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

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

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.

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

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

We also saw this when we looked at transfer learning with PoET embeddings compared with ESM - www.openprotein.ai/poet-foundat...