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Machine learning for functional protein design

Nature Biotechnology volume 42, pages 216–228 (2024)Cite this article

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Abstract

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.

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Fig. 1: Protein design objectives.
Fig. 2: Protein design applications.
Fig. 3: Typology of protein design models.

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Acknowledgements

We thank members of the Marks lab for valuable discussions. P.N. was supported by GSK, the UK Engineering and Physical Sciences Research Council (EPSRC ICASE award no. 18000077) and a Chan Zuckerberg Initiative Award (Neurodegeneration Challenge Network, CZI2018-191853). Y.G. holds a Turing AI Fellowship (Phase 1) at the Alan Turing Institute, which is supported by EPSRC grant reference V030302/1. C.S. is supported by the National Resource for Network Biology (NRNB, P41GM103504). D.S.M. holds a Ben Barres Early Career Award from the Chan Zuckerberg Initiative as part of the Neurodegeneration Challenge Network (CZI2018-191853) and is supported by a NIH Transformational Research Award (TR01 1R01CA260415).

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  1. These authors contributed equally: Pascal Notin, Nathan Rollins.

Authors and Affiliations

  1. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Pascal Notin, Chris Sander & Debora Marks

  2. Department of Computer Science, University of Oxford, Oxford, UK

    Pascal Notin & Yarin Gal

  3. Seismic Therapeutic, Cambridge, MA, USA

    Nathan Rollins

  4. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Chris Sander & Debora Marks

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D.M. is an advisor for Dyno Therapeutics, Octant, Jura Bio, Tectonic Therapeutic and Genentech and a cofounder of Seismic. N.R. is employed by Seismic. C.S. is on the scientific advisory board of CytoReason. The other authors declare no competing interests.

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Notin, P., Rollins, N., Gal, Y. et al. Machine learning for functional protein design. Nat Biotechnol 42, 216–228 (2024). https://doi.org/10.1038/s41587-024-02127-0

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