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Chu AE, Lu T, Huang PS. Sparks of function by de novo protein design. Nat Biotechnol 2024; 42:203-215. [PMID: 38361073 PMCID: PMC11366440 DOI: 10.1038/s41587-024-02133-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
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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Affiliation(s)
- Alexander E Chu
- Biophysics Program, Stanford University, Palo Alto, CA, USA
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
- Google DeepMind, London, UK
| | - Tianyu Lu
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Palo Alto, CA, USA.
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
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Emenecker RJ, Guadalupe K, Shamoon NM, Sukenik S, Holehouse AS. Sequence-ensemble-function relationships for disordered proteins in live cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564547. [PMID: 37961106 PMCID: PMC10634935 DOI: 10.1101/2023.10.29.564547] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Intrinsically disordered protein regions (IDRs) are ubiquitous across all kingdoms of life and play a variety of essential cellular roles. IDRs exist in a collection of structurally distinct conformers known as an ensemble. An IDR's amino acid sequence determines its ensemble, which in turn can play an important role in dictating molecular function. Yet a clear link connecting IDR sequence, its ensemble properties, and its molecular function in living cells has not been directly established. Here, we set out to test this sequence-ensemble-function paradigm using a novel computational method (GOOSE) that enables the rational design of libraries of IDRs by systematically varying specific sequence properties. Using ensemble FRET, we measured the ensemble dimensions of a library of rationally designed IDRs in human-derived cell lines, revealing how IDR sequence influences ensemble dimensions in situ. Furthermore, we show that the interplay between sequence and ensemble can tune an IDR's ability to sense changes in cell volume - a de novo molecular function for these synthetic sequences. Our results establish biophysical rules for intracellular sequence-ensemble relationships, enable a new route for understanding how IDR sequences map to function in live cells, and set the ground for the design of synthetic IDRs with de novo function.
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Affiliation(s)
- Ryan J. Emenecker
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO
- Center for Biomolecular Condensates (CBC), Washington University in St. Louis, St. Louis, MO
| | - Karina Guadalupe
- Department of Chemistry and Biochemistry, University of California, Merced, CA
- Center for Cellular and Biomolecular Machines, University of California, Merced, CA
| | - Nora M. Shamoon
- Center for Cellular and Biomolecular Machines, University of California, Merced, CA
- Quantitative Systems Biology Program, University of California, Merced, CA
| | - Shahar Sukenik
- Department of Chemistry and Biochemistry, University of California, Merced, CA
- Center for Cellular and Biomolecular Machines, University of California, Merced, CA
- Quantitative Systems Biology Program, University of California, Merced, CA
- Health Sciences Research Institute, University of California, Merced, CA
| | - Alex S. Holehouse
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO
- Center for Biomolecular Condensates (CBC), Washington University in St. Louis, St. Louis, MO
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