We're a big step closer to automated determination of protein structures. The key? Having AlphaFold listen to experimental data. Great work, led by @alisiafadini.bsky.social and @minhuanli.bsky.social in an inspiring collaboration with @moalquraishi.bsky.social and @randyjread.bsky.social.

Alisia Fadini@alisiafadini.bsky.social · 4mo ago

ROCKET 🚀 inference-time optimization of AlphaFold to fit structural data is published! rdcu.be/fa9YH Since our preprint, we’ve pushed it to regimes where other methods break: low resolution, weak signal, real experimental edge cases. Here’s what we learned: 1/15

ROCKET-fueled refinement? That's on-brand, along with bad space-themed puns and other cool software projects at the frontier of structural biology. 🚀 ROCKET source code: github.com/rs-station/r... 1/🧑‍🚀 More projects: rs-station.github.io

Alisia Fadini@alisiafadini.bsky.social · last yr.

Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging. @minhuanli.bsky.social and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.

ROCKET Launches Structural Biology Past AlphaFold 🧪 🧬 #AcademicSky #higherEd #RNA #Protein www.mcb.harvard.edu/department/n... @hekstralab.bsky.social @cam.ac.uk @flatironinstitute.org @umeauniversitet.bsky.social@tcddublin.bsky.social @stjuderesearch.bsky.social @columbiauniversity.bsky.social

ROCKET Launches Structural Biology Past AlphaFold - Harvard University - Department of Molecular & Cellular Biology

Five years ago, a team at Google’s DeepMind won CASP14, the long-running protein structure prediction competition, with AlphaFold, a machine-learning system capable of predicting the three-dimensional...

mcb.harvard.edu

Can AlphaFold refine structures for X-ray & Cryo-EM/ET data? Now it can! Great collab with @moalquraishi.bsky.social and @randyjread.bsky.social led by @alisiafadini.bsky.social and @minhuanli.bsky.social. 🧵below. Code to follow...

Alisia Fadini@alisiafadini.bsky.social · last yr.

Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging. @minhuanli.bsky.social and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.

Crystal structures are *not* God-given truth. They approximate, w/ flaws & errors, X-ray diffraction data. AlphaFold etc. have been trained on structures, not data. SFCalculator now differentiably connects structures to diffraction data. What does this enable? 🧵 1/4 www.biorxiv.org/content/10.1...

Now in Cell: K+ ion channels enable electric currents consisting of potassium ions to generate action potentials in heart, brain, and muscle. For the first time, we can now see these currents atom-by-atom. www.cell.com/cell/abstrac... 1/8

X-ray diffraction can access protein dynamics. Why so few datasets? One reason? Lack of powerful pulsed X-ray sources. XFELs are limited. Synchrotrons throw away >99% of photons. Working with Aaron Bewster (DIALS/LBNL) and BioCARS (biocars.uchicago.edu/) to change this! 🧵1/2

Hello World! Catching up after not posting for a long time on Twitter/X/BlueSky (what happened to Mastodon?).