Nature research paper: Semantic design of functional de novo genes from a genomic language model go.nature.com/48uEnAn

Semantic design of functional de novo genes from a genomic language model - Nature

By learning a semantics of gene function based on genomic context, the genomic language model Evo autocompletes DNA prompts to generate novel genes encoding protein and RNA molecules with defined activities, whose sequences generalize beyond those found in nature.

go.nature.com

Today in @nature.com, in work led by @adititm.bsky.social, we report the ability to prompt Evo to generate functional de novo genes. You shall know a gene by the company it keeps!

Published today in @nature.com, @adititm.bsky.social & researchers from the @brianhie.bsky.social lab report that the large-scale genomic model, Evo, is capable of using surrounding genomic context to produce novel, functional genes, enabling an an emergent approach they've termed 'semantic design'.

What if we could autocomplete DNA based on function? Today in @Nature, we share semantic design—a strategy for function-guided design with genomic language models that leverages genomic context to create de novo genes and systems with desired functions. 🧵 www.nature.com/articles/s41...

New @biorxiv-synthbio.bsky.social on #Evo 👀⤵️🧵 introducing Evo 1.5 for semantic mining + SynGenome - an AI-generated genomics database #AI #synbio #LLM🧬 @adititm.bsky.social @brianhie.bsky.social et al. @arcinstitute.org

Aditi Merchant@adititm.bsky.social · 2y ago

Excited to have the first project of my PhD out!! By leveraging genomic language model Evo’s ability to learn relationships across genes (i.e., "know a gene by the company it keeps"), we show that we can use prompt-engineering to generate highly divergent proteins with retained functionality. 🧵1/N

Excited to have the first project of my PhD out!! By leveraging genomic language model Evo’s ability to learn relationships across genes (i.e., "know a gene by the company it keeps"), we show that we can use prompt-engineering to generate highly divergent proteins with retained functionality. 🧵1/N