Machine-learning-guided Directed Evolution for AAV Capsid Engineering
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Current pharmaceutical design - (2024) vom: 05. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fu, Xianrong [VerfasserIn] |
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| Links: |
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| Themen: |
Adeno-associated virus |
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| Anmerkungen: |
Date Revised 06.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.2174/0113816128286593240226060318 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adeno-associated virus | |
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| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Vector | |
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| 700 | 1 | |a Zhang, Jiachen |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Dongmei |e verfasserin |4 aut | |
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