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Predicting Iron-Sulfur Cluster Redox Potentials: A Simple Model Derived from Protein Structures
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DOI：10.1021/acsomega.5c01976.png)
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Abstract
En 中文
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 (R 2 = 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.
Keywords:
ATOMIC-RESOLUTION STRUCTURES
CRYSTAL-STRUCTURE
FERREDOXIN
ANGSTROM
2FE-2S
DOMAIN
THERMOSTABILITY
PHOTOSYNTHESIS
REDUCTION
MUTATION
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