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Sparks of function by de novo protein design

Nature Biotechnology volume 42, pages 203–215 (2024)Cite this article

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Abstract

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This ‘central dogma’ underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

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Fig. 1: The central dogma of de novo protein design.
Fig. 2: Defining functional motifs in protein design.
Fig. 3: Controlling protein structure to scaffold functional elements.
Fig. 4: Hierarchical nature of diffusion models.
Fig. 5: Designing sequence to specify structure.
Fig. 6: Examples of functional de novo design.

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Acknowledgements

We thank S. Ovchinnikov for feedback on the manuscript. For readers interested in more depth on physics-based modeling approaches, such as Rosetta, we recommend other reviews2,3,17. We also recommend two recent reviews with related perspectives, focusing more on the details and impact of machine learning on protein engineering and design, especially on direct sequence modeling73,145. A.E.C. is supported by the NSF GRFP and the Merck SEEDS Program. T.L. is supported by a Stanford Graduate Fellowship. P.-S.H. is supported by the NIH (R01GM147893), the American Cancer Society (ACS 134055-IRG-218), the BASF CARA project and the Discovery Innovation Fund.

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  1. Alexander E. Chu

    Present address: Google DeepMind, London, UK

Authors and Affiliations

  1. Biophysics Program, Stanford University, Palo Alto, CA, USA

    Alexander E. Chu & Po-Ssu Huang

  2. Department of Bioengineering, Stanford University, Palo Alto, CA, USA

    Alexander E. Chu, Tianyu Lu & Po-Ssu Huang

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Planning, figure production and writing of the Review: all authors. Reference curation: A.E.C. and T.L. Supplementary tables: A.E.C. and T.L.

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Correspondence to Po-Ssu Huang.

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Chu, A.E., Lu, T. & Huang, PS. Sparks of function by de novo protein design. Nat Biotechnol 42, 203–215 (2024). https://doi.org/10.1038/s41587-024-02133-2

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