Tristan Bepler

Boltz-1 & Boltz-2 now live via GUI & APIs! Predict protein, protein–RNA/DNA/ligand structures with confidence scores & binding affinity metrics for virtual screening. Compare finetuned models in the new overview page to find your best performer fast.
www.openprotein.ai/early-access...

Product update: Indel Analysis lets you score insertions/deletions across your sequence using PoET-2. You can now also compare multiple 3D structures in Mol* to evaluate design alternatives.
Sign up now: www.openprotein.ai/early-access...

How would you use a tool like this? Do you design or screen indels in your work? 4/4

PoET-2 is state-of-the-art for zero-shot variant effect prediction of both DMS indels and clinical indels in ProteinGym.

Indels are still a major challenge for variant effect prediction and protein design. PoET-2 has significantly improved the state-of-the-art for functional and clinical indel variant effect prediction. 3/4

It supports screening deletions, insertion sites, and replacement sites. Explore viable shortened proteins, or insert new structural or functional sequences like localization signals or structural tags. 2/4

Why does no one in AI protein engineering work on indels?

We’re solving this at OpenProtein.AI. Check out our upcoming indel design tool! 🤩 1/4

@openprotein.bsky.social

Have we hit a "scaling wall" for protein language models? 🤔 Our latest ProteinGym v1.3 release suggests that for zero-shot fitness prediction, simply making pLMs bigger isn't better beyond 1-4B parameters. The winning strategy? Combining MSAs & structure in multimodal models!

Great to see this comparison with genome language models. The hype around these models seems to have strongly outstripped where they actually are in comparison with protein models.

Product update: PoET-2 now supports structure inputs for enhanced prediction and design via Python APIs. Check out our new inverse folding tutorial to see it in action.
🔗 docs.openprotein.ai/walkthroughs...

Sign up for OpenProtein.AI: www.openprotein.ai/early-access...

Huge thanks to the @openprotein.bsky.social team! We've got more exciting PoET-2 updates to come 🚀

A multimodal foundation model for controllable protein generation and representation learning PoET-2 is a next generation protein language model that transforms our ability to engineer proteins by learning from nature’s design principles. Through its unique multimodal architecture and training...

Learn more about PoET-2 in our whitepaper: www.openprotein.ai/a-multimodal...

Sign up for early access | OpenProtein.AI Join the revolution in protein research with early access to our cutting-edge Open Protein AI platform. Sign up now to explore the future of protein analysis and discovery.

Sign up for OpenProtein.AI (free for academic use): www.openprotein.ai/early-access...

and install the python client to get started: github.com/OpenProteinA...

Generative protein sequence design, variant effect prediction, and fine-tuning are now fully supported for PoET-2 with structure and sequence prompts in the @openprotein.bsky.social python client and APIs!

Check out our new walkthrough on inverse folding: docs.openprotein.ai/walkthroughs...

Protein Revolution: The Tiny Model Making a Massive Impact - SynBioBeta

🧬 Protein Revolution: The Tiny Model Making a Massive Impact!
PoET-2 is changing the game in computational protein design, slashing experimental data needs by 30x! 🚀

learn more: www.synbiobeta.com/read/protein...

#ProteinDesign #BiotechInnovation #AIRevolution

Huge thanks to our incredible team @openprotein.bsky.social, especially Tim Truong. This is just the beginning of AI systems that truly understand protein biology.

I can’t wait to see what the community can do with these models! 13/13

A multimodal foundation model for controllable protein generation and representation learning PoET-2 is a next generation protein language model that transforms our ability to engineer proteins by learning from nature’s design principles. Through its unique multimodal architecture and training...

Read more in our white paper: www.openprotein.ai/a-multimodal...
12/13

This has huge implications for protein engineering - from more efficient directed evolution and multiproperty optimization to de novo protein design. 11/13

Most importantly, PoET-2 gets us closer to understanding the sequence-structure-function relationship - learning from just a handful of examples to predict properties and design new sequences. 10/13

Beyond predictions, PoET-2 introduces a powerful prompt grammar for protein generation. One model for: free sequence generation, inverse folding, motif scaffolding, and more! 9/13

The results show PoET-2 has learned fundamental principles:
* Improves sequence and structure understanding
* Accurate zero-shot function prediction, especially for insertions and deletions
* 30x less data needed for transfer learning
8/13

This lets us break conventional scaling laws. PoET-2 achieves with 182M parameters what would require trillion-parameter models using standard architectures. 7/13

Enabled by our tiered attention structure, PoET-2 processes sequence families with order equivariance while preserving long-range dependencies within and between sequences, enabling processing of large sequence families with optional structural data. 6/13

Rather than regurgitating databases, PoET-2 "meta-learns" evolutionary principles through in-context learning - inferring structural and functional constraints at inference time from small numbers of examples. 5/13

PoET-2 takes a different approach. Instead of massive scale, we developed a multimodal architecture that learns to reason about sequences, structures, and evolutionary relationships simultaneously. 4/13

But memorizing sequences isn't enough. The real challenge: can we build models that learn the fundamental principles that govern how proteins evolve and function? 3/13

Since our first protein language models in 2019 (in Bonnie's lab!), the field has focused on scale - building ever-larger models up to 100B parameters to extract information from natural sequence databases. 2/13

Excited to share PoET-2, our next breakthrough in protein language modeling. It represents a fundamental shift in how AI learns from evolutionary sequences. 🧵 1/13

🧬 Announcing PoET-2: A breakthrough protein language model that achieves trillion-parameter performance with just 182M parameters, transforming our ability to understand proteins.

Can we learn protein biology from a language model?

In new work led by @liambai.bsky.social and me, we explore how sparse autoencoders can help us understand biology—going from mechanistic interpretability to mechanistic biology.

From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models Protein language models (pLMs) are powerful predictors of protein structure and function, learning through unsupervised training on millions of protein sequences. pLMs are thought to capture common mo...

We’re excited about the potential of SAEs in biology and would love to hear your ideas.

Our preprint: www.biorxiv.org/content/10.1...
Visualizer: interprot.com
Github: github.com/etowahadams/...
HuggingFace: huggingface.co/liambai/Inte...