1
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Al-Ansi AY, Al-Shawesh GH, Ru X, Lin Z. Quantum Mechanics-Based Fast and Reliable Prediction of Binding Pose Structures. J Phys Chem B 2024; 128:6059-6070. [PMID: 38875526 DOI: 10.1021/acs.jpcb.4c02596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
Predicting the binding poses of docking with an accurate estimation of binding energies is highly important but very challenging in computational drug design. A quantum mechanics (QM) calculation-based docking approach considering multiple conformations and orientations of the ligand is introduced here to tackle the problem. This QM docking consists of three steps: generating an ensemble of binding poses with a conventional docking simulation, computing the binding energies with self-consistent charge density functional theory tightly binding with dispersion correction (DFTB-D) to selecting the 10 top binding modes, and optimizing the selected binding mode structures using the ONIOM(DFTB:PM7) technique to determine the binding poses. The ONIOM(DFTB-D:PM6) docking approach is tested on 121 ligand-receptor biocomplexes with the crystal structures obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB). The result shows that the new method is highly satisfactory for the accurate prediction of the binding poses. The new docking method should be beneficial to structure-based drug design.
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Affiliation(s)
- Amar Y Al-Ansi
- Department of Physics, University of Science and Technology of China, Hefei 230026, China
- Department of Physics, Sana'a University, Sana'a, Yemen
- Hangzhou Nain Biotech Co. Ltd., Hangzhou 310015, China
| | - Gamal H Al-Shawesh
- Department of Physics, University of Science and Technology of China, Hefei 230026, China
| | - Xiao Ru
- Department of Physics, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Zijing Lin
- Department of Physics, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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2
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Bayarsaikhan B, Zsidó BZ, Börzsei R, Hetényi C. Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols. Int J Mol Sci 2024; 25:5945. [PMID: 38892133 PMCID: PMC11172440 DOI: 10.3390/ijms25115945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.
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Affiliation(s)
- Bayartsetseg Bayarsaikhan
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Balázs Zoltán Zsidó
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Rita Börzsei
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
- National Laboratory for Drug Research and Development, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
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3
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Goullieux M, Zoete V, Röhrig UF. Two-Step Covalent Docking with Attracting Cavities. J Chem Inf Model 2023; 63:7847-7859. [PMID: 38049143 PMCID: PMC10751798 DOI: 10.1021/acs.jcim.3c01055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 12/06/2023]
Abstract
Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.
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Affiliation(s)
- Mathilde Goullieux
- SIB
Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland
| | - Vincent Zoete
- SIB
Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland
- Department
of Oncology UNIL-CHUV, Lausanne University, Ludwig Institute for Cancer Research
Lausanne Branch, CH-1066 Epalinges, Switzerland
| | - Ute F. Röhrig
- SIB
Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland
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4
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Szél V, Zsidó BZ, Jeszenői N, Hetényi C. Target-ligand binding affinity from single point enthalpy calculation and elemental composition. Phys Chem Chem Phys 2023; 25:31714-31725. [PMID: 37964670 DOI: 10.1039/d3cp04483a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.
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Affiliation(s)
- Viktor Szél
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - Balázs Zoltán Zsidó
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - Norbert Jeszenői
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
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5
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Hasan MN, Ray M, Saha A. Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery. J Phys Chem B 2023; 127:9663-9684. [PMID: 37921534 DOI: 10.1021/acs.jpcb.3c04710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.
