Optimal trade-off control in machine learning–based library design, with application to adeno-associated virus (AAV) for gene therapy-Reference-Cited by-同舟云学术

Optimal trade-off control in machine learning–based library design, with application to adeno-associated virus (AAV) for gene therapy

Published:2024-01-26 Issue:4 Volume:10 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Zhu Danqing¹^ORCID,Brookes David H.²^ORCID,Busia Akosua³^ORCID,Carneiro Ana⁴^ORCID,Fannjiang Clara^ORCID,Popova Galina⁵⁶⁷,Shin David⁵⁶⁷^ORCID,Donohue Kevin C.⁶⁸⁹¹⁰^ORCID,Lin Li F.⁴,Miller Zachary M.¹¹^ORCID,Williams Evan R.¹¹^ORCID,Chang Edward F.¹²^ORCID,Nowakowski Tomasz J.⁵⁶⁷¹⁰¹²,Listgarten Jennifer³¹³^ORCID,Schaffer David V.¹⁴¹⁴¹⁵¹⁶^ORCID

Affiliation:

1. California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA.

2. Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA.

3. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA.

4. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.

5. Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA.

6. Department of Psychiatry and Behavioural Sciences, University of California San Francisco, San Francisco, CA 94143, USA.

7. Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.

8. School of Medicine, University of California San Francisco, San Francisco, CA, USA. 94143.

9. Kavli Institute of Fundamental Neuroscience, University of California San Francisco, San Francisco, CA 94143, USA.

10. Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143, USA.

11. Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA.

12. Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA.

13. Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

14. Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA.

15. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

16. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.

Abstract

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for gene therapies. AAVs have been successfully engineered—for instance, for more efficient and/or cell-specific delivery to numerous tissues—by creating large, diverse starting libraries and selecting for desired properties. However, these starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a prerequisite for any gene delivery goal. Here, we present and showcase a machine learning (ML) method for designing AAV peptide insertion libraries that achieve fivefold higher packaging fitness than the standard NNK library with negligible reduction in diversity. To demonstrate our ML-designed library’s utility for downstream engineering goals, we show that it yields approximately 10-fold more successful variants than the NNK library after selection for infection of human brain tissue, leading to a promising glial-specific variant. Moreover, our design approach can be applied to other types of libraries for AAV and beyond.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Link

https://www.science.org/doi/pdf/10.1126/sciadv.adj3786

Reference68 articles.

1. Directed evolution of adeno-associated virus yields enhanced gene delivery vectors

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