MeCOM: A Method for Comparing Three-Dimensional Metalloenzyme Active Sites

MeDBA: the Metalloenzyme Data Bank and Analysis platform

Metalloenzymes are attractive research targets in fields of chemistry, biology, and medicine. Given that metalloenzymes can manifest conservation of metal-coordination and ligand binding modes, the excavation and expansion of metalloenzyme-specific knowledge is of interest in bridging metalloenzyme-related fields. Building on our previous metalloenzyme-ligand association database, MeLAD, we have expanded the scope of metalloenzyme-specific knowledge and services, by forming a versatile platform, termed the Metalloenzyme Data Bank and Analysis (MeDBA). The MeDBA provides: (i) manual curation of metalloenzymes into different categories, that this M-I, M-II and M-III; (ii) comprehensive information on metalloenzyme activities, expression profiles, family and disease links; (iii) structural information on metalloenzymes, in particular metal binding modes; (iv) metalloenzyme substrates and bioactive molecules acting on metalloenzymes; (v) excavated metal-binding pharmacophores and (vi) analysis tools for structure/metal active site comparison and metalloenzyme profiling. The MeDBA is freely available at https://medba.ddtmlab.org.

The zinc proteome of SARS-CoV-2

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.

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

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.