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

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

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.

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

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

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...

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

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