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Class I and class II glutaredoxins (Grxs) are glutathione (GSH)-dependent proteins, that function as oxidoreductases (class I) or mediate cellular iron trafficking (class II). Some members of class I Grxs like human Grx2 are able to complex a [2Fe-2S] cluster and form a dimeric holo complex, which renders them catalytically inactive and is the basis for their function as redox sensors. Class II Grxs like human Grx5 also complex [2Fe-2S] clusters, however these proteins transfer the clusters to other proteins. Both functionally distinct classes share a similar thioredoxin fold and conserved interaction sites for the non-covalently binding of GSH, which is required to complex the [2Fe-2S] cluster. Furthermore, the proteins from both classes contain a highly nucleophilic active site cysteine that would allow both classes to catalyze GSH-dependent oxidoreduction reactions. Despite of these similar features, only class I Grxs are able to form a mixed disulfide with GSH and to reversibly transfer it to protein thiols (de-/glutathionylation). Interestingly, neither class I Grxs nor class II Grxs can effectively compensate the loss of an essential member of the other class. Even though some structural differences were described earlier, the basis for their different functions remained unknown. In particular, the lack of catalytic activity of class II Grxs as oxidoreductases could not be explained. Here, we demonstrate that the different conformations of a conserved lysyl side chain are the molecular determinant of the oxidoreductase or Fe-S transfer activity of class I and II Grxs, respectively. A specific loop structure that is conserved in all class II Grxs determines one lysyl conformation that prevents the formation of a mixed disulfide of the active site cysteinyl thiol with GSH. Using engineered mutants of hGrx2 and hGrx5, we demonstrated that the exchange of the distinct loop between the classes results in a loss of oxidoreductase function of class I hGrx2 and the gain of oxidoreductase activity of class II hGrx5. The altered GSH binding mode also profoundly changes the [2Fe-2S] cluster binding of the engineered mutants and thereby also influences stability of the holo complexes, a pre-determinant for [Fe-S] cluster transfer activity. With the minor shift of 2 Å in a conserved lysyl side chain orientation we were not only able to modify the catalytic activity of two small human mitochondrial proteins, but on a much larger scale also provided evidence for the previously unknown structural basis that determines the function of all class I and class II Grxs.
The oxidoreductase activity of hGrx2 was also analyzed in vivo in a model of doxorubicin cell toxicity. Applying a mass spectrometrical approach, we identified various mitochondrial proteins as targets for redox regulation. Furthermore, our results gave reason to reconsider some common assumptions regarding doxorubicin-induced apoptosis and the protective function of mitochondrial Grx2.
The hairpin ribozyme is a small Mg2+-dependent catalytic RNA molecule able to catalyze the trans-cleavage of an RNA substrate via a reversible trans-esterification mechanism. In this study, the cleavage activities of several fragmented hairpin ribozyme systems were examined. Due to the complex catalytic structure of the hairpin ribozyme, a new boronic acid ester was used as a covalent linkage to hold the folding of the functional system. It has been demonstrated the possibility of replacing the phosphodiester linkage, at specific positions, with a boronic acid ester to restore or improve the catalytic activity of fragmented hairpin ribozyme.
Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.