Even though I probably spent more time in the lab playing chess than doing research, for some reason they gave me a PhD 🥳🥳

That's my designed protein on the cover of Science! Achievement unlocked!

Researchers in Science present a next-generation protein structure prediction and design tool, #RoseTTAFold All-Atom, that can accept a wide range of ligands and covalent amino acid modifications. Learn more in this week's issue: https://t.co/PmmMUjxNaz

Specifying the input for cofolding models just got easier with my new gradio component "gradio_cofoldinginput". Try it here to generate inputs for RoseTTAfold2 All Atom predictions including covalent modifications:

Super excited to share our paper “Design of amyloidogenic peptide traps” is out now! 🧠 https://t.co/Qmu9BpZiMA Special thanks to @eaandrzejewska , Danny Sahtoe and David Baker

We’re excited to share our preprint where we show, for the first time, the atomically accurate design of VHH antibodies!

🧵(1/7) Excited to share that our work, EquiformerV2, has been accepted to #ICLR2024. EquiformerV2 is the state-of-the-art on large-scale atomistic benchmarks -- OC20, OC22, AdsorbML, and ODAC23. Joint work with @bwood_m, @abhshkdz from @OpenCatalyst and @tesssmidt Paper:

AlphaFold2 and RoseTTAFold are limited to predicting the 3D structure of proteins. A new all-atom method models complete biomolecular assemblies, including proteins, nucleic acids, small molecules, metals, and covalent modifications. #NBThighlight

Our work on modeling and designing biomolecular assemblies is now in @ScienceMagazine. https://t.co/J0xnuTGGgZ RFAA Code: https://t.co/PkMLFH9iid RFdiffusionAA Code:

Excited to finally be able to share our ICLR work critically analyzing the capacity of deep learning docking methods to generalize and how to improve this (spoiler scaling, augmentation and RL)! With this, we release a new significantly improved version of DiffDock! A thread! 🧵

Our paper using ProteinMPNN redesign for improved properties is up on JACS! Huge thanks to Reyes, @ikalvet, Sam, and many others for their work. Shout out to @JustasDauparas for developing such a groundbreaking (and versatile!) tool. @UWproteindesign https://t.co/bLm0jFuOvF

looking for interns for two projects next summer, come work with me, @nc_frey and the rest of the @PrescientDesign team applying bleeding edge machine learning to fascinating biological data!

Monday 11am Reading group: RosettaFold all-atom! https://t.co/Wh4UiZbbJE Let us discuss with @r_krishna3 what protein design niceties we can achieve with this. And also the AlphaFold all-atom "glimpse" :) Join on Zoom at 11am ET / 4pm UTC / 5pm CET: https://t.co/R8d1EHxLCx

Soo, this is a big one :) On Nov 20 we will have RosettaFold all-atom in the reading group with @r_krishna3! Quite amazing protein design capabilities. But that is my naive evaluation - join us and let's discuss and hear your insights! Sign up here: https://t.co/R8d1EHxLCx

Thrilled to announce the opening of the Srivatsan Lab ( https://t.co/mUPc2wHm0X) at the @fredhutch. The lab will be building new sequencing technologies to understand how our cells and bodies form over the course of development.

RFdiffusionAA generating a small molecule binding protein against an experimental FXIa inhibitor (OQO), a ligand which is significantly different than any in its training dataset.

RoseTTAFold updated to be All-Atom... biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications ... and diffusion🤯 https://t.co/36EtN0IzoL

All-atom version of RoseTTAFold looks quite exciting!

We made RoseTTAFold that predicts everything in the PDB, not just proteins, the used it to make RFDiffusion that generates ligand-binding proteins, including a 10nM binder of digoxigenin. See @r_krishna3's thread for details.

Very excited to share RoseTTAFold All-Atom and RFdiffusion All-Atom, methods for structure prediction and design of biomolecular assemblies! https://t.co/iG6rD0LHsi 1/n