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Affiliation(s)
- Md Nazmul Hasan
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
| | - Manisha Ray
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Arjun Saha
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
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6
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Wei Y, Zhao M, He T, Chen N, Rao L, Chen L, Zhang Y, Yang Y, Yuan Q. Quantitatively Lighting up the Spatial Organization of CD47/SIRPα Immune Checkpoints on the Cellular Membrane with Single-Molecule Localization Microscopy. ACS NANO 2023; 17:21626-21638. [PMID: 37878521 DOI: 10.1021/acsnano.3c06709] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Immunotherapy including immune checkpoint inhibition has reinvigorated the current cancer treatment field. The development of efficient cancer immunotherapies depends on a thorough understanding of the status of immune checkpoints and how they interact. However, the distribution and spatial organization changes of immune checkpoints during their interactions at the single-molecule level remain difficult to directly visualize due to the lack of in situ imaging techniques with appropriate spatial and stoichiometric resolution. Herein, we report the direct visualization and quantification of the spatial distribution and organization of CD47 on the bladder tumor cell membrane and SIRPα on the macrophage membrane by using a single-molecule localization imaging technique called quantitative direct stochastic optical reconstruction microscopy (QdSTORM). Results showed that a portion of CD47 and SIRPα was present on cell membranes as heterogeneous clusters of varying sizes and densities prior to activation. Quantitative analyses of the reconstructed super-resolution images and theoretical simulation revealed that CD47 and SIRPα were reorganized into larger clusters upon binding to each other. Furthermore, we found that blocking the immune checkpoint interaction with small-molecule inhibitors or antibodies significantly impacted the spatial clustering behavior of CD47 on bladder tumor cells, demonstrating the promise of our QdSTORM strategy in elucidating the molecular mechanisms underlying immunotherapy. This work offers a promising strategy to advance our understanding of immune checkpoint state and interactions while also contributing to the fields including signal regulation and cancer therapy.
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Affiliation(s)
- Yurong Wei
- College of Chemistry and Molecular Sciences, Key Laboratory of Biomedical Polymers of Ministry of Education, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, School of Microelectronics, Wuhan University, Wuhan 430072, P. R. China
| | - Min Zhao
- College of Chemistry and Molecular Sciences, Key Laboratory of Biomedical Polymers of Ministry of Education, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, School of Microelectronics, Wuhan University, Wuhan 430072, P. R. China
| | - Tianpei He
- College of Chemistry and Molecular Sciences, Key Laboratory of Biomedical Polymers of Ministry of Education, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, School of Microelectronics, Wuhan University, Wuhan 430072, P. R. China
| | - Na Chen
- College of Chemistry and Molecular Sciences, Key Laboratory of Biomedical Polymers of Ministry of Education, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, School of Microelectronics, Wuhan University, Wuhan 430072, P. R. China
| | - Li Rao
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Long Chen
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China
| | - Yun Zhang
- CAS Key Laboratory of Design and Assembly of Functional Nanostructures, and Fujian Provincial Key Laboratory of Nanomaterials, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350025, P. R. China
| | - Yanbing Yang
- College of Chemistry and Molecular Sciences, Key Laboratory of Biomedical Polymers of Ministry of Education, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, School of Microelectronics, Wuhan University, Wuhan 430072, P. R. China
| | - Quan Yuan
- College of Chemistry and Molecular Sciences, Key Laboratory of Biomedical Polymers of Ministry of Education, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, School of Microelectronics, Wuhan University, Wuhan 430072, P. R. China
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7
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Huwaimel B, Abouzied AS, Anwar S, Elaasser MM, Almahmoud SA, Alshammari B, Alrdaian D, Alshammari RQ. Novel landmarks on the journey from natural products to pharmaceutical formulations: Phytochemical, biological, toxicological and computational activities of Satureja hortensis L. Food Chem Toxicol 2023; 179:113969. [PMID: 37517548 DOI: 10.1016/j.fct.2023.113969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/01/2023]
Abstract
This study examined the ethanolic extract of the Satureja hortensis L. plant's aerial parts to describe its phytochemical makeup, biological functions, toxicity tests, and in-silico molecular docking tests. The GC-MS analysis was used to evaluate the phytochemical composition of the tested extract, and the ABTS and hydrogen peroxide antioxidant assays were used to measure antioxidant activity. Aspergillus fumigatus, Candida albicans, Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Proteus vulgaris were tested for antimicrobial potential. On cell lines such as HepG-2, MCF-7, A-549, and Panc-1, the in-vitro toxicity was also examined. The A-549 cell line was also used for flow cytometry analysis of apoptosis and cell cycle. Additionally, the compounds discovered by the GC-MS analysis were used in silico tests against biological targets. Eight different phytocompounds were tentatively identified as a result of the GC-MS analysis. The compounds also demonstrated significant antioxidant potential for the ABTS and H2O2 assays (IC50: 2.44 and 28.04 μg/ml, respectively). The tested extract was found to have a range of inhibition zones and to be significantly active against the tested bacterial and fungal strains. Apoptosis and cell cycle analysis for the A-549 cell line showed that the cell cycle was arrested at S-phase, and the extract was also found to be most active against this cell line with an IC50 value of 113.05 μg/ml. The docking studies have emphasized the compounds' interactions and binding scores with the EGFR-TK target as determined by the GC-MS.
