DNA insertion without breaks; plus diffusion-based approaches to protein design and more

A team led by Omar Abudayyeh and Jonathan Gootenberg at  Massachusetts Institute of Technology described in Nature Biotechnology a genomic insertion technique, dubbed PASTE (programmable addition via site-specific targeting elements), which uses a CRISPR–Cas9 nickase fused to both a reverse transcriptase and serine integrase for targeted genomic integration of large DNA sequences. The method marries the advantage of a prime editor, which doesn’t cause DNA double-strand breaks and therefore avoids possible undesirable outcomes of DNA damage, with site-specific integrases that allow precise insertion of large DNA sequences.

In separate preprints published Dec. 1, Generate Biomedicines Inc. and David Baker's lab from the Institute for Protein Design (IPD) at University of Washington each described generative methods to design de novo proteins that don't exist in nature using diffusion models, a rapidly expanding field of deep learning known for powering image-generating algorithms such as DALL-E. Generate, which raised one of the largest venture rounds of 2021, dubbed its approach Chroma; the IPD, which has spun out multiple protein design-based start-ups, called its method RoseTTAFold Diffusion, and experimentally characterized hundreds of designs including a picomolar-affinity binder to parathyroid hormone. Both groups have been at the forefront of the recent shift in computational protein design that is moving away from mechanistic calculations based on physical forces in favor of more powerful machine learning approaches...