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Application of Computational Methods and Biomolecular Structural Modeling to the Investigation of Metalloproteins

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 20768

Special Issue Editors

Prof. Dr. Antonio Rosato

E-Mail Website
Guest Editor
Department of Chemistry and Center for Magnetic Resonance, University of Florence, 50019 Sesto Fiorentino, Italy
Interests: metalloproteins; NMR; metallomics; structural biology; metalloproteomics; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesco Musiani

E-Mail Website
Guest Editor
Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
Interests: molecular dynamics; protein–ligand and protein–protein docking; bioinformatics; homology modeling; QM modeling
Dr. Claudia Andreini

E-Mail Website
Guest Editor
Department of Chemistry and Center for Magnetic Resonance, University of Florence, Florence, Italy
Interests: metalloproteins; bioinformatics; biological databases; bioinorganic chemistry; predicitive tools; computational biology

Special Issue Information

Dear Colleagues,

The importance of metalloproteins for the proper functioning of the cells of all living organisms is well exemplified by their number: indeed, about 30%–40% of all proteins encoded by the genome need at least one metal ion to perform their biological function. The correct biosynthesis of metalloproteins requires living organisms to be able to cope with such issues as the limited bioavailability or the potential cytotoxicity of several essential metals. Thus, organisms have developed complex machineries that guarantee the proper intracellular concentration and distribution among compartments of each metal, i.e., metal homeostasis. To understand how the different metal-binding proteins and enzymes—together with the proteins responsible for metal homeostasis—carry out their function, it is necessary to investigate their three-dimensional (3D) structure and mobility at the atomic level. Simulations of molecular dynamics (MD) complement experimental information by showing how the 3D structure fluctuates over time and as a function of environmental conditions, with the possibility of exploring a wider range of timescales and conditions than usually amenable in experiments. Other computational techniques, such as comparative modeling and macromolecular docking, are also used routinely with similar aims. The structural bioinformatics of metalloproteins is important for providing links between 3D structure and biological function, with particular focus on the role of metal ions and how this role has evolved and become diversified among organisms. In this Special Issue, we wish to cover the most recent advances in all these aspects of computational methods for the study of metalloproteins by hosting a mix of original research articles and short critical reviews.

Prof. Francesco Musiani
Prof. Antonio Rosato
Prof. Claudia Andreini
Guest Editors

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Keywords

  • molecular dynamics of metalloproteins
  • enhanced sampling techniques on metalloproteins
  • molecular modeling of metalloproteins
  • macromolecular docking between metalloproteins

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Published Papers (5 papers)

