Abstract
Iron-Sulfur (Fe-S) proteins play essential roles in a wide range of biological processes, from energy conversion and respiration to DNA repair and redox signaling, making them highly relevant to both bioenergetics and human health. These proteins mediate electron transfer through finely tuned reduction potentials (RP) defined by their metal cofactors. However, predicting RP from protein structures remains a significant challenge due to the complex electronic nature of Fe-S clusters and their intricate coupling with the surrounding protein environment. This complexity limits our ability to systematically modulate RP, hindering efforts in high-throughput and rational protein design. In this study, we introduce a Machine Learning (ML) framework, FeS-RedPred, for accurate and scalable prediction of RP in Fe-S proteins. We focus on mono- and binuclear clusters, such as rubredoxins and [2Fe-2S] clusters of ferredoxins, Rieske, and mitoNEET-type, which serve as ideal model systems thanks to the availability of abundant structural and electrochemical data. Our approach relies on structure-derived molecular descriptors computed across multiple spatial scales, from local atomic environments to global protein-level features. Using Extreme Gradient Boosting (XGB) models, we achieve a mean absolute error of ∼40 mV, which is competitive with state-of-the-art computational approaches, while also providing a highly efficient compromise between accuracy and computational cost. Beyond predictive accuracy, our model also offers indications about the determinants of RP, enabling a basis for interpretation and potentially guiding protein engineering. This work provides a valuable foundation for understanding the redox behavior of metalloproteins, enabling the high-throughput prediction of redox potentials and informing data-driven design across diverse protein families.