Summary
This narrative review examines how artificial intelligence has accelerated protein engineering over recent years, enabling more efficient exploration of high-dimensional sequence and structure space. The authors discuss generative modelling approaches (for sequences, backbones, and atomic structures), methods for tailoring general models to specific design objectives, protein representation and scoring techniques, and synthesis-aware library design. The paper unifies these advances through a statistical lens, offering conceptual coherence to the field's recent rapid development.
UK applicability
The methodological advances reviewed have potential application to UK-based agricultural biotechnology and synthetic biology research, particularly in crop and livestock improvement programmes supported by BBSRC and UK Research and Innovation. However, the paper is primarily a technical review without direct recommendations for UK farming systems or policy.
Key measures
Not applicable to a review paper; the work surveys methodological approaches and computational techniques rather than empirical metrics
Outcomes reported
The paper reviews advances in AI-driven protein engineering, including generative modelling of sequences and structures, tailored design approaches for specific protein properties, and library design techniques. It synthesises these developments through a unified statistical interpretation of modern AI methods.
Topic tags
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