Computationally guided AAV engineering for enhanced gene delivery
Copyright © 2024 Elsevier Ltd. All rights reserved..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
Trends in biochemical sciences - (2024) vom: 25. März |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Guo, Jingxuan [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
AAV libraries |
|---|
| Anmerkungen: |
Date Revised 26.03.2024 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1016/j.tibs.2024.03.002 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370212487 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370212487 | ||
| 003 | DE-627 | ||
| 005 | 20240328000754.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240328s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.tibs.2024.03.002 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1351.xml |
| 035 | |a (DE-627)NLM370212487 | ||
| 035 | |a (NLM)38531696 | ||
| 035 | |a (PII)S0968-0004(24)00054-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Guo, Jingxuan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Computationally guided AAV engineering for enhanced gene delivery |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 26.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a AAV libraries | |
| 650 | 4 | |a ancestral sequence reconstruction | |
| 650 | 4 | |a directed evolution | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a next-generation sequencing | |
| 650 | 4 | |a protein engineering | |
| 700 | 1 | |a Lin, Li F |e verfasserin |4 aut | |
| 700 | 1 | |a Oraskovich, Sydney V |e verfasserin |4 aut | |
| 700 | 1 | |a Rivera de Jesús, Julio A |e verfasserin |4 aut | |
| 700 | 1 | |a Listgarten, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Schaffer, David V |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Trends in biochemical sciences |d 1982 |g (2024) vom: 25. März |w (DE-627)NLM01261078X |x 0968-0004 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:25 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.tibs.2024.03.002 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 25 |c 03 | ||