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The coming of age of de novo protein design

Author

Listed:
  • Po-Ssu Huang

    (University of Washington
    Institute for Protein Design, University of Washington
    Present address: Department of Bioengineering, Stanford University, Stanford, California 94305, USA.)

  • Scott E. Boyken

    (University of Washington
    Institute for Protein Design, University of Washington
    Howard Hughes Medical Institute, University of Washington)

  • David Baker

    (University of Washington
    Institute for Protein Design, University of Washington
    Howard Hughes Medical Institute, University of Washington)

Abstract

There are 20200 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.

Suggested Citation

  • Po-Ssu Huang & Scott E. Boyken & David Baker, 2016. "The coming of age of de novo protein design," Nature, Nature, vol. 537(7620), pages 320-327, September.
    • Handle: RePEc:nat:nature:v:537:y:2016:i:7620:d:10.1038_nature19946
    DOI: 10.1038/nature19946
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    Cited by:

    1. Smrithi Krishnan R & Kalyanashis Jana & Amina H. Shaji & Karthika S. Nair & Anjali Devi Das & Devika Vikraman & Harsha Bajaj & Ulrich Kleinekathöfer & Kozhinjampara R. Mahendran, 2022. "Assembly of transmembrane pores from mirror-image peptides," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Julia Skokowa & Birte Hernandez Alvarez & Murray Coles & Malte Ritter & Masoud Nasri & Jérémy Haaf & Narges Aghaallaei & Yun Xu & Perihan Mir & Ann-Christin Krahl & Katherine W. Rogers & Kateryna Maks, 2022. "A topological refactoring design strategy yields highly stable granulopoietic proteins," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Shunshi Kohyama & Béla P. Frohn & Leon Babl & Petra Schwille, 2024. "Machine learning-aided design and screening of an emergent protein function in synthetic cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    5. Evgenii Lobzaev & Giovanni Stracquadanio, 2024. "Dirichlet latent modelling enables effective learning and sampling of the functional protein design space," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. Thomas W. Linsky & Kyle Noble & Autumn R. Tobin & Rachel Crow & Lauren Carter & Jeffrey L. Urbauer & David Baker & Eva-Maria Strauch, 2022. "Sampling of structure and sequence space of small protein folds," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Shuangjia Zheng & Tao Zeng & Chengtao Li & Binghong Chen & Connor W. Coley & Yuedong Yang & Ruibo Wu, 2022. "Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    8. Biao Ruan & Yanan He & Yingwei Chen & Eun Jung Choi & Yihong Chen & Dana Motabar & Tsega Solomon & Richard Simmerman & Thomas Kauffman & D. Travis Gallagher & John Orban & Philip N. Bryan, 2023. "Design and characterization of a protein fold switching network," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Sasha B. Ebrahimi & Devleena Samanta, 2023. "Engineering protein-based therapeutics through structural and chemical design," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Fatima A. Davila-Hernandez & Biao Jin & Harley Pyles & Shuai Zhang & Zheming Wang & Timothy F. Huddy & Asim K. Bera & Alex Kang & Chun-Long Chen & James J. Yoreo & David Baker, 2023. "Directing polymorph specific calcium carbonate formation with de novo protein templates," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Jose M. Martínez-Parra & Rebeca Gómez-Ojea & Geert A. Daudey & Martin Calvelo & Hector Fernández-Caro & Javier Montenegro & Julian Bergueiro, 2024. "Exo-chirality of the α-helix," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    12. W. Clifford Boldridge & Ajasja Ljubetič & Hwangbeom Kim & Nathan Lubock & Dániel Szilágyi & Jonathan Lee & Andrej Brodnik & Roman Jerala & Sriram Kosuri, 2023. "A multiplexed bacterial two-hybrid for rapid characterization of protein–protein interactions and iterative protein design," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    13. Amir Pandi & David Adam & Amir Zare & Van Tuan Trinh & Stefan L. Schaefer & Marie Burt & Björn Klabunde & Elizaveta Bobkova & Manish Kushwaha & Yeganeh Foroughijabbari & Peter Braun & Christoph Spahn , 2023. "Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    14. Baran Hashemi & Nikolai Hartmann & Sahand Sharifzadeh & James Kahn & Thomas Kuhr, 2024. "Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. Fabian Sesterhenn & Che Yang & Jaume Bonet & Johannes T. Cramer & Xiaolin Wen & Yimeng Wang & Chi I. Chiang & Luciano Andres Abriata & Iga Kucharska & Giacomo Castoro & Sabrina S. Vollers & Marie Gall, 2020. "De novo protein design enables the precise induction of RSV-neutralizing antibodies," Post-Print hal-02677103, HAL.
    16. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    17. Jordan Yang & Nandita Naik & Jagdish Suresh Patel & Christopher S Wylie & Wenze Gu & Jessie Huang & F Marty Ytreberg & Mandar T Naik & Daniel M Weinreich & Brenda M Rubenstein, 2020. "Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-26, May.
    18. SM Bargeen Alam Turzo & Justin T. Seffernick & Amber D. Rolland & Micah T. Donor & Sten Heinze & James S. Prell & Vicki H. Wysocki & Steffen Lindert, 2022. "Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    19. Pengfei Tian & Robert B Best, 2020. "Exploring the sequence fitness landscape of a bridge between protein folds," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
    20. Agnese I. Curatolo & Ofer Kimchi & Carl P. Goodrich & Ryan K. Krueger & Michael P. Brenner, 2023. "A computational toolbox for the assembly yield of complex and heterogeneous structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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