If you have an ACS member number, please enter it here so we can link this account to your membership. (optional)

ACS values your privacy. By submitting your information, you are gaining access to C&EN and subscribing to our weekly newsletter. We use the information you provide to make your reading experience better, and we will never sell your data to third party members.



George Church lab spin-off Dyno Therapeutics to use machine learning for gene therapy

A new publication from the start-up’s founders highlights the potential of machine learning for adeno-associated virus (AAV) design

by Ryan Cross
November 30, 2019 | A version of this story appeared in Volume 97, Issue 47


A model of AAV.
Credit: Eric Kelsic/Dyno Therapeutics
A model of the surface (left) and cross-section (right) of AAV showing the amino acids that can be changed to improve (red) or hinder (blue) production of the virus.

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 to the liver. A collection of viruses designed with this method performed better than viruses with random mutations. But making multiple changes broke most of the AAVs and made them nonfunctional (Science 2019, DOI: 10.1126/science.aaw2900).

“What they’ve done here is truly a remarkable tour de force,” says Luk Vandenberghe, director of the Grousbeck Gene Therapy Center at Massachusetts Eye and Ear. The study highlights the potential of machine learning for AAV design, he adds, though it falls short of actually designing a significantly improved AAV that is ready for prime time in clinical testing.

Eric Kelsic, who as a postdoc worked on the project in Church’s lab, is Dyno’s CEO. He says the project gives “a snapshot of what’s important, and it’s a starting point for engineering capsids in a principled way.” Dyno is now applying machine learning to design better capsids.



This article has been sent to the following recipient:

Chemistry matters. Join us to get the news you need.