Machine learning for functional protein design

doi:10.1038/s41587-024-02127-0

Machine learning for functional protein design

2024-02-15

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AI

DOI：10.1038/s41587-024-02127-0

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Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation. Notin, Rollins and colleagues discuss advances in computational protein design with a focus on redesign of existing proteins.

Keywords:

DE-NOVO DESIGN

COMPUTATIONAL DESIGN

DIRECTED EVOLUTION

ENZYME

PREDICTION

LANGUAGE

GENERATION

ANTIBODIES

LANDSCAPE

STABILITY

Machine learning for functional protein design

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