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Machine learning for functional protein design
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PRE
AI
DOI：10.1038/s41587-024-02127-0.png)
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Abstract
En 中文
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
Journal
IF:
41.7
Papers:
1.2W
Citations:
10.1W
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