Abstract
Metal ions are essential for a broad range of biochemical processes in living organisms, with zinc being the second most abundant transition metal ion. Zinc has catalytic, structural, and regulatory functions in proteins, impacting virtually all aspects of cell biology. Currently, there are notable challenges in performing a large-scale accurate systematic analysis of the as-yet unexplored occurrences of zinc ion in nature. To address this, we developed ZincSight for predicting zinc-binding sites. ZincSight performs on par with existing structure-based tools in terms of the precision-recall curve for zinc ion detection, and the accuracy of spatial positioning of the ion, yet it is significantly faster, and offers a straightforward reasoning for its predictions, which is missing even in the best alternatives. Tests using a panel of metals show that, while trained on zinc-binding sites, ZincSight in fact detects all transition metal binding sites alike - a reflection of the similarity in coordination among the transition metals. It also detects binding sites for calcium and other alkaline-earth metals with lower accuracy, but not alkali metal binding sites. Suitable for exploring the usage of zinc and other transition metals in large sets of protein structures, or models thereof, ZincSight is available as a free-to-download open-source software at: https://github.com/MECHTI1/ZincSight. A Google Colab notebook is available at: https://colab.research.google.com/github/MECHTI1/ZincSight/blob/master/ZincSight.ipynb.