Gene therapy could potentially cure genetic diseases but it remains a challenge to package and deliver new genes to specific cells safely and effectively. Existing methods of engineering one of the most commonly used gene-delivery vehicles, adeno-associated viruses (AAV), are often slow and inefficient. Now, researchers have developed a machine-learning approach that promises to speed up AAV engineering for gene therapy. The tool helps researchers engineer the protein shells of AAVs, called capsids, to have multiple desirable traits, such as the ability to deliver cargo to a specific organ but not others or to work in multiple species. Other methods only look for capsids that have one trait at a time.