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Affiliation(s)
- Bader Huwaimel
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail, 81442, Saudi Arabia; Medical and Diagnostic Research Center, University of Ha'il, Hail, 55473, Saudi Arabia
| | - Amr S Abouzied
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail, 81442, Saudi Arabia; Department of Pharmaceutical Chemistry, National Organization for Drug Control and Research (NODCAR), Giza, 12311, Egypt.
| | - Sirajudheen Anwar
- Department of Pharmacology & Toxicology, College of Pharmacy, University of Hail, Hail, Saudi Arabia
| | - Mahmoud M Elaasser
- The Regional Center for Mycology and Biotechnology, Al-Azhar University, Egypt
| | - Suliman A Almahmoud
- Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, Qassim University, Buraidah, 51452, Saudi Arabia
| | - Bahaa Alshammari
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail, 81442, Saudi Arabia
| | - Dareen Alrdaian
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail, 81442, Saudi Arabia
| | - Reem Q Alshammari
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail, 81442, Saudi Arabia
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8
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Wei L, Xu M, Liu Z, Jiang C, Lin X, Hu Y, Wen X, Zou R, Peng C, Lin H, Wang G, Yang L, Fang L, Yang M, Zhang P. Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study. J Chem Inf Model 2023; 63:5341-5355. [PMID: 37549337 DOI: 10.1021/acs.jcim.3c00543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.
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Affiliation(s)
- Lin Wei
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Min Xu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Zhiqiang Liu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Chongguo Jiang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Xiaohua Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Yaogang Hu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Xiaoming Wen
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Rongfeng Zou
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Chunwang Peng
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Hongrui Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Guo Wang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Lijun Yang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Lei Fang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Mingjun Yang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Peiyu Zhang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
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9
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Liu J, Wan J, Ren Y, Shao X, Xu X, Rao L. DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2023; 63:4850-4863. [PMID: 37539963 DOI: 10.1021/acs.jcim.3c00776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.
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Affiliation(s)
- Jiaqi Liu
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Jian Wan
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Yanliang Ren
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Xubo Shao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Xin Xu
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education (MOE) Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, People's Republic of China
| | - Li Rao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
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10
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Oyedele AQK, Ogunlana AT, Boyenle ID, Adeyemi AO, Rita TO, Adelusi TI, Abdul-Hammed M, Elegbeleye OE, Odunitan TT. Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices. Mol Divers 2023; 27:1879-1903. [PMID: 36057867 PMCID: PMC9441019 DOI: 10.1007/s11030-022-10523-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/26/2022] [Indexed: 01/18/2023]
Abstract
The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.
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Affiliation(s)
- Abdul-Quddus Kehinde Oyedele
- Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
- Department of Chemistry, University of New Haven, West Haven, CT, USA
| | - Abdeen Tunde Ogunlana
- Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Ibrahim Damilare Boyenle
- Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
- Department of Chemistry and Biochemsitry, University of Maryland, Maryland, USA.
- College of Health Sciences, Crescent University, Abeokuta, Nigeria.
| | | | - Temionu Oluwakemi Rita
- Department of Medical Laboratory Technology, Lagos State College of Health, Lagos, Nigeria
| | - Temitope Isaac Adelusi
- Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Misbaudeen Abdul-Hammed
- Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Oluwabamise Emmanuel Elegbeleye
- Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Tope Tunji Odunitan
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
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11
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Zhou Y, Yu H, Vind AC, Kong L, Liu Y, Song X, Tu Z, Yun C, Smaill JB, Zhang QW, Ding K, Bekker-Jensen S, Lu X. Rational Design of Covalent Kinase Inhibitors by an Integrated Computational Workflow (Kin-Cov). J Med Chem 2023. [PMID: 37220641 DOI: 10.1021/acs.jmedchem.3c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Covalent kinase inhibitors (CKIs) hold great promise for drug development. However, examples of computationally guided design of CKIs are still scarce. Here, we present an integrated computational workflow (Kin-Cov) for rational design of CKIs. The design of the first covalent leucine-zipper and sterile-α motif kinase (ZAK) inhibitor was presented as an example to showcase the power of computational workflow for CKI design. The two representative compounds, 7 and 8, inhibited ZAK kinase with half-maximal inhibitory concentration (IC50) values of 9.1 and 11.5 nM, respectively. Compound 8 displayed an excellent ZAK target specificity in Kinome profiling against 378 wild-type kinases. Structural biology and cell-based Western blot washout assays validated the irreversible binding characteristics of the compounds. Our study presents a rational approach for the design of CKIs based on the reactivity and accessibility of nucleophilic amino acid residues in a kinase. The workflow is generalizable and can be applied to facilitate CKI-based drug design.