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Research

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15 pages, 2572 KiB  
Article
Probing the Suitability of Different Ca2+ Parameters for Long Simulations of Diisopropyl Fluorophosphatase
by Alexander Zlobin, Igor Diankin, Sergey Pushkarev and Andrey Golovin
Molecules 2021, 26(19), 5839; https://doi.org/10.3390/molecules26195839 - 26 Sep 2021
Cited by 6 | Viewed by 2530
Abstract
Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of [...] Read more.
Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of diisopropyl fluorophosphatase (DFPase). Molecular modeling techniques offer a unique opportunity to address this task rationally by providing a physical description of the substrate-binding process. However, DFPase is a metalloenzyme, and correct modeling of metal cations is a challenging task generally coming with a tradeoff between simulation speed and accuracy. Here, we probe several molecular mechanical parameter combinations for their ability to empower long simulations needed to achieve a quantitative description of substrate binding. We demonstrate that a combination of the Amber19sb force field with the recently developed 12-6 Ca2+ models allows us to both correctly model DFPase and obtain new insights into the DFP binding process. Full article
(This article belongs to the Special Issue Application of Computational Methods and Biomolecular Structural Modeling to the Investigation of Metalloproteins)
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23 pages, 5329 KiB  
Article
Unravelling the Structure of the Tetrahedral Metal-Binding Site in METP3 through an Experimental and Computational Approach
by Salvatore La Gatta, Linda Leone, Ornella Maglio, Maria De Fenza, Flavia Nastri, Vincenzo Pavone, Marco Chino and Angela Lombardi
Molecules 2021, 26(17), 5221; https://doi.org/10.3390/molecules26175221 - 28 Aug 2021
Cited by 5 | Viewed by 3178
Abstract
Understanding the structural determinants for metal ion coordination in metalloproteins is a fundamental issue for designing metal binding sites with predetermined geometry and activity. In order to achieve this, we report in this paper the design, synthesis and metal binding properties of METP3, [...] Read more.
Understanding the structural determinants for metal ion coordination in metalloproteins is a fundamental issue for designing metal binding sites with predetermined geometry and activity. In order to achieve this, we report in this paper the design, synthesis and metal binding properties of METP3, a homodimer made up of a small peptide, which self assembles in the presence of tetrahedrally coordinating metal ions. METP3 was obtained through a redesign approach, starting from the previously developed METP molecule. The undecapeptide sequence of METP, which dimerizes to house a Cys4 tetrahedral binding site, was redesigned in order to accommodate a Cys2His2 site. The binding properties of METP3 were determined toward different metal ions. Successful assembly of METP3 with Co(II), Zn(II) and Cd(II), in the expected 2:1 stoichiometry and tetrahedral geometry was proven by UV-visible spectroscopy. CD measurements on both the free and metal-bound forms revealed that the metal coordination drives the peptide chain to fold into a turned conformation. Finally, NMR data of the Zn(II)-METP3 complex, together with a retrostructural analysis of the Cys-X-X-His motif in metalloproteins, allowed us to define the model structure. All the results establish the suitability of the short METP sequence for accommodating tetrahedral metal binding sites, regardless of the first coordination ligands. Full article
(This article belongs to the Special Issue Application of Computational Methods and Biomolecular Structural Modeling to the Investigation of Metalloproteins)
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11 pages, 602 KiB  
Article
Zincbindpredict—Prediction of Zinc Binding Sites in Proteins
by Sam M. Ireland and Andrew C. R. Martin
Molecules 2021, 26(4), 966; https://doi.org/10.3390/molecules26040966 - 12 Feb 2021
Cited by 10 | Viewed by 3929
Abstract
Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions [...] Read more.
Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site—missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥ 0.93 and precision ≥ 0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥ 0.80 and precision ≥ 0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API. Full article
(This article belongs to the Special Issue Application of Computational Methods and Biomolecular Structural Modeling to the Investigation of Metalloproteins)
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14 pages, 1396 KiB  
Article
Assessing the Direct Binding of Ark-Like E3 RING Ligases to Ubiquitin and Its Implication on Their Protein Interaction Network
by Dimitris G. Mintis, Anastasia Chasapi, Konstantinos Poulas, George Lagoumintzis and Christos T. Chasapis
Molecules 2020, 25(20), 4787; https://doi.org/10.3390/molecules25204787 - 19 Oct 2020
Cited by 2 | Viewed by 3082
Abstract
The ubiquitin pathway required for most proteins’ targeted degradation involves three classes of enzymes: E1-activating enzyme, E2-conjugating enzyme, and E3-ligases. The human Ark2C is the single known E3 ligase that adopts an alternative, Ub-dependent mechanism for the activation of Ub transfer in the [...] Read more.
The ubiquitin pathway required for most proteins’ targeted degradation involves three classes of enzymes: E1-activating enzyme, E2-conjugating enzyme, and E3-ligases. The human Ark2C is the single known E3 ligase that adopts an alternative, Ub-dependent mechanism for the activation of Ub transfer in the pathway. Its RING domain binds both E2-Ub and free Ub with high affinity, resulting in a catalytic active UbR-RING-E2-UbD complex formation. We examined potential changes in the conformational plasticity of the Ark2C RING domain and its ligands in their complexed form within the ubiquitin pathway through molecular dynamics (MD). Three molecular mechanics force fields compared to previous NMR relaxation studies of RING domain of Arkadia were used for effective and accurate assessment of MDs. Our results suggest the Ark2C Ub-RING docking site has a substantial impact on maintaining the conformational rigidity of E2-E3 assembly, necessary for the E3’s catalytic activity. In the UbR-RING-E2-UbD catalytic complex, the UbR molecule was found to have greater mobility than the other Ub, bound to E2. Furthermore, network-based bioinformatics helped us identify E3 RING ligase candidates which potentially exhibit similar structural modules as Ark2C, along with predicted substrates targeted by the Ub-binding RING Ark2C. Our findings could trigger a further exploration of related unrevealed functions of various other E3 RING ligases. Full article
(This article belongs to the Special Issue Application of Computational Methods and Biomolecular Structural Modeling to the Investigation of Metalloproteins)
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Review

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23 pages, 1922 KiB  
Review
Bioinformatics of Metalloproteins and Metalloproteomes
by Yan Zhang and Junge Zheng
Molecules 2020, 25(15), 3366; https://doi.org/10.3390/molecules25153366 - 24 Jul 2020
Cited by 47 | Viewed by 7100
Abstract
Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and [...] Read more.
Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life. Full article
(This article belongs to the Special Issue Application of Computational Methods and Biomolecular Structural Modeling to the Investigation of Metalloproteins)
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