Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Periodical |
| Magazine / Source | Angewandte Chemie International Edition |
| MU Faculty or unit | |
| Citation | |
| web | https://onlinelibrary.wiley.com/doi/10.1002/anie.202421686 |
| Doi | http://dx.doi.org/10.1002/anie.202421686 |
| Keywords | AlphaFold; Computational protein design; Nobel prize; Protein engineering; Protein structure prediction |
| Attached files | |
| Description | 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 has advanced our understanding of 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. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science. |
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