Predicting unknown binding sites for transition-metal-based compounds in proteins

Abstract

Transition-metal-based compounds are promising therapeutic agents, particularly in cancer treatment. However, predicting the binding sites of such compounds remains a major challenge. In this work, we investigate the applicability of two tools, Metal3D and Metal1D, for this purpose. Although originally trained to predict zinc ion binding sites only, both predictors correctly identify at least one of the experimentally observed binding sites for transition metal complexes in each of the apo protein structures tested. At the same time, we highlight current limitations, such as the sensitivity to side-chain conformations, and discuss possible strategies for improvement. This work provides a first step toward establishing a robust computational pipeline in which rapid and low-cost predictors are able to identify putative hotspots for transition metal binding, which can then be refined using more accurate but computationally demanding methods.