IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-54814-w.html
   My bibliography  Save this article

Protein engineering using variational free energy approximation

Author

Listed:
  • Evgenii Lobzaev

    (The University of Edinburgh
    The University of Edinburgh)

  • Michael A. Herrera

    (The University of Edinburgh)

  • Martyna Kasprzyk

    (The University of Edinburgh)

  • Giovanni Stracquadanio

    (The University of Edinburgh)

Abstract

Engineering proteins is a challenging task requiring the exploration of a vast design space. Traditionally, this is achieved using Directed Evolution (DE), which is a laborious process. Generative deep learning, instead, can learn biological features of functional proteins from sequence and structural datasets and return novel variants. However, most models do not generate thermodynamically stable proteins, thus leading to many non-functional variants. Here we propose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which generates stable and functional variants by learning the sequence and thermodynamic landscape of a protein. We evaluate PREVENT by designing 40 variants of the conditionally essential E. coli phosphotransferase N-acetyl-L-glutamate kinase (EcNAGK). We find 85% of the variants to be functional, with 55% of them showing similar growth rate compared to the wildtype enzyme, despite harbouring up to 9 mutations. Our results support a new approach that can significantly accelerate protein engineering.

Suggested Citation

  • Evgenii Lobzaev & Michael A. Herrera & Martyna Kasprzyk & Giovanni Stracquadanio, 2024. "Protein engineering using variational free energy approximation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54814-w
    DOI: 10.1038/s41467-024-54814-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-54814-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-54814-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ivan Anishchenko & Samuel J. Pellock & Tamuka M. Chidyausiku & Theresa A. Ramelot & Sergey Ovchinnikov & Jingzhou Hao & Khushboo Bafna & Christoffer Norn & Alex Kang & Asim K. Bera & Frank DiMaio & La, 2021. "De novo protein design by deep network hallucination," Nature, Nature, vol. 600(7889), pages 547-552, December.
    2. Jung-Eun Shin & Adam J. Riesselman & Aaron W. Kollasch & Conor McMahon & Elana Simon & Chris Sander & Aashish Manglik & Andrew C. Kruse & Debora S. Marks, 2021. "Protein design and variant prediction using autoregressive generative models," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Alex Hawkins-Hooker & Florence Depardieu & Sebastien Baur & Guillaume Couairon & Arthur Chen & David Bikard, 2021. "Generating functional protein variants with variational autoencoders," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-23, February.
    4. Peter Eastman & Jason Swails & John D Chodera & Robert T McGibbon & Yutong Zhao & Kyle A Beauchamp & Lee-Ping Wang & Andrew C Simmonett & Matthew P Harrigan & Chaya D Stern & Rafal P Wiewiora & Bernar, 2017. "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-17, July.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. Jeffrey A. Ruffolo & Lee-Shin Chu & Sai Pooja Mahajan & Jeffrey J. Gray, 2023. "Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Erika Erickson & Japheth E. Gado & Luisana Avilán & Felicia Bratti & Richard K. Brizendine & Paul A. Cox & Raj Gill & Rosie Graham & Dong-Jin Kim & Gerhard König & William E. Michener & Saroj Poudel &, 2022. "Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. 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.
    7. 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.
    8. Janni Harju & Muriel C. F. Teeseling & Chase P. Broedersz, 2024. "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    9. Christian Hentrich & Mateusz Putyrski & Hanh Hanuschka & Waldemar Preis & Sarah-Jane Kellmann & Melissa Wich & Manuel Cavada & Sarah Hanselka & Victor S. Lelyveld & Francisco Ylera, 2024. "Engineered reversible inhibition of SpyCatcher reactivity enables rapid generation of bispecific antibodies," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    10. Fatma-Elzahraa Eid & Albert T. Chen & Ken Y. Chan & Qin Huang & Qingxia Zheng & Isabelle G. Tobey & Simon Pacouret & Pamela P. Brauer & Casey Keyes & Megan Powell & Jencilin Johnston & Binhui Zhao & K, 2024. "Systematic multi-trait AAV capsid engineering for efficient gene delivery," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    11. Tom Dixon & Derek MacPherson & Barmak Mostofian & Taras Dauzhenka & Samuel Lotz & Dwight McGee & Sharon Shechter & Utsab R. Shrestha & Rafal Wiewiora & Zachary A. McDargh & Fen Pei & Rajat Pal & João , 2022. "Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry," Nature Communications, Nature, vol. 13(1), pages 1-24, December.
    12. Junkil Park & Youhan Lee & Jihan Kim, 2025. "Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    13. Joseph G. Beton & Thomas Mulvaney & Tristan Cragnolini & Maya Topf, 2024. "Cryo-EM structure and B-factor refinement with ensemble representation," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    14. Andreas Mardt & Tim Hempel & Cecilia Clementi & Frank Noé, 2022. "Deep learning to decompose macromolecules into independent Markovian domains," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    15. Cheng Shen & Yuqing Zhang & Wenwen Cui & Yimeng Zhao & Danqi Sheng & Xinyu Teng & Miaoqing Shao & Muneyoshi Ichikawa & Jin Wang & Motoyuki Hattori, 2023. "Structural insights into the allosteric inhibition of P2X4 receptors," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    16. Re’em Moskovitz & Tossapol Pholcharee & Sophia M. DonVito & Bora Guloglu & Edward Lowe & Franziska Mohring & Robert W. Moon & Matthew K. Higgins, 2023. "Structural basis for DARC binding in reticulocyte invasion by Plasmodium vivax," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    17. Namrata Anand & Raphael Eguchi & Irimpan I. Mathews & Carla P. Perez & Alexander Derry & Russ B. Altman & Po-Ssu Huang, 2022. "Protein sequence design with a learned potential," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    18. David P. McDonogh & Julian D. Gale & Paolo Raiteri & Denis Gebauer, 2024. "Redefined ion association constants have consequences for calcium phosphate nucleation and biomineralization," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    19. Karol Buda & Charlotte M. Miton & Nobuhiko Tokuriki, 2023. "Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    20. F. P. Panei & P. Gkeka & M. Bonomi, 2024. "Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54814-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.