Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins
Problem
Predicting protein structures and designing functional proteins (efficient biocatalysts, bioactive proteins) is key to medicine, diagnostics and sustainable chemical manufacture. The paper reviews computational tools addressing this.
Why it matters
These tools impact medicine, diagnostics and chemical manufacture, and allow therapeutic protein targets to be modelled.
What the AI did
The review highlights machine-learning-based structure prediction (AlphaFold) and computational protein design, and their use in designing functional proteins for organic synthesis and drug discovery.
Results
not stated in source
Impact
The underlying AI tools were recognized with the 2024 Nobel Prize in Chemistry, per the abstract.
Record
| Venue | Angewandte Chemie International Edition |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Rebecca Buller, Jiřı́ Damborský, Donald Hilvert, Uwe T. Bornscheuer |
| Citations | 40 (OpenAlex, at import time) |
| References | 81 |
| Model or method | AlphaFold; computational protein design tools |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/anie.202421686 |
| Open access | hybrid |
| Keywords | AlphaFold, protein structure prediction, computational protein design, biocatalysts, drug discovery |
Importance rating
major — millions of lives, an industry or a field
Abstract states these tools fundamentally changed protein research, earned the 2024 Nobel Prize in Chemistry, and impact medicine, diagnostics, drug discovery, and sustainable chemical manufacture.
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