Shelly Fan in Singularity Hub:
CRISPR has a problem: an embarrassment of riches.
Ever since the gene editing system rocketed to fame, scientists have been looking for variants with better precision and accuracy. One search method screens for genes related to CRISPR-Cas9 in the DNA of bacteria and other creatures. Another artificially evolves CRISPR components in the lab to give them better therapeutic properties—like greater stability, safety, and efficiency inside the human body. This data is stored in databases containing billions of genetic sequences. While there may be exotic CRISPR systems hidden in these libraries, there are simply too many entries to search. This month, a team at MIT and Harvard led by CRISPR pioneer Dr. Feng Zhang took inspiration from an existing big-data approach and used AI to narrow the sea of genetic sequences to a handful that are similar to known CRISPR systems.
The AI scoured open-source databases with genomes from uncommon bacteria—including those found in breweries, coal mines, chilly Antarctic shores, and (no kidding) dog saliva. In just a few weeks, the algorithm pinpointed thousands of potential new biological “parts” that could make up 188 new CRISPR-based systems—including some that are exceedingly rare. Several of the new candidates stood out. For example, some could more precisely lock onto the target gene for editing with fewer side effects. Other variations aren’t directly usable but could provide insight into how some existing CRISPR systems work—for example, those targeting RNA, the “messenger” molecule directing cells to build proteins from DNA.
More here.