Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

Abstract

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures advances our understanding about how life’s molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine‐learning based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Importantly, both technologies also have a profound impact on drug discovery as any therapeutic protein target can now be modelled allowing the de novo design of peptide binders or the identification of small molecule ligands through the in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design enabled by this transformative shift in protein science.