HAI Seminar with Brian Hie: Genome Modeling & Design Across All Domains of Life

Quick Overview

Dr. Brian He introduced EVO 2, a genomic foundation model trained on 9.3 trillion DNA base pairs across all domains of life, demonstrating its capability to generate novel, functional biological sequences like CRISPR systems and complete bacterial phage genomes that are significantly divergent from natural counterparts.

Key Points: Dr. He introduced EVO 2, a genomic foundation model scaled to 40 billion parameters, trained on 9 trillion DNA base pairs from bacteria, archaea, and eukaryotes, allowing it to process input sequences up to one million base pairs long. The team successfully used supervised fine-tuning on EVO 1 to generate a functional CRISPR-Cas system whose AI-generated Cas9 protein cleaved double-stranded DNA in vitro as effectively as the state-of-the-art SpCas9, achieving less than 80% sequence identity to anything in nature. Using prompt engineering with EVO, researchers generated evolutionarily novel anti-CRISPR proteins, experimentally confirming their activity, with some generated sequences showing no significant sequence identity above random null to any gene observed in nature. Inference time guidance allowed the model to write Morse code patterns into synthetic chromatin accessibility in mouse embryonic stem cells, achieving good agreement between computationally predicted designs and experimental ATAC-seq results. By applying pre-training, fine-tuning, prompt engineering, and inference guidance, the team designed viable, complete genomes for bacteriophage $\Phi$X174 variants; one generated phage showed less than 95% sequence identity to any phage in nature and qualified as a new species under some taxonomic thresholds. The generated phage cocktail rapidly overcame bacterial resistance when tested against E. coli C strains resistant to the wild-type $\Phi$X174, demonstrating resilience potential for phage therapy. Dr. He stressed that computational bottlenecks are currently less severe than experimental bottlenecks, noting that synthesizing and testing complete, large genomes remains the biggest challenge for achieving whole-organism design.