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

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

Gradient-based refinement struggles when AF2’s initial model is too far from experiment. E.g. AF2 predicts the serpin PAI-1 in a metastable active state, but experimental data shows it in a hyperstable “latent” state with a 40 Å loop shift. 8/14

The low-res challenge is key for emerging cryo-ET data.
🔹 ROCKET extracts two conformations from a 9.6Å GroEL map. Its modeling matches humans here and even surpasses them in tough regions, boosting fit to data from CC=0.2 to 0.5 in a flexible domain (see ⭐) 7/14

Model building below 3–4 Å is tough – even for experts.
🔹 ROCKET refines low-res (3.82 Å) HAI-1 X-ray data, improving backbone accuracy beyond AF2. It smartly preserves ambiguous regions & corrects a possibly misregistered helix (310–330), without adding geometric artifacts 6/14

✔️ Peptide flips (e.g. PTP-1B)
✔️ Domain shifts (e.g. GroEL)

5/14

ROCKET samples barrier-crossing conformations that standard refinement methods often fail to reach:
✔️ Ligand-induced loop rearrangements (e.g. c-Abl kinase and PTP1B)

4/14

ROCKET integrates experimental likelihood targets within OpenFold’s differentiable prediction pipeline to optimize MSA profile features. Structure refinement becomes a search in evolutionary space instead of Cartesian space. What does this unlock? 3/14

AF-based methods encode rich structural priors but lack a general mechanism for integrating arbitrary data modalities. ROCKET tackles this by optimizing latent representations to fit experimental data at inference time, without retraining! 2/14

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.