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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Review Article

Machine-learning-guided Directed Evolution for AAV Capsid Engineering

Author(s): Xianrong Fu, Hairui Suo*, Jiachen Zhang and Dongmei Chen

Volume 30, Issue 11, 2024

Published on: 05 March, 2024

Page: [811 - 824] Pages: 14

DOI: 10.2174/0113816128286593240226060318

Price: $65

Abstract

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.

Keywords: Gene therapy, vector, adeno-associated virus, capsid, directed evolution, machine learning.

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