Read the full preprint below 👇 If you're interested in the interface of bioengineering, DNA and virtual cell foundation models, and agentic reasoning, shoot me a note. We're hiring postdocs and ML researchers and starting some crazy new projects

www.biorxiv.org/content/10.1...

This work was a wonderful collaboration with Silvana Konermann, led by star graduate student Nick Perry with key contributions from the amazing Liam Bartie, Dhruva Katrekar, Gabe Gonzalez, Matt Durrant, James Pai, Alison Fanton, Masa Hiraizumi, Chiara Ricci-Tam, and Hiroshi Nishimasu

Arc is on 🔥

Bridge recombinases can modify the genome from single gene insertions to megabase-sized rearrangements

We're excited about programmable genome design at unprecedented length scales, especially when combined with AI-generated DNA sequences of high complexity (e.g. Evo 2)

Most people think of recombinases for payload insertion (e.g. of CARs or corrective genes)

We provide a therapeutic proof-of-concept with bridge-mediated excision of the BCL11A enhancer for sickle cell anemia and of expanded repeat sequences found in Friedreich's ataxia

But unlike other tools, bridge editing is not limited to insertion! We use IS622 for programmable, precise, and scarless genome rearrangements, inverting up to 0.93 Mb and excising up to 0.13 Mb

We then performed a systematic deep mutational scan of IS622 and combined a rationally engineered, high activity recombinase mutant with our enhanced bridge RNAs to demonstrate 20% insertion efficiency into the human genome

Using these enhanced bridge RNAs, we discovered design principles for maximizing the specificity of insertion into the human genome, achieving as high as 82% specificity genome-wide

In a tour de force of molecular engineering, our team conducted computational ortholog mining, human cell activity screening, and structure-guided bridge RNA engineering to enhance the activity of IS622, a bridge system that showed promising but low activity in human cells

Bridge recombination systems are elegant molecular tools that utilize a recombinase enzyme and a programmable bridge RNA to "bridge" and recombine two distinct DNA molecules

This is a universal mechanism for insertion, excision, or inversion of any two DNA sequences

This was an insane team effort between Arc and Nvidia that convened machine learning and computational biology researchers across Stanford, UC Berkeley, and UCSF. Especially grateful to Jensen Huang for his belief and support of this vision and labor of love, and the entire Evo 2 team below

Finally, if Evo 2 sounded exciting, @arcinstitute.org
is hiring. Check out open Arc jobs at arcinstitute.org/jobs or just email me directly. Our research group is hiring in molecular machine learning and the interface of computational and synthetic biology

and check out our blog post! arcinstitute.org/news/blog/evo2

and a few more :)

NVIDIA BioNeMo: github.com/NVIDIA/bione...
NVIDIA NIM (Generation): build.nvidia.com/nvidia/evo2-...
NVIDIA NIM (Forward): build.nvidia.com/arc/evo2-40b
HuggingFace Evo 2 40B: huggingface.co/arcinstitute...
HuggingFace Evo 2 7B: huggingface.co/arcinstitute...

Here are some useful links:

Evo 2 preprint: arcinstitute.org/manuscripts/...
Evo Designer: arcinstitute.org/tools/evo/ev...
Evo Mech Interp Visualizer: arcinstitute.org/tools/evo/ev...
Evo 2 code: github.com/arcinstitute...

DNA is just the beginning. In middle school, we learn that genotype and the environment collaborate to create phenotype. We are incorporating Evo 2's understanding of genetic variation into Arc's virtual cell models that can be used for drug discovery and target ID

We're excited to see what the research community builds on top of this foundation model to enable the biological "app store"

Beyond pretraining scale, Evo 2 also scales at inference time. We demonstrate "generative epigenomics" by controlling the position and width of predicted chromatin accessibility to encode Morse code messages in the epigenome. Can you guess what's written below? .- .-. -.-.