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Machine-learning how to create better AAV gene delivery vehicles


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IMAGE: In their machine learning-based capsid diversification strategy, the team focused on a 28 amino acid peptide within a segment of the AAV2 VP3 capsid protein that exposes the AAV capsid.
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Credit: Wyss Institute at Harvard University (original by Drew Bryant)
(Boston) Adeno-associated viruses (AAVs) have become promising vehicles for delivering gene therapies to defective tissues in the human body because they are non-pathogenic and can transfer therapeutic DNA into target cells. However, while the first gene therapy products approved by the Federal Drug Administration (FDA) use AAV vectors and others are likely to follow, AAV vectors still have not reached their full potential to meet gene therapeutic challenges. ....

United States , Boston University , University Of Massachusetts Medical School , Massachusetts Institute Of Technology , United Kingdom , Dana Farber Cancer Institute , Tufts University , Spaulding Rehabilitation Hospital , Ali Bashir , Eric Kelsic , Donald Ingber , Benjamin Boettner , Ninak Jain , Beth Israel Deaconess Medical Center , University Of Cambridge , Google Research , Wyss Institute , Wyss Institute Synthetic Biology , George Church Synthetic Biology , Lucyj Colwell At Google Research , Health Sciences , University Of Zurich , Drug Administration , Professor Of Genetics At Harvard Medical School , Arts Sciences , Harvard Wyss Institute For Biologically Inspired Engineering ,