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Duerr, S. L.

Publications and source records attributed to Duerr, S. L..

2 recordsLinked to original sources

Zero-shot design of a de novo metalloenzyme

The de novo design of enzymes remains a central challenge, requiring consideration of catalytic mechanism and optimization across biochemical and biophysical criteria. Here we present dEVA (design by EVolutionary Algorithm), a multi-objective protein design framework built on principles drawn from evolutionary biology. We apply dEVA to the zero-shot, de novo design of metalloenzymes by optimizing the coordination sphere of catalytic metals. We characterize a bizinc metalloenzyme that exhibits promiscuous hydrolytic activity towards both phosphomonoesters and phosphodiesters. This design achieves a rate enhancement ((kcat/KM)/kw) up to 3 x 1013, comparable to characterized natural phosphatases. dEVA offers a general and modular strategy for the programmable design of protein function without dependence on natural templates or evolutionary information.

biochemistry↗

Predicting metal-protein interactions using cofolding methods: Status quo

Metals play important roles for enzyme function and many therapeutically relevant proteins. Despite the fact that the first drugs developed via computer aided drug design were metalloprotein inhibitors, many computational pipelines for drug discovery still discard metalloproteins due to the difficulties of modelling them computationally. New "cofolding" methods such as AlphaFold3 (AF3) (Abramson et al., 2024) and RoseTTAfold-AllAtom (RFAA) (Krishna et al., 2024) promise to improve this issue by being able to dock small molecules in presence of multiple complex cofactors including metals or covalent modifications. Here, we analyze the current status for metal ion prediction using these methods. We find that currently only AF3 provides realistic predictions for metal ions, RFAA in contrast does perform worse than more specialized models such as AllMetal3D in predicting the location of metal ions accurately. We find that AF3 predictions are consistent with expected physico-chemical trends/intuition whereas RFAA often also predicts unrealistic metal ion locations.

bioinformatics↗