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Affiliation(s)
- Yang Zhou
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
| | - Hang Yu
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
| | - Anna Constance Vind
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Lulu Kong
- Department of Biochemistry and Biophysics, Institute of Systems Biomedicine and Beijing Key Laboratory of Tumor Systems Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Yiling Liu
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
| | - Xiaojuan Song
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
| | - Zhengchao Tu
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
| | - Caihong Yun
- Department of Biochemistry and Biophysics, Institute of Systems Biomedicine and Beijing Key Laboratory of Tumor Systems Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Jeff B Smaill
- Auckland Cancer Society Research Centre, Faculty of Medical and Health Sciences and Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland 92019, New Zealand
| | - Qing-Wen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, China
| | - Ke Ding
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
| | - Simon Bekker-Jensen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Xiaoyun Lu
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, 855 Xingye Avenue, Guangzhou 510632, China
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12
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Yu W, Weber DJ, MacKerell AD. Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation. J Chem Theory Comput 2023; 19:3007-3021. [PMID: 37115781 PMCID: PMC10205696 DOI: 10.1021/acs.jctc.3c00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.
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Affiliation(s)
- Wenbo Yu
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - David J. Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Alexander D. MacKerell
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
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13
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Song Q, Zeng L, Zheng Q, Liu S. SCARdock: A Web Server and Manually Curated Resource for Discovering Covalent Ligands. ACS OMEGA 2023; 8:10397-10402. [PMID: 36969452 PMCID: PMC10034828 DOI: 10.1021/acsomega.2c08147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Covalent drugs have been intentionally discarded historically due to the concern of off-target side effects, but the past decade has seen a fast resurgence of the discovery of covalent drugs. Compared to noncovalent ligands, covalent ligands might have better biochemical efficiency, lower patient burden, less dosing frequency, less drug resistance, and improved target specificity. RESULTS Previously, we proposed the steric-clashes alleviating receptor (SCAR) strategy for screening and repurposing covalent inhibitors. To help the discovery of covalent ligands targeting protein targets, we have developed a web server dedicated to providing the SCARdock protocol to general users. Along with this server, we presented three high-quality data sets for the discovery of covalent ligands: a manually curated data set containing 954 high-quality complex structures of covalent ligands and proteins, a manually curated data set of 68 experimentally confirmed covalent warheads targeting 11 different residues, and a prefiltered, classified, and ready-to-use data set of 690,018 entries of purchasable virtual compounds containing these experimentally verified warheads. CONCLUSIONS The SCARdock server and the accompanied data sets would be of great value to the discovery of covalent ligands and are available freely at http://www.liugroup.site/scardock/ or https://scardock.com.
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Affiliation(s)
- Qi Song
- Cooperative
Innovation Center of Industrial Fermentation (Ministry of Education
& Hubei Province) & Key Laboratory of Fermentation Engineering
(Ministry of Education), Hubei University
of Technology, Wuhan 430068, China
- Hubei
Key Laboratory of Industrial Microbiology & National “111”
Center for Cellular Regulation and Molecular Pharmaceutics, Hubei University of Technology, Wuhan 430068, China
| | - Lingyu Zeng
- Cooperative
Innovation Center of Industrial Fermentation (Ministry of Education
& Hubei Province) & Key Laboratory of Fermentation Engineering
(Ministry of Education), Hubei University
of Technology, Wuhan 430068, China
- Hubei
Key Laboratory of Industrial Microbiology & National “111”
Center for Cellular Regulation and Molecular Pharmaceutics, Hubei University of Technology, Wuhan 430068, China
| | - Qiang Zheng
- Cooperative
Innovation Center of Industrial Fermentation (Ministry of Education
& Hubei Province) & Key Laboratory of Fermentation Engineering
(Ministry of Education), Hubei University
of Technology, Wuhan 430068, China
- Hubei
Key Laboratory of Industrial Microbiology & National “111”
Center for Cellular Regulation and Molecular Pharmaceutics, Hubei University of Technology, Wuhan 430068, China
| | - Sen Liu
- Cooperative
Innovation Center of Industrial Fermentation (Ministry of Education
& Hubei Province) & Key Laboratory of Fermentation Engineering
(Ministry of Education), Hubei University
of Technology, Wuhan 430068, China
- Hubei
