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"Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening" from Neil Thomas Lucy Colwell and team looks amazing. They compare directed evolution, hit recombination, and ML-guided design of NucB.

Machine learning guided directed evolution beats directed evolution and hit recombination head-to-head for enzyme engineering. Neil Thomas Lucy Colwell

🧬What are protein language models (PLMs) actually learning about biology? Our paper introduces InterPLM - a framework that reveals interpretable features in PLMs using sparse autoencoders, giving us a window into how these models represent protein structure and function. 🧵(1/9)

Introducing ESM Cambrian. Unsupervised learning can invert biology at scale to reveal the hidden structure of the natural world. We’ve scaled up compute and data to train a new generation of protein language models. ESM C defines a new state of the art for protein

I'll be in Vancouver for NeurIPS / MLSB from Dec 13-16! If you're interested in protein language models, semi-supervised learning, or otherwise interested in meeting up, reach out! :)

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API

Can we learn protein biology from a language model? In new work led by Liam Bai and me, we explore how sparse autoencoders can help us understand biology—going from mechanistic interpretability to mechanistic biology.

Protein function often depends on protein dynamics. To design proteins that function like natural ones, how do we predict their dynamics? Hannah Wayment-Steele and I are thrilled to share the first big, experimental datasets on protein dynamics and our new model: Dyna-1! 🧵

Happy Sunday to all! This morning, we are excited to share Chase’s work developing a simple, scalable method to assemble 100s-1000s of custom genes from oligo pools using standard lab tools! (Small 🧵 below)

Gene synthesis is often the most expensive part of protein engineering with generative models. Happy to have played a small part in this work, where Chase developed a method for precision library construction at scale, with per-gene costs as low as $1.50. Romero lab

Thrilled to see my digital art on the cover of Trends in Genetics! The two binary strings represent reverse-complementary DNA sequences (00=A, 01=C, 10=G, 11=T) and the connecting rectangles represent “embeddings” learned by DNA language models. Our article: doi.org/10.1016/j.tig.…