Machine-learning-guided Directed Evolution for AAV Capsid Engineering


Дәйексөз келтіру

Толық мәтін

Аннотация

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.

Авторлар туралы

Xianrong Fu

School of Artificial Intelligence, Hangzhou Dianzi University

Email: info@benthamscience.net

Hairui Suo

School of Artificial Intelligence, Hangzhou Dianzi University

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Jiachen Zhang

School of Artificial Intelligence, Hangzhou Dianzi University

Email: info@benthamscience.net

Dongmei Chen

School of Artificial Intelligence, Hangzhou Dianzi University

Email: info@benthamscience.net

Әдебиет тізімі

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