The coming of age of de novo protein design
Abstract
There are 20(200) possible amino-acid sequences for a 200-residue protein, of which the natural evolutionary process has sampled only an infinitesimal subset. De novo protein design explores the full sequence space, guided by the physical principles that underlie protein folding. Computational methodology has advanced to the point that a wide range of structures can be designed from scratch with atomic-level accuracy. Almost all protein engineering so far has involved the modification of naturally occurring proteins; it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology.
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Quantum annealing is suitable to the problem of protein design, i.e. finding the amino acid sequence that minimizes the energy of a chosen main-chain configuration.
“Quantum annealing is also suitable to the problem of protein design [43, 77, 78], i.e. finding the amino acid sequence that minimizes the energy of a chosen main-chain configuration.”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [77]
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De novo protein design enables the precise induction of RSV-neutralizing antibodies
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Self-citationComputational prediction of protein–protein binding affinities
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“On the longer run, it is desired to create completely new protein partners with programmed surface properties to allow for new interactions with selected candidate partners.(8,9) Such efforts may form the basis for synthetic complexes that can act as designed molecular machines with a desired new function.”
BackgroundLanguage models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I. M., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., & Rives, A. (2022) · doi:10.1101/2022.12.21.521521 · Cited by 181 · see citing works
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What has de novo protein design taught us about protein folding and biophysics?
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Self-citationLeveraging mRNA Sequences and Nanoparticles to Deliver SARS‐CoV‐2 Antigens In Vivo
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Self-citationUnderstanding a protein fold: The physics, chemistry, and biology of α-helical coiled coils
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Protein design via deep learning
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“This is a way to model enzymatic reactions, photophysical/photochemical processes or excitation and emission of photoactive proteins, which are usually confined to the active site [43, 77, 78].”
BackgroundDe novo designed peptides for cellular delivery and subcellular localisation
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Recent advances in automated protein design and its future challenges
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Coiled‐Coil Peptide Beacon: A Tunable Conformational Switch for Protein Detection
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- Authors
- Huang, P.-S., Boyken, S. E., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, David
- Venue
- Nature
- Published
- 2016-09
- Volume
- 537
- Issue
- 7620
- Pages
- 320-327
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2519539312
- Cited by
- 1,478
- References
- 86
- Citations by year
- 2026: 146 · 2025: 156 · 2024: 162 · 2023: 142 · 2022: 164 · 2021: 186 · 2020: 169 · 2019: 137 · 2018: 125 · 2017: 86 · 2016: 2 · 2013: 1
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- de novo protein design, protein folding, sequence spaces, protein engineering, functional proteins, protein structure prediction, nanotechnology, biomedicine
- MeSH terms
- Biophysics, Proteins, Drug Design, Protein Folding, Computational Biology, Nanostructures, Protein Stability
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