Evidence map›Paper›PMID 38531696›Full record

ReviewTrends in biochemical sciences2024

Computationally guided AAV engineering for enhanced gene delivery.

Jingxuan Guo, Li F Lin, Sydney V Oraskovich, Julio A Rivera de Jesús, Jennifer Listgarten, David V Schaffer

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Trends in biochemical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Tissue-specific gene delivery approaches.Bioengineering & translational medicine · 2026
    Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Advances in AAV capsid engineering: Integrating rational design, directed evolution and machine learning.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
  12. ABI and generative biology: A new paradigm for gene therapy, genome engineering, and engineered cell therapy.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Article
  13. Review
  14. Gene Therapy Techniques and Delivery Methods (Review).Sovremennye tekhnologii v meditsine · 2025
    Review
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jingxuan GuoCalifornia Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA.
Li F LinDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA.
Sydney V OraskovichDepartment of Bioengineering, University of California, Berkeley, CA 94720, USA; Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA.
Julio A Rivera de JesúsDepartment of Bioengineering, University of California, Berkeley, CA 94720, USA; Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA; Department of Neurological Surgery, University of California, San Francisco, CA 94143, USA.
Jennifer ListgartenDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA.
David V SchafferCalifornia Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA; Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA; Department of Bioengineering, University of California, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA. Electronic address: schaffer@berkeley.edu.

Funding

Directed Evolution of Novel AAVs and Regulatory Elements for Selective Microglial Gene ExpressionR01NS126397 · NINDS · UNIVERSITY OF CALIFORNIA BERKELEY · PI Tomasz Nowakowski, DAVID V SCHAFFER · 2023 to 2026
$3.2M
Machine Learning Augmented Discovery of AAV Capsids for Cell Type Specific Access into Human Neurons and GliaUF1MH130700 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI NOWAKOWSKI, TOMASZ, SCHAFFER, DAVID V · 2022 to 2022
$2.9M
Biology and Biotechnology of Cell and Gene TherapyT32GM139780 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI Amy Elizabeth Herr, Dirk Hockemeyer · 2021 to 2026
$2.4M
NIGMS NIH HHS T32 GM139780NIMH NIH HHS UF1 MH130700NINDS NIH HHS R01 NS126397
6 · The paper itself

Abstract

Gene delivery vehicles based on adeno-associated viruses (AAVs) are enabling increasing success in human clinical trials, and they offer the promise of treating a broad spectrum of both genetic and non-genetic disorders. However, delivery efficiency and targeting must be improved to enable safe and effective therapies. In recent years, considerable effort has been invested in creating AAV variants with improved delivery, and computational approaches have been increasingly harnessed for AAV engineering. In this review, we discuss how computationally designed AAV libraries are enabling directed evolution. Specifically, we highlight approaches that harness sequences outputted by next-generation sequencing (NGS) coupled with machine learning (ML) to generate new functional AAV capsids and related regulatory elements, pushing the frontier of what vector engineering and gene therapy may achieve.

Indexed as

DependovirusGene Transfer TechniquesAnimalsComputational BiologyGenetic EngineeringGenetic TherapyGenetic VectorsHumansAAV librariesancestral sequence reconstructiondirected evolutionmachine learningnext-generation sequencingprotein engineering

Identifiers

PMID38531696
PMCPMC11456259

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.