Check out our latest work! 👇We leverage pretrained protein generative models (here Chroma Generate:Biomedicines ) as a prior for inverse problems in protein space (e.g. structure completion, distance contraints, cryo-EM model building). ❄️🏗️ Paper: arxiv.org/abs/2406.04239

cryoDRGN-ET is a deep generative neural network-based method for heterogeneous reconstruction of cryo-ET subtomograms that can capture both conformational and compositional heterogeneity. Ramya Rangan Ryan Feathers Abhay Kotecha Ellen Zhong nature.com/articles/s4159…

CryoDRGN-ET❄️🐉 is published in Nature Methods! We combine neural fields and generative modeling to reveal the structural heterogeneity of protein complexes *in situ*! So excited to see this out, and huge congrats to Ramya Rangan, Ryan Feathers and team! nature.com/articles/s4159…

Awesome to see our work on cryoDRGN-ET is now out in Nature Methods! CryoDRGN-ET can resolve the structural heterogeneity of macromolecules in situ from cryo-ET data. We've showcased the method here on ribosomes and yeast fatty acid synthase.

CryoDRGN-ET also was able to identify high quality yeast fatty acid synthase (FAS) particles and resolve motions of the complex in situ. Thanks to Ryan Feathers for the exciting new analyses of the yeast FAS!

🚀 Excited to share our preprint: CryoBench 🧊🪑: Diverse & Challenging Datasets for the Heterogeneity Problem in Cryo-EM! 🌐cryobench.cs.princeton.edu 🙌 Huge thanks to my amazing co-authors, collaborators, & advisor @zhongingalong! 🎉 #cryoEM #MachineLearning 🧵 Details below!

Excited to share CryoBench🧊🪑 our dataset and benchmarking effort for heterogeneous cryo-EM reconstruction! Led by Minkyu Jeon, who is an absolute machine, and super fun collab with Pilar Cossio Sonya groups Flatiron Institute A few thoughts on our benchmark design 👇

Some thoughts on the current state of single particle cryo-EM map/model validation. (TLDR: preferred orientation remains a big problem, ❤️ Q-scores. There's been progress, but there's still more to do. ) authors.elsevier.com/sd/article/S09…

You can find cryoDRGN-ET along with many new features and improvements in the latest version of the cryoDRGN ❄️🐉 software (v3.4.1). V3.4.2 release coming in hot by Michal Grzadkowski later this week 🔥

These are exciting times for #TeamTomo and I think the best is yet to come! I'm so grateful for the opportunity to work in such supportive and collaborative groups in Princeton MolBio and Princeton Computer Science 👨‍🔬👨‍💻

Fromme Lab use cryo-electron microscopy and functional experiments to reveal how Rab6 is activated by the Ric1-Rgp1 complex. nature.com/articles/s4146…

CryoBench 🧊🪑is accepted at NeurIPS as a Spotlight! Poster session happening now! Minkyu Jeon We’ve been making a lot of improvements to the code and docs to make it as easy as possible to compute metrics ➡️ ez-lab.gitbook.io/cryobench The heterogeneity problem in cryo-EM is

🚨New paper at #NeurIPS2024! We present Hydra, a mixture of neural fields 🐉🐉🐉 for ab initio cryo-EM reconstruction of complex mixtures. Hydra is our attempt at pushing the boundaries for even more extreme forms of heterogeneity in cryo-EM 🤯. This was joint work with an

🚀 Exciting update on CryoBench! 🚀 We’ve refactored the metric (per-img-FSC) script to make it even easier to use and created a user manual on GitBook: CryoBench Manual: ez-lab.gitbook.io/cryobench Big thanks to Michal Grzadkowski for the amazing refactor! 🙏

🎉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,

CryoDRGN-AI ❄️🐉🤖 is now published in Nature Methods!!! So excited to see this out and a huge congrats to Axel Levy and team! CryoDRGN-AI extends cryoDRGN from requiring input, fixed camera poses, to end-to-end ab initio reconstruction of biomolecules and their conformational