Directed Evolution (DE) is an optimization algorithm for proteins, so Machine Learning (ML) should help! We show on a large fitness landscape that DE+ML improves proteins better than DE alone and provide an example application. https://t.co/QwUPD5ceFZ @francesarnold @sbjennykan

LLM engineered carbon capture enzymes have officially been produced. The best designs were 170% more active and 25% more stable across extreme pH (Tm +8.5 C). Winning strategies include adapting a tag from a bacterial carbonic anhydrase, beta barrel core packing, and removing

Excited to share Caliby 🐈, our new model for structure-conditioned sequence design! Caliby is a Potts model-based sequence design method that can condition on structural ensembles. We use this to average out non-structural signal (e.g. evolutionary bias) learned by models 🧵1/N

Very pleased to see our AI model GenSLM designing novel and versatile enzymes in a challenging setting in @francesarnold lab. https://t.co/0zjpIg716I Back in 2022, we built the world's first genome-scale language model (GenSLM) trained on all known bacterial and viral genomes

Check out our new perspective "Illuminating the universe of enzyme catalysis in the era of artificial intelligence" now out in @CellSystemsCP ! We discuss a vision and path forward for genetically encoding almost all chemistry, powered by new AI tools: https://t.co/Tc7zQ6TExm

Excited to share our recent work: Sparse autoencoders uncover biologically interpretable features in protein language model representations now in PNAS. Thread below 🧵

We have a new collection of protein structure generative models which we call Protpardelle-1c. It builds on the original Protpardelle and is tailored for conditional generation: motif scaffolding and binder generation.

DynaRepo: The repository of macromolecular conformational dynamics 1. DynaRepo is a novel repository that addresses the critical gap in studying the dynamic behavior of macromolecules, which is essential for understanding interactions like antibody–antigen recognition and

🚨New paper 🚨 Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇 1/12

We made it on the cover! So proud of the team 🎉 You can use BioEmu for free now to get AI predictions for the entire distribution of conformations a protein can take:

1/ 🧵Can protein language models reason about evolution, not just model it? We built a 24 M-param PLM, Phyla, that reconstructs phylogenetic trees better than existing PLMs. Details & links in 2/👇

Really enjoying #Repartzymes Repurposed and Artificial Enzymes meeting organized by #TomWard of Uni. Basel. Many former group members are here! https://t.co/P3GoQqgUce

Excited to share #AlphaGenome, a start of our AlphaGenome named journey to decipher the regulatory genome! The model matches or exceeds top-performing external models on 24 out of 26 variant evaluations, across a wide range of biological modalities.1/6

🎉New preprint!🎉 Extremely excited to share CryoBoltz❄️⚡️, led by superstar @rishwanth_raghu! We develop a multiscale guidance recipe to steer structure prediction models (e.g. AlphaFold3 / Boltz-1) towards experimental cryo-EM density maps, including heterogeneous,

Excited to unveil Boltz-2, our new model capable not only of predicting structures but also binding affinities! Boltz-2 is the first AI model to approach the performance of FEP simulations while being more than 1000x faster! All open-sourced under MIT license! A thread… 🤗🚀

🚀 Excited to release BoltzDesign1! ✨ Now with LogMD-based trajectory visualization. 🔗 Demo: https://t.co/tjBXSibZhT Feedback & collabs welcome! 🙌 🔗: GitHub: https://t.co/HsQBrB8amQ 🔗: Colab: https://t.co/TzX4q5m2qp @sokrypton @MartinPacesa

Guide your favorite protein generative model with experimental data? Meet ProteinGuide - a method to condition pre-trained models on properties without retraining. We validated it both in silico by guiding ProteinMPNN and ESM3 on 3 tasks and in vitro by engineering base editors.

I'm excited to share our new preprint "Steering Generative Models with Experimental Data for Protein Fitness Optimization" (🧵1/5)! Paper: https://t.co/SeOO3QNimY Code:

In a medical milestone, a customized base editor was developed, characterized in human and mouse cells, tested in mice, studied for safety in non-human primates, cleared by @US_FDA for clinical trial use, manufactured as a complex with an LNP, and dosed into a baby with a severe,

Check out this work led by the incredible @AdaFang_ ATOMICA constructs representations of molecular interactions at the atomic scale, capturing universal physiochemical principles. Read about the exciting insights from applying ATOMICA below 👇

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)