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Systematic multi-trait AAV capsid engineering for efficient gene delivery

Author

Listed:
  • Fatma-Elzahraa Eid

    (Broad Institute of MIT and Harvard
    Al-Azhar University)

  • Albert T. Chen

    (Broad Institute of MIT and Harvard)

  • Ken Y. Chan

    (Broad Institute of MIT and Harvard)

  • Qin Huang

    (Broad Institute of MIT and Harvard)

  • Qingxia Zheng

    (Broad Institute of MIT and Harvard)

  • Isabelle G. Tobey

    (Broad Institute of MIT and Harvard)

  • Simon Pacouret

    (Broad Institute of MIT and Harvard)

  • Pamela P. Brauer

    (Broad Institute of MIT and Harvard)

  • Casey Keyes

    (Broad Institute of MIT and Harvard)

  • Megan Powell

    (Broad Institute of MIT and Harvard)

  • Jencilin Johnston

    (Broad Institute of MIT and Harvard)

  • Binhui Zhao

    (Broad Institute of MIT and Harvard)

  • Kasper Lage

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Broad Institute of MIT and Harvard
    Mental Health Services)

  • Alice F. Tarantal

    (University of California)

  • Yujia A. Chan

    (Broad Institute of MIT and Harvard)

  • Benjamin E. Deverman

    (Broad Institute of MIT and Harvard)

Abstract

Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that uniformly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 88% of library variants on all six predetermined criteria. Furthermore, the models, trained only on mouse in vivo and human in vitro Fit4Function data, accurately predicted AAV capsid variant biodistribution in macaque. Top candidates exhibited production yields comparable to AAV9, efficient murine liver transduction, up to 1000-fold greater human hepatocyte transduction, and increased enrichment relative to AAV9 in a screen for liver transduction in macaques. The Fit4Function strategy ultimately makes it possible to predict cross-species traits of peptide-modified AAV capsids and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50555-y
    DOI: 10.1038/s41467-024-50555-y
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    References listed on IDEAS

    as
    1. Jonas Weinmann & Sabrina Weis & Josefine Sippel & Warut Tulalamba & Anca Remes & Jihad El Andari & Anne-Kathrin Herrmann & Quang H. Pham & Christopher Borowski & Susanne Hille & Tanja Schönberger & No, 2020. "Identification of a myotropic AAV by massively parallel in vivo evaluation of barcoded capsid variants," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    2. Leszek Lisowski & Allison P. Dane & Kirk Chu & Yue Zhang & Sharon C. Cunningham & Elizabeth M. Wilson & Sean Nygaard & Markus Grompe & Ian E. Alexander & Mark A. Kay, 2014. "Selection and evaluation of clinically relevant AAV variants in a xenograft liver model," Nature, Nature, vol. 506(7488), pages 382-386, February.
    3. 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.
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