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 (E_m) 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 E_m 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.