Predicting the location of coordinated metal ion-ligand binding sites using geometry-aware graph neural networks

Benchmarking Zinc-Binding Site Predictors: A Comparative Analysis of Structure-Based Approaches

Metalloproteins play crucial physiological roles across all domains of life, relying on metal ions for structural stability and catalytic activity. In recent years, computational approaches have emerged as powerful and increasingly reliable tools for predicting metal-binding sites in metalloproteins, enabling their application in the challenging field of metalloproteomics. Given the growing number of available tools, it is timely to design a reproducible approach to characterize their performance in specific usage scenarios. Thus, in this study, we selected some state-of-the-art structure-based predictors for zinc-binding sites and evaluated their performance on two data sets: experimental apoprotein structures and structural models generated by AlphaFold. Our results indicate that apoprotein structures pose significant challenges for predicting metal-binding sites. For these systems, the predictors achieved lower-than-expected performance due to the structural rearrangements occurring upon metalation. Conversely, predictions based on AlphaFold models yielded significantly better results, suggesting that they more closely resemble the holo forms of metalloproteins. Our findings highlight the great potential of metal-binding site predictions for advancing research in the field of metalloproteomics.