Megabase-scale human genome rearrangement with programmable bridge recombinases Bridge recombinases are a class of naturally occurring RNA-guided DNA recombinases. We previously demonstrated they can programmably insert, excise, and invert DNA in vitro and in bacteria. Here, we r...

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

Genomes encode biological complexity, which is determined by combinations of DNA mutations across millions of bases

In new work @arcinstitute.org, we report the discovery and engineering of the first programmable DNA recombinases capable of megabase-scale human genome rearrangement

The Endpoints Slack interview: Arc Institute's Patrick Hsu on investing, AI and new ways of research An interview with Patrick Hsu, 31, Harvard PhD grad and CRISPR pioneer, who co-founded Arc Institute and helped develop Evo 2, a biology-focused AI model.

This was a fun one — new Endpoints Slack interview with @pdhsu.bsky.social :

endpts.com/endpoints-sl...

are there good CROs for cell line engineering and generation?

Arc Virtual Cell Atlas launches, combining data from over 300 million cells | Arc Institute Arc Institute today launched the Arc Virtual Cell Atlas, a growing resource for computation-ready single-cell measurements, starting with data from over 300 million cells. The initial release of the A...

Today, we're launching the Arc Virtual Cell Atlas, a growing resource for computation-ready single-cell measurements. arc-website-git-ben-virtual-cell-atlas-tool-arc-institute.vercel.app/news/news/ar...

At the @arcinstitute.org we are building AI models of cell state from the ground up, rethinking every step, from data generation to biologically relevant evaluation

Today we launch scBaseCamp, the largest public repository of single cell RNAseq data, uniformly processed from raw sequencing reads.

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling Building predictive models of the cell requires systematically mapping how perturbations reshape each cell's state, function, and behavior. Here, we present Tahoe-100M, a giga-scale single-cell atlas ...

@thejohnnyyu.bsky.social, @therealnima.bsky.social, and I, are excited to tell you about Tahoe-100M! The largest publicly available single-cell dataset that measures the effect of 1200 genes on 50 cell line models. The Vevo team has outdone itself. #Tahoe100M www.biorxiv.org/content/10.1...

YouTube video by No Priors: AI, Machine Learning, Tech, & Startups No Priors Ep. 103 | With Vevo Therapeutics and the Arc Institute

Watch @thejohnnyyu.bsky.social @therealnima.bsky.social (@vevotherapeutics.bsky.social), @pdhsu.bsky.social , Dave Burke and I (@arcinstitute.org) talking about virtual cells, and how #Tahoe100M, now on. @arcinstitute.org's Virtual Cell Atlas, can change the game!

www.youtube.com/watch?v=ak_f...

Arc Institute’s AI Model Evo 2 Designs the Genetic Code Across All Domains of Life Evo 2 now includes information from humans, plants, and other eukaryotic species to expand its capabilities in generative functional genomics.

New from @arcinstitute.org is "the largest publicly available #AI model for biology to date"!

Evo 2 now includes information from all domains in life to expand its capabilities in generative functional genomics. @pdhsu.bsky.social @brianhie.bsky.social

tinyurl.com/3t83vseh

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

AI can now model and design the genetic code for all domains of life with Evo 2 | Arc Institute Arc Institute develops the largest AI model for biology to date in collaboration with NVIDIA, bringing together Stanford University, UC Berkeley, and UC San Francisco researchers

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

GitHub - NVIDIA/bionemo-framework: BioNeMo Framework: For building and adapting AI models in drug discovery at scale BioNeMo Framework: For building and adapting AI models in drug discovery at scale - NVIDIA/bionemo-framework

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...

Manuscript | Arc Institute Arc Institute is a independent nonprofit research organization headquartered in Palo Alto, California.

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? .- .-. -.-.

Evo 2 can also be used for biological design. We demonstrate generation of entire human mitochondrial genomes with coherent synteny and even whole bacterial genomes and eukaryotic chromosomes (see the preprint for more detail)

A common critique of LLMs is that they're black box. To probe what Evo 2 is learning about biology (without any labels or annotations), we turned to mechanistic interpretability with Goodfire AI

Intriguingly, this AI brain has features that may correspond to regulatory elements

With a simple supervised model trained on Evo 2 embeddings, its performance gets even better, reaching SOTA for coding mutations also

Without any variant-specific training, architectural optimization, or multiple sequence alignments, Evo 2 can predict the pathogenicity of breast cancer-associated mutations in genes like BRCA1

It's state of the art in doing this zero-shot for noncoding mutations

Great, but what can it do? Evo 2 is a generalist model that can predict the pathogenic effects of human genome variants across coding and noncoding mutations

In other words, if you have a genetic mutation, Evo 2 has an opinion on whether or not it might cause disease