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Gene Therapy

Gene Therapy

Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...

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Related Experiment Video

Updated: Jun 29, 2025

Isolation of Next-Generation Gene Therapy Vectors through Engineering, Barcoding, and Screening of Adeno-Associated Virus AAV Capsid Variants

Isolation of Next-Generation Gene Therapy Vectors through Engineering, Barcoding, and Screening of Adeno-Associated Virus AAV Capsid Variants

Published on: October 18, 2022

Computationally guided AAV engineering for enhanced gene delivery.

Jingxuan Guo¹, Li F Lin², Sydney V Oraskovich³

¹California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA.

Trends in Biochemical Sciences

|March 26, 2024

Summary

This summary is machine-generated.

Computational approaches, including machine learning, are enhancing adeno-associated virus (AAV) gene therapy vectors. This research explores how designed AAV libraries improve delivery efficiency for genetic and non-genetic disorders.

Keywords:

AAV libraries ancestral sequence reconstruction directed evolution machine learning next-generation sequencing protein engineering

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AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

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Published on: March 31, 2022

Area of Science:

Biotechnology
Gene Therapy
Bioinformatics

Background:

Adeno-associated viruses (AAVs) are promising gene delivery vehicles for treating genetic and non-genetic disorders.
Current AAV therapies require improved delivery efficiency and targeting for enhanced safety and efficacy.

Purpose of the Study:

To review the role of computationally designed AAV libraries in directed evolution.
To highlight methods combining next-generation sequencing (NGS) and machine learning (ML) for AAV engineering.

Main Methods:

Harnessing next-generation sequencing (NGS) data.
Utilizing machine learning (ML) algorithms for AAV variant design.
Employing directed evolution strategies with computational libraries.

Main Results:

Computationally designed AAV libraries facilitate the generation of novel functional AAV capsids.
Integration of NGS and ML accelerates the discovery of improved AAV vectors.
Advancements in AAV engineering are pushing the boundaries of gene therapy.

Conclusions:

Computational design and directed evolution are key to advancing AAV-based gene therapies.
Machine learning and NGS integration offer powerful tools for optimizing AAV vector performance.
Improved AAV vectors hold significant potential for treating a wider range of diseases.