Inferring metal binding sites in flexible regions of proteins

Prediction of Ca2+ Binding Site in Proteins With a Fast and Accurate Method Based on Statistical Mechanics and Analysis of Crystal Structures

Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. A fast, accurate, and interpretable computational prediction method can complement the experimental studies. In the current work, we have developed a method to predict the location of Ca2+ ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ ion density around protein structures, and the locations of the Ca2+ ions are obtained from the density. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method is physical, and the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that x-ray structures have issues in many of the failed cases, such as geometric irregularities and dubious assignment of ion positions. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.

Exploring the significance of potassium homeostasis in copper ion binding to human αB-Crystallin

αB-Crystallin (αB-Cry) is a small heat shock protein known for its protective role, with an adaptable structure that responds to environmental changes through oligomeric dynamics. Cu(II) ions are crucial for cellular processes but excessive amounts are linked to diseases like cataracts and neurodegeneration. This study investigated how optimal and detrimental Cu(II) concentrations affect αB-Cry oligomers and their chaperone activity, within the potassium-regulated ionic-strength environment. Techniques including isothermal titration calorimetry, differential scanning calorimetry, fluorescence spectroscopy, inductively coupled plasma atomic emission spectroscopy, cyclic voltammetry, dynamic light scattering, circular dichroism, and MTT assay were employed and complemented by computational methods. Results showed that potassium ions affected αB-Cry's structure, promoting Cu(II) binding at multiple sites and scavenging ability, and inhibiting ion redox reactions. Low concentrations of Cu(II), through modifications of oligomeric interfaces, induce regulation of surface charge and hydrophobicity, resulting in an increase in chaperone activity. Subunit dynamics were regulated, maintaining stable interfaces, thereby inhibiting further aggregation and allowing the functional reversion to oligomers after stress. High Cu(II) disrupted charge/hydrophobicity balance, sewing sizable oligomers together through subunit-subunit interactions, suppressing oligomer dissociation, and reducing chaperone efficiency. This study offers insights into how Cu(II) and potassium ions influence αB-Cry, advancing our understanding of Cu(II)-related diseases.

A cold‐active esterase enhances mesophilic properties through Mn 2+ binding

A key aspect of adaptation to cold environments is the production of cold-active enzymes by psychrophilic organisms. These enzymes not only have high activity at low temperatures, but also exhibit remarkable structural flexibility and thermolability. In this context, the role of metal ions has been little explored, and the few available studies seem to suggest that metal binding counteracts structural flexibility. This article reports an investigation into the role of the binding of manganese ion (Mn²⁺ ) in the thermal adaptation of an esterase (M-Est) of the GDSx family, identified in the genome of the Antarctic bacterium Marinomonas sp. ef1. M-Est is specific for esters containing acetate groups and turned out to be a highly thermolabile cold-active enzyme, with a catalysis optimum temperature of 5 °C and a melting temperature of 31.7 °C. A combination of biochemical and computational analyses, including molecular dynamics simulations, revealed that M-Est binds Mn²⁺ ions via a single binding site located on the surface of the enzyme, close to the active site. Although the interaction between M-Est and Mn²⁺ induces only local conformational changes involving the active site, quite surprisingly they trigger an improvement in both thermal stability and catalytic efficiency under mild temperature conditions. These results, together with the conservation of the Mn²⁺ binding site among psychrophilic and psychrotolerant homologues, suggest that Mn²⁺ binding may be a useful, albeit atypical, strategy to mitigate the detrimental effects of temperature on true cold-active enzymes.

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.

Zinc and Copper Ions Induce Aggregation of Human β-Crystallins

Cataracts are defined as the clouding of the lens due to the formation of insoluble protein aggregates. Metal ions exposure has been recognized as a risk factor in the cataract formation process. The γ and β crystallins are members of a larger family and share several structural features. Several studies have shown that copper and zinc ions induce the formation of γ-crystallins aggregates. However, the interaction of metal ions with β-crystallins, some of the most abundant crystallins in the lens, has not been explored until now. Here, we evaluate the effect of Cu(II) and Zn(II) ions on the aggregation of HβA1, as a representative of the acidic form, and HβB2, as a representative of the basic β-crystallins. We used several biophysical techniques and computational methods to show that Cu(II) and Zn(II) induce aggregation following different pathways. Both metal ions destabilize the proteins and impact protein folding. Copper induced a small conformational change in HβA1, leading to high-molecular-weight light-scattering aggregates, while zinc is more aggressive towards HβB2 and induces a larger conformational change. Our work provides information on the mechanisms of metal-induced aggregation of β-crystallins.