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Clemente CM, Prieto JM, Martí M. Unlocking Precision Docking for Metalloproteins. J Chem Inf Model 2024; 64:1581-1592. [PMID: 38373276 DOI: 10.1021/acs.jcim.3c01853] [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: 02/21/2024]
Abstract
Metalloproteins play a fundamental role in molecular biology, contributing to various biological processes. However, the discovery of high-affinity ligands targeting metalloproteins has been delayed due, in part, to a lack of suitable tools and data. Molecular docking, a widely used technique for virtual screening of small-molecule ligand interactions with proteins, often faces challenges when applied to metalloproteins due to the particular nature of the ligand metal bond. To address these limitations associated with docking metalloproteins, we introduce a knowledge-driven docking approach known as "metalloprotein bias docking" (MBD), which extends the AutoDock Bias technique. We assembled a comprehensive data set of metalloprotein-ligand complexes from 15 different metalloprotein families, encompassing Ca, Co, Fe, Mg, Mn, and Zn metal ions. Subsequently, we conducted a performance analysis of our MBD method and compared it to the conventional docking (CD) program AutoDock4, applied to various metalloprotein targets within our data set. Our results demonstrate that MBD outperforms CD, significantly enhancing accuracy, selectivity, and precision in ligand pose prediction. Additionally, we observed a positive correlation between our predicted ligand free energies and the corresponding experimental values. These findings underscore the potential of MBD as a valuable tool for the effective exploration of metalloprotein-ligand interactions.
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Affiliation(s)
- Camila M Clemente
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellón 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
| | - Juan M Prieto
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellón 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
| | - Marcelo Martí
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellón 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
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Jiang D, Ye Z, Hsieh CY, Yang Z, Zhang X, Kang Y, Du H, Wu Z, Wang J, Zeng Y, Zhang H, Wang X, Wang M, Yao X, Zhang S, Wu J, Hou T. MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions. Chem Sci 2023; 14:2054-2069. [PMID: 36845922 PMCID: PMC9945430 DOI: 10.1039/d2sc06576b] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/11/2023] [Indexed: 01/21/2023] Open
Abstract
Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.
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Affiliation(s)
- Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China .,Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China .,College of Computer Science and Technology, Zhejiang University Hangzhou 310006 Zhejiang China
| | - Zhaofeng Ye
- Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Ziyi Yang
- Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaorui Wang
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and TechnologyMacao
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and TechnologyMacao
| | - Shengyu Zhang
- Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China
| | - Jian Wu
- College of Computer Science and Technology, Zhejiang University Hangzhou 310006 Zhejiang China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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Wang K. GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability. Brief Bioinform 2023; 24:6987821. [PMID: 36642411 DOI: 10.1093/bib/bbac620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/09/2022] [Accepted: 12/17/2022] [Indexed: 01/17/2023] Open
Abstract
Accurately predicting the interaction modes for metalloproteins remains extremely challenging in structure-based drug design and mechanism analysis of enzymatic catalysis due to the complexity of metal coordination in metalloproteins. Here, we report a docking method for metalloproteins based on geometric probability (GPDOCK) with unprecedented accuracy. The docking tests of 10 common metal ions with 9360 metalloprotein-ligand complexes demonstrate that GPDOCK has an accuracy of 94.3% in predicting binding pose. What is more, it can accurately realize the docking of metalloproteins with ligand when one or two water molecules are engaged in the metal ion coordination. Since GPDOCK only depends on the three-dimensional structure of metalloprotein and ligand, structure-based machine learning model is employed for the scoring of binding poses, which significantly improves computational efficiency. The proposed docking strategy can be an effective and efficient tool for drug design and further study of binding mechanism of metalloproteins. The manual of GPDOCK and the code for the logistical regression model used to re-rank the docking results are available at https://github.com/wangkai-zhku/GPDOCK.git.
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Affiliation(s)
- Kai Wang
- School of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, P. R. China.,Abinitio Technology Company, Ltd, Guangzhou 510640, P. R. China
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Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224:113705. [PMID: 34303871 DOI: 10.1016/j.ejmech.2021.113705] [Citation(s) in RCA: 182] [Impact Index Per Article: 60.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022]
Abstract
Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.
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Affiliation(s)
- Victor T Sabe
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Thandokuhle Ntombela
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Lindiwe A Jhamba
- HIV Pathogenesis Program, School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Glenn E M Maguire
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa; School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Thavendran Govender
- Faculty of Science and Agriculture, Department of Chemistry, University of Zululand, KwaDlangezwa, 3886, South Africa
| | - Tricia Naicker
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Hendrik G Kruger
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
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