Jeff Ruffolo

Today we’re announcing $106M in new funding led by Altimeter Capital and Bezos Expeditions. This brings our total to $150M to scale our frontier AI models which make biology programmable. Our frontier models have generated functional proteins (Nature Biotech, 2023), created the first CRISPR system designed from scratch (Nature, 2025), and showed clear scaling behavior (NeurIPS spotlight, 2025). The opportunities ahead are unimaginable. If you’re excited by shaping the future of biology – join us in pushing the science forward. Forbes: forbes.com/sites/amyfeldm… Press Release: businesswire.com/news/home/2025… -- Nature Biotech, 2023: nature.com/articles/s4158… NeurIPS spotlight, 2025: biorxiv.org/content/10.110… Nature, 2025: nature.com/articles/s4158…

AI can bring high quality genetic tools within reach for rare diseases. Today, Profluent is partnering with the Rett Syndrome Research Trust (RSRT) to design personalized genomic medicines for Rett syndrome. Our aim is to engineer compact editors that can fit into a single AAV delivery capsid, a key step toward reaching the central nervous system. Once there, our editors will need to precisely correct recurrent MECP2 hotspot mutations that drive Rett syndrome pathology. Rare disease used to mean rare attention. Specialized genetic tools were reserved only for the most common conditions. AI is changing that, bringing state-of-the-art biotechnology to a rare neurodevelopmental disorder that touches families every day. Profluent and RSRT are excited to bring the ProGen3 model and the full strength of our platform to bear on Rett syndrome. We’re working relentlessly toward a future when AI helps to make the best science accessible to all patients. 🔗 reverserett.org/news/articles/…

Protein language models just got an upgrade. Meet Profluent-E1: a free, flexible, frontier protein sequence encoder. E1 is built with retrieval augmentation to learn from multiple sequences. Models trained over 4T tokens with only 150M-600M params, E1 is SOTA for zero-shot functional and unsupervised structural tasks. It raises the bar for protein representation learning and is freely available today.

Learn more about E1 and start using the model: Paper: biorxiv.org/content/10.110… Blog: profluent.bio/showcase/e1 Code: github.com/Profluent-AI/e1 Congrats to @successar_nlp and the rest of the team @AadyotB @daggerlog @thisismadani on the release!

These evaluations are just the beginning. At @ProfluentBio, we’ve already found E1 to be a compelling alternative to existing encoders. Our hope in releasing these models is for others to boost their own workflows and provide feedback that helps us continue to push the frontier.

I’m excited to share Profluent-E1, our internal encoder model for protein sequences, with the protein-ML community! The E1 series of models can be used as a drop-in replacement to upgrade a broad range protein modeling and design of workflows. biorxiv.org/content/10.110…

I don’t know about that…

Biology gets its scaling laws too. @ProfluentBio's ProGen3 trained on 1.5T tokens and created a compute frontier for protein language models. This is unlocking generalisation in novel protein space and a path to novel therapeutics such as custom gene editors.

We’re excited to announce a multi-year partnership between Profluent Bio and @Corteva Agriscience to accelerate sustainable, AI-powered crop innovation. 🌱🤝 Together, we aim to unlock new possibilities for developing resilient crops, improving resource efficiency, and advancing global sustainability goals. This partnership reflects the transformative potential of AI in biology—delivering real-world solutions for farmers and communities worldwide. 🔗 businesswire.com/news/home/2025… #AI #Agriculture #Sustainability #GeneEditing

We are excited to announce that our work on ProGen3 has been accepted as a Spotlight paper at NeurIPS 2025 – the premier AI research conference. This year NeurIPS received 21,575 valid submissions, of which only 3.2% earned a Spotlight distinction. We’re thrilled that ProGen3 is among them. 🎉 ProGen3 is a family of generative protein language models, scaled up to 46B parameters, trained on our curated Profluent Protein Atlas which continues to grow as the largest protein sequence data resource in the world. It brings compute-optimal scaling, sparse architectures, and alignment with experimental data into the domain of protein generation.

Democratized access to CRISPR just got a major boost! @ProfluentBio’s OpenCRISPR-1 AI-created, open-access alternative to Cas9, is now peer-reviewed in Nature. And we’ve already shown it works with our licensable Pin-point™ base editing system. Read our blog: ms.spr.ly/6010suoqG Read the paper: ms.spr.ly/6011suoqH

We’re excited to share new data published in @Nature detailing the impressive activity, specificity, and low immunogenicity of our AI-designed CRISPR-Cas proteins, including OpenCRISPR-1. The future of gene editing is here and we’re scaling our capabilities to tackle the hardest problems that will unlock new medicines.

@anshulkundaje Our view is that we should monitor this model behavior (and other things like unsupervised structure learning) but focus more on teasing apart what the models are actually learning directly as they scale in order to best inform how they should be used.

@anshulkundaje At smaller scales we might learn the fundamentals that are reflected in ProteinGym, but the likelihoods from larger models might reflect more nuanced understanding. If so, then we can then use techniques like alignment to draw out particular aspects of protein understanding.

What does pushing the boundaries of model capacity and data scale do for generative protein language models? I’m super excited to share our latest work @ProfluentBio where we begin to explore and test some of our hypotheses!