Key Laboratory of Industrial Microbiology & National “111”
Center for Cellular Regulation and Molecular Pharmaceutics, Hubei University of Technology, Wuhan 430068, China
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14
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Sha M, Rao L, Xu W, Qin Y, Su R, Wu Y, Fang Q, Wang H, Cui X, Zheng L, Gu W, Zhu C. Amino-Ligand-Coordinated Dicopper Active Sites Enable Catechol Oxidase-Like Activity for Chiral Recognition and Catalysis. NANO LETTERS 2023; 23:701-709. [PMID: 36598260 DOI: 10.1021/acs.nanolett.2c04697] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Developing highly active and selective advanced nanozymes for enzyme-mimicking catalysis remains a long-standing challenge for basic research and practical applications. Herein, we grafted a chiral histidine- (His-) coordinated copper core onto Zr-based metal-organic framework (MOF) basic backbones to structurally mirror the bimetal active site of natural catechol oxidase. Such a biomimetic fabricated process affords MOF-His-Cu with catechol oxidase-like activity, which can catalyze dehydrogenation and oxidation of o-diphenols and then transfer electrons to O2 to generate H2O2 by the cyclic conversion of Cu(II) and Cu(I). Specifically, the elaborate incorporation of chiral His arms results in higher catalytic selectivity over the chiral catechol substrates than natural enzyme. Density functional theory calculations reveal that the binding energy and potential steric effect in active site-substrate interactions account for the high stereoselectivity. This work demonstrates efficient and selective enzyme-mimicking catalytic processes and deepens the understanding of the catalytic mechanism of nanozymes.
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Affiliation(s)
- Meng Sha
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Li Rao
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Weiqing Xu
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Ying Qin
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Rina Su
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Yu Wu
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Qie Fang
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Hengjia Wang
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Xiaowen Cui
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Lirong Zheng
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Wenling Gu
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Chengzhou Zhu
- Key Laboratory of Pesticides and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
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15
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Wu Q, Huang SY. HCovDock: an efficient docking method for modeling covalent protein-ligand interactions. Brief Bioinform 2023; 24:6961470. [PMID: 36573474 DOI: 10.1093/bib/bbac559] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/02/2022] [Accepted: 11/17/2022] [Indexed: 12/28/2022] Open
Abstract
Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.
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Affiliation(s)
- Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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16
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Al-Ansi AY, Lin Z. MDO: A Computational Protocol for Prediction of Flexible Enzyme-Ligand Binding Mode. Curr Comput Aided Drug Des 2022; 18:CAD-EPUB-125919. [PMID: 36043706 DOI: 10.2174/1573409918666220827151546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/15/2022] [Accepted: 07/05/2022] [Indexed: 11/22/2022]
Abstract
AIM Developing a method for use in computer aided drug design Background: Predicting the structure of enzyme-ligand binding mode is essential for understanding the properties, functions, and mechanisms of the bio-complex, but is rather difficult due to the enormous sampling space involved. OBJECTIVE Accurate prediction of enzyme-ligand binding mode conformation. METHOD A new computational protocol, MDO, is proposed for finding the structure of ligand binding pose. MDO consists of sampling enzyme sidechain conformations via molecular dynamics simulation of enzyme-ligand system and clustering of the enzyme configurations, sampling ligand binding poses via molecular docking and clustering of the ligand conformations, and the optimal ligand binding pose prediction via geometry optimization and ranking by the ONIOM method. MDO is tested on 15 enzyme-ligand complexes with known accurate structures. RESULTS The success rate of MDO predictions, with RMSD < 2 Å, is 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities. CONCLUSION The MDO protocol provides high quality enzyme-ligand binding mode prediction with reasonable computational cost. The MDO protocol is recommended for use in the structure-based drug design.
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Affiliation(s)
- Amar Y Al-Ansi
- Hefei National Laboratory for Physical Sciences at Microscale & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei 230026, China
- Department of Physics, Sana'a University, Sana'a, Yemen
| | - Zijing Lin
- Hefei National Laboratory for Physical Sciences at Microscale & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei 230026, China
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