BioBrigit, a Hybrid Machine Learning and Knowledge-Based Approach to Model Metal Pathways in Proteins: Application to a Dicopper Tyrosinase

The interaction of metallic species with proteins has been fundamental in evolution and is key in many physiological processes. How metals bind to proteins also holds promise in many fields, such as 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 among the most promising tools for this endeavor. Only a handful of efficient structural predictors of metal binding sites exist. Most of the work focuses on identifying the most stable binding sites in the protein scaffolds. Although these methods are interesting, they do not consider the exploration of transient, suboptimal 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 Machine Learningknowledge-based approach that suggests metal binding pathways in proteins. To demonstrate the method's viability, we apply it to the dicopper 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 a better understanding of the copper recruitment mechanism. BioBrigit appears as an asset in a field full of unknowns such as metal binding to proteins and opens the way to further algorithms in this area.