Dyno to use machine learning for AAV design

Abstract

Adeno-associated viruses (AAVs) are the DNA-delivery vehicle of choice for many gene-therapy companies, but they’re not perfect. Many past attempts to engineer improved AAVs have failed, because the protein shell of the virus—the capsid—is like a Rubik’s Cube. Improving one feature often throws others out of line. A new study suggests that machine learning could help solve that molecular puzzle. And a new start-up, Dyno Therapeutics, has been quietly raising money to test the concept on an industrial scale. A team led by George Church at Harvard Medical School created all possible single codon mutations—substitutions, deletions, and insertions—in the capsid of an AAV variant called AAV2. The team then tested how the mutations altered the AAVs’ immunogenicity, thermostability, ability to multiply in cells, and distribution to different tissues in mice. With all this data in hand, the team set out to introduce multiple mutations in AAVs to improve their delivery