Jingxuan Guo , Li F. Lin , Sydney V. Oraskovich , Julio A. Rivera de Jesús , Jennifer Listgarten , David V. Schaffer
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引用次数: 0
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.
期刊介绍：
For over 40 years, Trends in Biochemical Sciences (TIBS) has been a leading publication keeping readers informed about recent advances in all areas of biochemistry and molecular biology. Through monthly, peer-reviewed issues, TIBS covers a wide range of topics, from traditional subjects like protein structure and function to emerging areas in signaling and metabolism. Articles are curated by the Editor and authored by top researchers in their fields, with a focus on moving beyond simple literature summaries to providing novel insights and perspectives. Each issue primarily features concise and timely Reviews and Opinions, supplemented by shorter articles including Spotlights, Forums, and Technology of the Month, as well as impactful pieces like Science & Society and Scientific Life articles.
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