New on OpenProtein.AI: esmfold2, esmfold2-fast, esmc-300m/600m/6b, esm-if1, and Protenix-v2, plus full multichain support across all workflows. Sign up link in the comments!

We’re excited to announce our expanded partnership with Boehringer Ingelheim. Together, we are building the future of AI‑driven antibody discovery and optimization. www.openprotein.ai/strategic-partnership-with-boehringer-ingelheim

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@openprotein.bsky.social · 6mo ago

New on OpenProtein.AI: → Improved protein design GUI & refolding metrics for candidate filtering → New structure design models (RFdiffusion, BoltzGen) Nanobody and miniprotein design walkthroughs now live! Links in thread.

New job openings @openprotein.bsky.social across protein foundation model research, computational protein design, and cloud platform engineering www.openprotein.ai/careers

Careers

At OpenProtein.AI, we are building tools to democratize protein engineering. Join us.

openprotein.ai

Our preprint on sequence-to-property learning and zero-shot fitness prediction with PoET-2 is live: arxiv.org/abs/2508.04724 PoET-2 is also open sourced on github: github.com/OpenProteinA... Thanks to the @openprotein.bsky.social team!

Understanding protein function with a multimodal retrieval-augmented foundation model

Protein language models (PLMs) learn probability distributions over natural protein sequences. By learning from hundreds of millions of natural protein sequences, protein understanding and design capa...

arxiv.org

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...

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!

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...

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! 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

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

synbiobeta.com

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.

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...

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...

biorxiv.org

A flexible framework for fast and accurate segmentation of filaments and membranes in tomograms and micrographs - the TARDIS manuscript is now live @biorxivpreprint.bsky.social ! Thanks to hard work by Robert Kiewisz and our many collaborators! www.biorxiv.org/content/10.1...

Accurate and fast segmentation of filaments and membranes in micrographs and tomograms with TARDIS

It is now possible to generate large volumes of high-quality images of biomolecules at near-atomic resolution and in near-native states using cryogenic electron microscopy/electron tomography (Cryo-EM...

biorxiv.org

I'll be talking about shrinking protein language models with #PoET and protein engineering at openprotein.ai tomorrow at A*STAR's Bioinformatics Institute. If you can't make it, I'll also be presenting at the Berger Lab seminar @mitofficial.bsky.social on Wednesday!

Yet more evidence that transfer learning of sequence-only PLMs does not benefit from scale beyond 650M params 🧵

Claus Wilke@clauswilke.com · 2y ago

The complete guide for transfer learning with the protein language model ESM-2. (In brief: Use ESM-2 650M and calculate mean embeddings across sites.) www.biorxiv.org/content/10.1...