InterMetalDB: A Database and Browser of Intermolecular Metal Binding Sites in Macromolecules with Structural Information

Tran, Józef Ba; Krężel, Artur

doi:10.1021/acs.jproteome.0c00906

Cited by 17 publications

(14 citation statements)

References 44 publications

(104 reference statements)

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“… 40 , 45 , 46 In the InterMetalDB database, zinc(II) is the second-most common interfacial metal ion, closely following calcium(II). 47 Intriguingly, the prediction and experimental validation of zinc(II)-driven protein–protein (either intermolecular or inter-domain) interactions is still regarded as an outstanding question in bioinorganic chemistry. 46 …”

Section: Resultsmentioning

confidence: 99%

The zinc proteome of SARS-CoV-2

2022

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Zinc is an essential element for human health. Among its many functions, zinc(II) modulates the immune response to infections and, at high concentrations or in the presence of ionophores, inhibits the replication of various RNA viruses. Structural biology studies on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed that zinc(II) is the most common metal ion that binds to viral proteins. However, the number of zinc(II)-binding sites identified by experimental methods is far from exhaustive, as metal ions may be lost during protein purification protocols. To better define the zinc(II)-binding proteome of coronavirus, we leveraged the wealth of deposited structural data and state-of-the-art bioinformatics methods. Through this in silico approach, 15 experimental zinc(II) sites were identified and a further 22 were predicted in Spike, open reading frame (ORF)3a/d, ORF8, and several nonstructural proteins, highlighting an essential role of zinc(II) in viral replication. Furthermore, the structural relationships between viral and eukaryotic sites (typically zinc fingers) indicate that SARS-CoV-2 can compete with human proteins for zinc(II) binding. Given the double-edged effect of zinc(II) ions, both essential and toxic to coronavirus, only the complete elucidation of the structural and regulatory zinc(II)-binding sites can guide selective antiviral strategies based on zinc supplementation.

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Section: Resultsmentioning

confidence: 99%

The zinc proteome of SARS-CoV-2

2022

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“…Our survey reveals that Zn 2+ , Ca 2+ , and Mg 2+ occupy the top three prevalent metal ions. The Zn 2+ is currently the best-explored and described metal ion [ 26 ]. Zn 2+ participates in many biological processes, such as metabolism, immune system, neurotransmission, hormone secretion, and signaling [ 27 ].…”

Section: Public Databases For Metal Bindingmentioning

confidence: 99%

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Zhou

Liang

et al. 2022

BioMed Research International

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Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

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“…In order to extend the selection to include the environment around the metal and its ligands, we add to the MBS any other residue or chemical species with at least one atom within 5.0 Å of a metal ligand (orange residues in Figure 1 ). A simpler approach to the definition of the MBS adopted by some authors is simply to include any protein/nucleic acid residue or chemical species having at least one atom that is at a distance lower than an arbitrary threshold from the metal (e.g., [ 30 ]). In other words, with the latter definition, a sphere of fixed radius is centered on the metal, and the MBS is computed as the ensemble of all of the residues or molecules that have at least one atom contained in the sphere.…”

Section: Structure-based Definition Of Metal Binding Sites (Mbs)mentioning

confidence: 99%

“…The redundancy of the database contents was reduced by using both a protein sequence filter (at the 50% level) and a clustering approach to identify unique MBSs in structures harboring multiple sites. The analysis of InterMetalDB permitted the identification of metal preferences in interfacial sites as well as the corresponding macromolecular environments [ 30 ].…”

Section: Metalloprotein Databasesmentioning

confidence: 99%

“…having at least one atom that is at a distance lower than an arbitrary threshold from the metal (e.g., [30]). In other words, with the latter definition, a sphere of fixed radius is centered on the metal, and the MBS is computed as the ensemble of all of the residues or molecules that have at least one atom contained in the sphere.…”

Section: Structure-based Definition Of Metal Binding Sites (Mbs)mentioning

confidence: 99%

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Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

Andreini

Rosato

2022

IJMS

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All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.

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InterMetalDB: A Database and Browser of Intermolecular Metal Binding Sites in Macromolecules with Structural Information

Cited by 17 publications

References 44 publications

The zinc proteome of SARS-CoV-2

The zinc proteome of SARS-CoV-2

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

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