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 method's 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.