Alisia Fadini

Introducing The Structural History of Eukarya (SHE): The first proteome-scale phylogeny constructed entirely from 3D structure.
We computed 300 trillion alignments across 1,542 species to map the tree of life. 🧵👇 (1/5)

GitHub - rs-station/meteor: bringing you the best difference maps bringing you the best difference maps. Contribute to rs-station/meteor development by creating an account on GitHub.

New Title Alert: meteor- a tool for computing crystallographic difference maps that specializes in robust identification of weak signals from minor populations such as bound ligands or time-resolved experimental changes.

Learn more here: buff.ly/bgJYF9N

#SBGrid #SBGridSoftware #StructuralBiology

Check our new preprint smoothing rugged Cryo-EM landscapes: shorturl.at/gYs9U

We tackle practical hurdles of Optimal Transport (OT) loss—differentiability, cost & noise sensitivity—make it a feasible inference workhorse.
W/ G. Woollard, D. Herreros, @pilarcossio.bsky.social, K. Dao Duc 🧵👇 (1/9)

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge pubmed.ncbi.nlm.nih.gov/41280101/ #cryoEM

Denoising and iterative phase recovery reveal low-occupancy populations in protein crystals - Communications Biology Difference map denoising reveals bound ligands and time-resolved dynamics in macromolecular crystallographic data.

A huge thanks to the team: TJ Lane, Virginia Apostolopoulou and Jasper van Thor, and to @rs-station.bsky.social for supporting the project! You can check out the full paper here: www.nature.com/articles/s42... or try it on your data: github.com/rs-station/m... 8/8

We hope this tool becomes a useful addition for the community working on dynamic systems, transient complexes, or small fragment screening. 7/8

Why? Many experiments (e.g. time-resolved crystallography 🎥+ ligand screening 💊) generate minor populations that are hard to find and model. Our method gives a way to detect them more reliably. 6/8

Xtallography can’t measure phases, though they carry key info. With TV denoising as a Bayesian prior, we infer these latent phases. Inspired by coherent diffractive imaging, we embed the TV denoiser in an iterative EM loop that fixes experimental amplitudes, updating phases. 5/8

Rather than tweak parameters, we aimed for automation: optimal, reproducible, bias-free. Assuming noisy maps resemble Gaussian white noise, we use difference map negentropy–a measure of how non-Gaussian a signal is–as an automatic objective to optimize our parameters, avoiding manual tuning. 4/8

We apply TV denoising 📺 to difference maps, helping suppress noise and reveal subtle density changes corresponding to low-occupancy species. 3/8

🕺 As a retro detour from ML-based methods, we introduce total variation (TV) denoising to boost SNR in crystallographic difference maps. TV “flattens” regions like solvent or areas far from a ligand site while making minimal changes to the underlying data. 2/8

Frontier structural biology chases low occupancies: weak binders in drug discovery & fleeting intermediates in time-resolved studies. When squeezing SNR, confirmation bias looms – you can see what you hope to see in the noise! Enter METEOR ☄️, our denoising+phasing framework! 1/8

Excited to share our latest @nature.com: How does naloxone (Narcan) stop an opioid overdose? We determined the first GDP-bound μ-opioid receptor–G protein structures and found naloxone traps a novel "latent” state, preventing GDP release and G protein activation.💊🧪 🧵👇 www.nature.com/articles/s41...

SBGrid webinars are hosted with partial support from the NIH R25 Continuing Education for Structural Biology Mentors #GM151273, in collaboration with Co-PI Jamaine Davis of Belmont University.… ROCKET

If you missed the live presentation with
@alisiafadini.bsky.social and @minhuanli.bsky.social introducing ROCKET, you can watch the video on SBGrid's YouTube channel.

More here: buff.ly/nsA12eg

#SBGrid #Webinars

OpenFold3-preview (OF3p) is out: a sneak peek of our AF3-based structure prediction model. Our aim for OF3 is full AF3-parity for every modality. We now believe we have a clear path towards this goal and are releasing OF3p to enable building in the OF3 ecosystem. More👇

Write to us on GitHub for any issues and we'll improve! We are working to have a server available in collaboration with Phenix very soon.

ROCKET User Guide | ROCKET Docs Documentation, installation instructions, and tutorials for ROCKET

@minhuanli.bsky.social and I are really excited to see new users trying ROCKET out. If you haven't heard about it but are curious, our GitBooks are a good place to start: rocket-9.gitbook.io/rocket-docs

YouTube video by SBGrid Consortium ROCKET

ROCKET's webinar on cryo-EM+crystal data-guided protein structure prediction is now online 🚀: www.youtube.com/watch?v=_29C...

Thank you to the many of you who attended and stayed overtime for more Q&A, and especially to @sbgrid.bsky.social for hosting us last week!

We'll cover our latest work on low resolution applications, a run-through of our codebase, and tutorials of how to run ROCKET on your own data. Join if you're interested!! 🚀

At the moment we both cluster and uniformly sample and the validation of whether we have a better model compared from a full MSA comes from fit to the data, at the end, and geometric validation.

(1) ROCKET uses experimental information to score different conformations after subsampling (2) that first conformation arising from clustering is further modified through gradient descent in continuous MSA cluster profile space.

Thanks a lot Jake! The reliance of ROCKET on the MSA subsampling is different from other work in, I would say, 2 main ways.

Hey Martin, yes — www.biorxiv.org/content/10.1... it’s a little (too far) down the thread!

It has been very much on my mind – was excited to see the code posted!

Very grateful for the work, support, and guidance of all authors: Airlie, Tom, @randyjread.bsky.social, @hekstralab.bsky.social, and @moalquraishi.bsky.social. It’s a privilege to work with such a great team. 14/14

Very interesting work is also happening using diffusion-based priors! 🔗Solving Inverse Problems in Protein Space Using Diffusion-Based Priors arxiv.org/abs/2406.04239 & Inverse problems with experiment-guided AlphaFold arxiv.org/abs/2502.09372 13/14

AlphaFold as a Prior: Experimental Structure Determination Conditioned on a Pretrained Neural Network Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, challenges persist in capturing sidechain packing,...

All method details and equations in our preprint: 🔗 www.biorxiv.org/content/10.1... Code available soon! 12/14

ROCKET performs a new type of structure refinement by optimizing latent representations in evolutionary space. This unlocks possibilities for high-throughput ligand screening, assemblies solved at low resolution, and conformational landscapes – automation 🔜 new frontiers. 11/14

Key Takeaways:
• No AF2 retraining needed
• Works with X-ray & cryo-EM/cryo-ET
• If you formulate a likelihood target, you can test with your favorite data type
• Refines large-scale conformational changes
• Robust at low resolution
• Enables automated experiment-guided refinement 10/14

ROCKET subsamples MSAs (inspired by www.nature.com/articles/s41...) to generate diverse starting models, selects the best-fit conformation, then refines further with gradient descent. Note: we cannot use pLDDT alone to succeed! 9/14