org.

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    Cited By

    This article is cited by 12 publications.

    1. Camila M. Clemente, Juan M. Prieto, Marcelo Martí. Unlocking Precision Docking for Metalloproteins. Journal of Chemical Information and Modeling 2024, Article ASAP.
    2. Süleyman Selim Çınaroğlu, Emel Timuçin. Comparative Assessment of Seven Docking Programs on a Nonredundant Metalloprotein Subset of the PDBbind Refined. Journal of Chemical Information and Modeling 2019, 59 (9) , 3846-3859. https://doi.org/10.1021/acs.jcim.9b00346
    3. Damodara N. Reddy, Flavio Ballante, Timothy Chuang, Adele Pirolli, Biagina Marrocco, and Garland R. Marshall . Design and Synthesis of Simplified Largazole Analogues as Isoform-Selective Human Lysine Deacetylase Inhibitors. Journal of Medicinal Chemistry 2016, 59 (4) , 1613-1633. https://doi.org/10.1021/acs.jmedchem.5b01632
    4. Flavio Ballante and Garland R. Marshall . An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design. Journal of Chemical Information and Modeling 2016, 56 (1) , 54-72. https://doi.org/10.1021/acs.jcim.5b00603
    5. Kerlen T. Korbeld, Maximilian J. L. J. Fürst. Curse and Blessing of Non‐Proteinogenic Parts in Computational Enzyme Engineering. ChemBioChem 2023, 24 (12) https://doi.org/10.1002/cbic.202300192
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    7. Kai Wang. GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability. Briefings in Bioinformatics 2023, 24 (1) https://doi.org/10.1093/bib/bbac620
    8. Giulio Poli, Claudiu T. Supuran, Tiziano Tuccinardi. Carbonic Anhydrase Inhibitors: Identifying Therapeutic Cancer Agents Through Virtual Screening. 2021, 237-252. https://doi.org/10.1007/978-3-030-79511-5_11
    9. Kai Wang, Nan Lyu, Hongjuan Diao, Shujuan Jin, Tao Zeng, Yaoqi Zhou, Ruibo Wu, . GM-DockZn: a geometry matching-based docking algorithm for zinc proteins. Bioinformatics 2020, 36 (13) , 4004-4011. https://doi.org/10.1093/bioinformatics/btaa292
    10. Deliang Chen, Yibao Li, Wei Guo, Yongdong Li, Tor Savidge, Xun Li, Xiaolin Fan. The shielding effect of metal complexes on the binding affinities of ligands to metalloproteins. Physical Chemistry Chemical Physics 2019, 21 (1) , 205-216. https://doi.org/10.1039/C8CP06555A
    11. Wei Xiao, Disha Wang, Zihao Shen, Shiliang Li, Honglin Li. Multi-Body Interactions in Molecular Docking Program Devised with Key Water Molecules in Protein Binding Sites. Molecules 2018, 23 (9) , 2321. https://doi.org/10.3390/molecules23092321
    12. Laura Riccardi, Vito Genna, Marco De Vivo. Metal–ligand interactions in drug design. Nature Reviews Chemistry 2018, 2 (7) , 100-112. https://doi.org/10.1038/s41570-018-0018-6

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