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Bazayeva M, Andreini C, Rosato A. A database overview of metal-coordination distances in metalloproteins. Acta Crystallogr D Struct Biol 2024; 80:362-376. [PMID: 38682667 PMCID: PMC11066882 DOI: 10.1107/s2059798324003152] [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: 12/06/2023] [Accepted: 04/11/2024] [Indexed: 05/01/2024] Open
Abstract
Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.
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Affiliation(s)
- Milana Bazayeva
- Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Claudia Andreini
- Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Antonio Rosato
- Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
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Pelentir GF, Tomazini A, Bevilaqua VR, Viviani VR. Role of Histidine 310 in Amydetes vivianii firefly luciferase pH and metal sensitivities and improvement of its color tuning properties. Photochem Photobiol Sci 2024; 23:997-1010. [PMID: 38693447 DOI: 10.1007/s43630-024-00570-1] [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: 11/08/2023] [Accepted: 03/28/2024] [Indexed: 05/03/2024]
Abstract
Firefly luciferases emit yellow-green light and are pH-sensitive, changing the bioluminescence color to red in the presence of heavy metals, acidic pH and high temperatures. These pH and metal-sensitivities have been recently harnessed for intracellular pH indication and toxic metal biosensing. However, whereas the structure of the pH sensor and the metal binding site, which consists mainly of two salt bridges that close the active site (E311/R337 and H310/E354), has been identified, the specific role of residue H310 in pH and metal sensing is still under debate. The Amydetes vivianii firefly luciferase has one of the lowest pH sensitivities among the group of pH-sensitive firefly luciferases, displaying high bioluminescent activity and special spectral selectivity for cadmium and mercury, which makes it a promising analytical reagent. Using site-directed mutagenesis, we have investigated in detail the role of residue H310 on pH and metal sensitivity in this luciferase. Negatively charged residues at position 310 increase the pH sensitivity and metal sensitivity; H310G considerably increases the size of the cavity, severely impacting the activity, H310R closes the cavity, and H310F considerably decreases both pH and metal sensitivities. However, no substitution completely abolished pH and metal sensitivities. The results indicate that the presence of negatively charged and basic side chains at position 310 is important for pH sensitivity and metals coordination, but not essential, indicating that the remaining side chains of E311 and E354 may still coordinate some metals in this site. Furthermore, a metal binding site search predicted that H310 mutations decrease the affinity mainly for Zn, Ni and Hg but less for Cd, and revealed the possible existence of additional binding sites for Zn, Ni and Hg.
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Affiliation(s)
- Gabriel F Pelentir
- Graduate Program of Biotechnology, Federal University of São Carlos (UFSCar), São Carlos, São Paulo, Brazil
| | - Atílio Tomazini
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
| | - Vanessa R Bevilaqua
- Biomaterials Laboratory, Pontifical Catholic University-PUC, Sorocaba, SP, Brazil
| | - Vadim R Viviani
- Graduate Program of Biotechnology, Federal University of São Carlos (UFSCar), São Carlos, São Paulo, Brazil.
- Department of Physics, Chemistry and Mathematics, Center for Sustainable Sciences and Technologies (CCTS), UFSCar, Sorocaba, SP, Brazil.
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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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Ibrahim IH. Metalloproteins and metalloproteomics in health and disease. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 141:123-176. [PMID: 38960472 DOI: 10.1016/bs.apcsb.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Metalloproteins represents more than one third of human proteome, with huge variation in physiological functions and pathological implications, depending on the metal/metals involved and tissue context. Their functions range from catalysis, bioenergetics, redox, to DNA repair, cell proliferation, signaling, transport of vital elements, and immunity. The human metalloproteomic studies revealed that many families of metalloproteins along with individual metalloproteins are dysregulated under several clinical conditions. Also, several sorts of interaction between redox- active or redox- inert metalloproteins are observed in health and disease. Metalloproteins profiling shows distinct alterations in neurodegenerative diseases, cancer, inflammation, infection, diabetes mellitus, among other diseases. This makes metalloproteins -either individually or as families- a promising target for several therapeutic approaches. Inhibitors and activators of metalloenzymes, metal chelators, along with artificial metalloproteins could be versatile in diagnosis and treatment of several diseases, in addition to other biomedical and industrial applications.
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Affiliation(s)
- Iman Hassan Ibrahim
- Department of Biochemistry and Molecular Biology, Faculty of Pharmacy (Girls), Al-Azhar University, Cairo, Egypt.
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Laveglia V, Bazayeva M, Andreini C, Rosato A. Hunting down zinc(II)-binding sites in proteins with distance matrices. Bioinformatics 2023; 39:btad653. [PMID: 37878807 PMCID: PMC10630175 DOI: 10.1093/bioinformatics/btad653] [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: 08/14/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 10/27/2023] Open
Abstract
MOTIVATION In recent years, high-throughput sequencing technologies have made available the genome sequences of a huge variety of organisms. However, the functional annotation of the encoded proteins often still relies on low-throughput and costly experimental studies. Bioinformatics approaches offer a promising alternative to accelerate this process. In this work, we focus on the binding of zinc(II) ions, which is needed for 5%-10% of any organism's proteins to achieve their physiologically relevant form. RESULTS To implement a predictor of zinc(II)-binding sites in the 3D structures of proteins, we used a neural network, followed by a filter of the network output against the local structure of all known sites. The latter was implemented as a function comparing the distance matrices of the Cα and Cβ atoms of the sites. We called the resulting tool Master of Metals (MOM). The structural models for the entire proteome of an organism generated by AlphaFold can be used as input to our tool in order to achieve annotation at the whole organism level within a few hours. To demonstrate this, we applied MOM to the yeast proteome, obtaining a precision of about 76%, based on data for homologous proteins. AVAILABILITY AND IMPLEMENTATION Master of Metals has been implemented in Python and is available at https://github.com/cerm-cirmmp/Master-of-metals.
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Affiliation(s)
- Vincenzo Laveglia
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
| | - Milana Bazayeva
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy
| | - Claudia Andreini
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Sesto Fiorentino 50019, Italy
| | - Antonio Rosato
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Sesto Fiorentino 50019, Italy
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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: 6] [Impact Index Per Article: 6.0] [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: 1] [Impact Index Per Article: 1.0] [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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