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 their binding sites 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 successfully identify several experimentally observed binding sites for transition metal complexes directly from apo protein structures. 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. Author summaryTransition metals play a crucial role as therapeutic agents, especially in cancer therapy. However, the prediction of their binding site locations is challenging, as accurate computational methods often require time-consuming simulations, making them impractical when many possible binding sites must be explored. In this work, we explored the capability of two binding site predictors, originally developed to locate metal ions in proteins, to identify binding sites for more complex covalently-bound transition metal based agents. We found that these tools can often identify the experimentally-known binding regions, even when starting from the apo structure, in which the protein does not already contain the metal compound. At the same time, our results show clear limitations in more challenging cases, particularly when the binding involves only a single amino acid or when the binding site undergoes major structural rearrangements upon binding. Overall, our study shows that fast predictors can provide valuable early insights in the investigation of the binding sites of covalently-bound transition metal based compounds. When combined with more accurate simulation techniques, they can help focus computational efforts and ultimately support the rational design of transition metal based drugs.