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

View ORCID ProfileDanqing Zhu, David H. Brookes, Akosua Busia, Ana Carneiro, View ORCID ProfileClara Fannjiang, Galina Popova, David Shin, Kevin. C. Donohue, Edward F. Chang, Tomasz J. Nowakowski, View ORCID ProfileJennifer Listgarten, View ORCID ProfileDavid. V. Schaffer
doi: https://doi.org/10.1101/2021.11.02.467003
Danqing Zhu
1California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
Ph.D.
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  • ORCID record for Danqing Zhu
David H. Brookes
2Biophysics Graduate Group, University of California, Berkeley, CA, USA
Ph.D.
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Akosua Busia
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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Ana Carneiro
4Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA
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Clara Fannjiang
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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Galina Popova
5Department of Anatomy, University of California, San Francisco, CA, USA
6Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
7Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA
Ph.D.
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David Shin
5Department of Anatomy, University of California, San Francisco, CA, USA
6Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
7Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA
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Kevin. C. Donohue
6Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
8School of Medicine, University of California, San Francisco, CA, USA
9Kavli Institute of Fundamental Neuroscience, University of California, San Francisco, CA, USA
11Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
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Edward F. Chang
10Department of Neurological Surgery, University of California, San Francisco, CA, USA
MD
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Tomasz J. Nowakowski
5Department of Anatomy, University of California, San Francisco, CA, USA
6Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
7Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA
10Department of Neurological Surgery, University of California, San Francisco, CA, USA
11Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
Ph.D.
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Jennifer Listgarten
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
12Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
Ph.D.
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  • For correspondence: jennl@berkeley.edu schaffer@berkeley.edu
David. V. Schaffer
1California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
4Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA
13Department of Bioengineering, University of California, Berkeley, California, CA, USA
14Department of Molecular and Cell Biology, University of California, Berkeley, California, USA
15Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA
16Innovative Genomics Institute (IGI), University of California, Berkeley, California, USA
Ph.D.
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  • For correspondence: jennl@berkeley.edu schaffer@berkeley.edu
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Abstract

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for clinical gene therapy, but they need improvement. AAVs with enhanced properties, such as more efficient and/or cell-type specific infection, can be engineered by creating a large, diverse starting library and screening for desired phenotypes, in some cases iteratively. Although this approach has succeeded in numerous specific cases, such as infecting cell types from the brain to the lung, the starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a general prerequisite for engineering any gene delivery goal. Herein, we develop and showcase a machine learning (ML)-based method for systematically designing more effective starting libraries — ones that have broadly good packaging capabilities while being as diverse as possible. Such carefully designed but general libraries stand to significantly increase the chance of success in engineering any property of interest. Furthermore, we use this approach to design a clinically-relevant AAV peptide insertion library that achieves 5-fold higher packaging fitness than the state-of-the-art library, with negligible reduction in diversity. We demonstrate the general utility of this designed library on a downstream task to which our approach was agnostic: infection of primary human brain tissue. The ML-designed library had approximately 10-fold more successful variants than the current state-of-the-art library. Not only should our new library serve useful for any number of other engineering goals, but our library design approach itself can also be applied to other types of libraries for AAV and beyond.

Competing Interest Statement

D.Z., D.H.B., J.L., and D.V.S. are inventors on patent related to improving packaging and diversity of AAV libraries with machine learning. Jennifer Listgarten is on the Scientific Advisory Board for Foresite Labs and Patch Biosciences. David H. Brookes is currently an employee for Dyno Therapeutics. Other authors declare no competing interests.

Footnotes

  • Sections on Introduction & Results were updated to better clarify our findings; Figure 1 & 2 were updated to include schematics; Figure 4 was revised to include additional control group; Figure 6 was updated to include additional cell-type infection results.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 15, 2022.
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Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy
Danqing Zhu, David H. Brookes, Akosua Busia, Ana Carneiro, Clara Fannjiang, Galina Popova, David Shin, Kevin. C. Donohue, Edward F. Chang, Tomasz J. Nowakowski, Jennifer Listgarten, David. V. Schaffer
bioRxiv 2021.11.02.467003; doi: https://doi.org/10.1101/2021.11.02.467003
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Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy
Danqing Zhu, David H. Brookes, Akosua Busia, Ana Carneiro, Clara Fannjiang, Galina Popova, David Shin, Kevin. C. Donohue, Edward F. Chang, Tomasz J. Nowakowski, Jennifer Listgarten, David. V. Schaffer
bioRxiv 2021.11.02.467003; doi: https://doi.org/10.1101/2021.11.02.467003

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