Fernandez Diaz, R. | bioRxiv Science

bioRxiv2026

BioMetAll v2.0: Introducing Scores, Metal Discrimination, and Side-Chain Descriptors for Predicting Metal-Binding Sites in Proteins.

Predicting the location of metal-binding sites in proteins is crucial for fundamental biological questions and biotechnological applications. Over the past decade, the rise in metal-bound protein structures in the Protein Data Bank, combined with advanced statistical models such as deep learning, has accelerated the development of metal-binding site prediction tools. Several approaches are now available, offering high-quality benchmarks and predictive performance. Our initial development in this area is BioMetAll, whose first version was based on backbone pre-organization. Here, we introduce its second version, featuring two major updates: 1) metal-specific scoring functions and 2) prediction using backbone geometry alone or in combination with first coordination sphere descriptors. Apart from demonstrating metal sensitivity and yielding better benchmarking results, this new version allows the assessment of the influence of considering the metals first coordination sphere versus backbone pre-organization on how metallic species bind to proteins.

bioinformatics↗

bioRxiv2024

BioBrigit, A Hybrid Deep Learning and Knowledge-based Approach to Model Metal Pathways in Proteins: Application to a Di-Copper Tyrosinase

The interaction of metallic species with proteins has been fundamental in evolution and key in many physiological processes. How metals bind to proteins also holds promise in many fields, like the design of new biocatalysts or the fight against pathogens. Nonetheless, uncovering the mechanism under which proteins recruit metal ions is far from understood and is one of the challenges in bioinorganic chemistry and structural biology. Computational methods are potentially among the most promising tools for this endeavor. Only a handful of efficient structural predictors of metal binding sites exist to date. Most focus on identifying the most stable binding sites in the protein scaffolds. Although these methods are very interesting, they do not consider the exploration of transient, sub-optimal binding sites that could be relevant in metal binding pathways in proteins. At the far end of modeling capabilities nowadays, we introduce BioBrigit, a hybrid Deep Learning - knowledge-based approach that suggests metal binding pathways in proteins. To demonstrate the methods viability, we apply it to the di-copper tyrosinase from Streptomyces castaneoglobisporus, a system for which crystallographic experiments allowed the identification of a series of transient sites of the copper in its path from a chaperone to the final catalytic site. Combined with homology modeling and large-scale molecular dynamics, BioBrigit allows for computational characterization of all experimental sites and for better understanding of the copper recruitment mechanism. BioBrigit appears as an asset in a field full of unknowns like metal binding to proteins and opens the way to further algorithms in this area. Source code, documentation, and data are available at https://github.com/insilichem/BioBrigit

bioinformatics↗