Predicting Iron-Sulfur Cluster Redox Potentials: A Simple Model Derived from Protein Structures

Abstract

Iron-sulfur (Fe-S) clusters are critical cofactors in metalloproteins, essential for cellular processes such as energy production, DNA repair, enzymatic catalysis, and metabolic regulation. While Fe-S cluster functions are intimately linked to their redox properties, their precise roles in many proteins remain unclear. In this study, we present a regression model based on experimental redox potential (Em) data, utilizing only two features: the Fe-S cluster’s total charge and the Fe atoms’ average valence. This model achieves a high correlation with experimental data (R2 = 0.82) and an average prediction error of 0.12 V. Applying this model across the Protein Data Bank, we predict Em values for all cataloged Fe-S clusters, uncovering redox potential trends across diverse cluster classes. The computed redox potentials showed strong agreement with experimental values, achieving an overall accuracy of 88%. This streamlined, computationally accessible approach enhances the annotation and mechanistic understanding of Fe-S proteins, offering new insights into the redox variability of electron transport proteins. Our model holds promise for advancing studies of metalloprotein function and facilitating the design of bioinspired redox systems.