Inspired by the #NobelPrize for #proteinstructure prediction and design and want to dive into the field? Our Rosetta Commons code school video playlist will teach you all the tools - Rosetta, AlphaFold, RFDiffusion, and more! #ProteinDesign #DavidBaker youtube.com/playlist?list=…

Now in nature, AI Microsoft Research that simulates proteins with more than 10,000 atoms with quantum accuracy, orders of magnitude faster than ever before. AI2BMD's results match those of wet-lab experiments, enabling new biomedical research advances. nature.com/articles/s4158…

Beautiful work from Adam and coworkers!

Design of high specificity binders for peptide-MHC-I complexes 🚀 New preprint from David Baker!🚀 • Researchers from Institute for Protein Design introduced a novel approach using deep learning tools like RFdiffusion to design specific protein binders for peptide-MHC-I (pMHC) complexes,

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

Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from Microsoft Research AI for Science. #ML #AI #NeuralNetworks #Biology #AI4Science biorxiv.org/content/10.110…

1/🧬 Excited to share PLAID, our new approach for co-generating sequence and all-atom protein structures by sampling from the latent space of ESMFold. This requires only sequences during training, which unlocks more data and annotations: bit.ly/plaid-proteins 🧵

In new work led by Aditi Merchant with Samuel King, we prompt engineer Evo to perform function-guided protein design with high experimental success rates, including designs that go beyond natural sequences. We also release SynGenome, the first AI-generated genomics database. 🧵 1/N

New preprint from our group! We propose SHAPES, a set of metrics to quantify the distributional coverage of generative models of protein structures with embeddings at different structural hierarchies and quantify undersampling / extrapolation behaviors.

Really cool work, congrats Wenjie!

📢Excited to introduce NanoCas -our new mini CRISPR system that can reach tissues previously out of reach! By shrinking CRISPR to 1/3 its normal size, we can now edit genes in muscle, heart & brain that were difficult to access before. Summary & link to paper:

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.

New research in Science represents a notable step forward in designing enzymes from scratch. With a new approach, researchers designed an enzyme that uses a covalent intermediate to catalyze a two-step reaction, analogous to what many proteases do when breaking apart proteins.

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! 🧵

Announcing Neo-1: the world’s most advanced atomistic foundation model, unifying structure prediction and all-atom de novo generation for the first time - to decode and design the structure of life 🧵(1/10)

Top-tier podcast content. Smart, deep, and inspiring.

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2

Harvard will continue to defend against illegal government overreach aimed at stifling research and innovation that make Americans safer and more secure. Read the full statement: hrvd.me/d24cca

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… 🤗🚀

Today in the journal Science: BioEmu from Microsoft Research AI for Science. This generative deep learning method emulates protein equilibrium ensembles – key for understanding protein function at scale. msft.it/6010S7T8n