Pinned
We’re excited to share our preprint where we show, for the first time, the atomically accurate design of VHH antibodies!
00:00
Nate Bennett
60 posts
Nate Bennett
@naterbennett0
Co-Founder at Xaira Therapeutics. De novo protein binder design. Former Postdoctoral Scholar at @UWproteindesign.
Seattle, WA
Joined June 2022
- 1/7 Improving de novo Protein Binder Design with Deep Learning (biorxiv.org/content/10.110…) We show that AF2 is an effective predictor of whether a de novo designed miniprotein will bind to the intended target or not.
- It’s exciting to see our work on improving binder design with deep learning methods published in Nature Communications! nature.com/articles/s4146…
- RFdiffusion is now free and open source. We’re excited to see what cool proteins you all will diffuse!Today we're making RF Diffusion, our guided diffusion model for protein design with potential applications in medicine, vaccines & advanced materials, free to use. The software has proven much faster and more capable than prior protein design tools. bakerlab.org/2023/03/30/rf-…
GIF - Turns out RFdiffusion is incredible at designing binders to arbitrary targets. We see a 100x increase in experimental success rate over Rosetta-based binder design methods!We’re very happy to announce that our RFdiffusion manuscript is now on bioRxiv! A lot can change in a week - we’ve now tested over a thousand designs and there’s so much exciting new data! 🧵
GIF - Diffusion models excel at protein design including binder design! Check out Joe’s thread to see how we made a pM binder straight off the computer!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!
00:00 - Replying to @naterbennett0Here’s a link to the paper on BioRxiv! biorxiv.org/content/10.110…
- Replying to @naterbennett0 and @UWproteindesignProtein binder design has never been easier and we are excited to see what you design! A big thanks to my co-lead on this paper: Brian Coventry (who is not on Twitter).
- Replying to @naterbennett0This work is a proof-of-concept and needs to be refined before it is on-par with RFdiffusion for mini binder design. I'm incredibly excited, however, about the potential of this type of technology for therapeutic design and I can't wait to see where this leads!
- Replying to @naterbennett02/7 This paper builds on the work of Cao et al. (nature.com/articles/s4158…) that presented a pipeline to computationally design miniprotein binders. The major issue with this pipeline has been the low experimental success rate of designed binders.
- Replying to @naterbennett0 and @UWproteindesignThis method is what we use for sequence design and in silico filtering of RFdiffusion protein binder backbones! RFdiffusion is already open source and we are making this method free and open source as well:
- Replying to @naterbennett05/7 The confidence AF2 assigns to interchain contacts between target and miniprotein (a metric we call pAE_interaction) is by-far the best predictor of whether a miniprotein will work experimentally that we have ever seen. A new RoseTTAFold (RF2) is similarly predictive to AF2.
- Replying to @naterbennett0RFdiffusion is capable of designing diverse antibodies, that are truly de novo (no similarity to existing antibodies to those epitopes/in the training set). @RobertRagotte led the experimental effort, and characterised numerous VHH antibody binders to four different targets.
- Replying to @naterbennett04/7 Crucially, initializing AF2’s prev_pos variable with the Rosetta design structure helps AF2 find the correct dock more often (we call this protocol “AF2 with an initial guess”).
