Thread by @_JosephWatson

DALL-E’s amazing images are popping up all over the web. That software uses something called a diffusion model, which is trained to remove noise from static until a clear picture is formed.

Turns out diffusion models can design proteins too!

Today we’re sharing a deep-learning method for protein design called RoseTTAFold Diffusion. With minimal input, it turns prompts (“create a molecule that binds X”) into new proteins that fold and function in the lab. We’ve tested 100s already.

PDF here: bakerlab.org/2022/11/30/dif…

RFdiffusion is best-in-class for protein backbone generation (low RMSDs to AlphaFold models) and surpasses inpainting and hallucination at scaffolding functional motifs. It makes bigger, more diverse, and more accurate proteins. (600aa protein, gray=design, colors=AF2)

RFdiffusion can also be guided with symmetry. For example, we have designed and are characterizing a new protein that engages all three symmetric ACE2 binding sites on the SARS-CoV-2 spike protein. In this case C3 symmetry works, but any symmetry is possible.

Not to mention all the symmetric oligomers @HelenEisenach and @andrewjborst have designed and tested in the lab! Being able to design with any symmetry you want opens up so many applications for therapeutics and enzyme design.

Base RFdiffusion is capable, but with tuning and guidance *excels* at hard tasks, such as scaffolding enzyme active sites! Woody Ahern showed that doing this, AF predicts (color) that our designs (gray) fold to atomic accuracy. An exciting future for enzyme design lies ahead!

We designed binders to five medically-relevant molecules. These binder proteins pass our most stringent in silico metrics and we’re testing them in the lab right now. In the future, it might only take a few seconds to design a high-affinity binder protein for any target you want

.@PreethamVi and @SusanaVazTor then used RFdiffusion to design binders to two hormone peptides. These bound too tightly for our instrument to measure! Likely picomolar affinity. That’s the strongest binding to any protein, peptide, or small molecule achieved by computation alone!

Here at @UWProteinDesign, we’re discovering every day just how powerful RFdiffusion can be. It’s amazing to see technology grow so rapidly. We’re going to make RFdiffusion code available in the near future so everyone can get a chance to design their own amazing proteins!

This work builds on the progress and insights of so many people, both @UWProteinDesign and beyond. It’s such an exciting area to be working in right now! Just today there was this news from @generate_biomed:

https://twitter.com/generate_biomed/status/1598315590971756544

And it’s amazing to see people all around the world getting excited about this stuff too:
technologyreview.com/2022/12/01/106…

This project was co-led by @davejuergens and myself, alongside @naterbennett0, @brianltrippe and @json_yim. And was only possible because of the amazing contributions from @HelenEisenach @woodyahern @PreethamVi @andrewjborst @SusanaVazTor

And also to @BasileWicky @LFMilles @RobertRagotte @jueseph and all the Twitter-less collaborators who made this such an exciting and productive project!

And a special shoutout to @ichaydon for all of the amazing graphics :D