proteinbase.com from @adaptyv.bio looks amazing - hundreds of open-source binding affinity measurements. It’s hard to overstate the value of high-quality, uniform affinity data for training and evaluating filtering and ranking methods

Pretty interesting that AFAICT the filtering was done after the fact (so, library 1 had no filtering). This could make it an excellent dataset for training/testing filters/rankers. Too bad it looks like the dataset is not public

Another great post on binder design from @btnaughton.bsky.social !

It's a very cool result but IMO there are caveats. Inference is (mostly) slower. There is existing work on faster models (e.g. MiniFold or protenix mini), and also existing work on ensemble prediction. I doubt this works without training on AFDB, which bakes in inductive bias from triangle layers

could be fun to implement this in github.com/escalante-bi... -- and then try swapping AF2 for Boltz or Protenix, adding an inverse folding term during design, etc

Great nanobody design paper from @brianhie.bsky.social and @synbiogaolab.bsky.social ! Amazing what combining a few public models can do

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round.

When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the lab data they're trained on and the hypotheses they can test in the real world.

Looks interesting (particularly for in silico affinity maturation) & code available on github: github.com/TencentAI4S/...

mpnn actually improved the hit rate and affinities for af2 -- motivation same as af2cycler 😀. I wouldn't read much into boltz results, there are a ton of hyperparameter choices for boltz we made (e.g. # of diffusion steps, MSAs vs templates, refolding with AF2 multimer) that complicate the story

For these designs we used the proven BindCraft filters. `mosaic` is similar to BindCraft but supports many more models (AF2, Boltz 1+2, Protenix, several PLM and inverse folding models, any JAX model). We’re really excited about `mosaic` for more complex design objectives like dual target binders.

Recently tested some de-novo minibinders against two targets (thanks Adaptyv!) designed using our open-source design library, `mosaic`; our best method got hit rates of 7/10 and 8/10 and affinities as low as single-digit nanomolar. Wrote up some thoughts here: blog.escalante.bio/minibinder-d...

Have no fear if you weren't in the packed audience this week for @martinpacesa.bsky.social's awesome seminar, because you can check out the recording 👇
youtu.be/qQihl6If9vU

✨Cradle is hiring protein+ML researchers!✨

We operate ML for lab-in-the-loop lead optimization across all industries (pharma, synbio, ...), modalities (antibodies, enzymes, ...), properties (binding, activity, ...)

We're a scaleup and already relied upon by 4 of the top 20 big pharma.

Apply here!

For this target it should be, I've run it on an A10 with 24gb

Try it out yourself with a demo marimo notebook: github.com/escalante-bi...

Still a work in progress; feedback and PRs welcome :)

p.s. this is a very small target but each one of these designs takes only 14 seconds to generate on an H100 (slightly longer to get discrete sequences)

We added Boltz-2 to our protein design package! Under the hood this relies on a JAX translation, which, thanks to @jeremywohlwend.bsky.social and @gcorso.bsky.social ’s clean code, was fairly easy to write. We’ve been getting great results -- and we have yet to explore the most exciting new features

Designed my (the?) first minibinder using @gcorso.bsky.social 's new Boltz-2 model. Excited to explore how templates and increased MSA dropout impact binder design. This is work in progress but will be open sourced as usual

Interesting! Seems very similar to what @sokrypton.org does with his contact losses; probably encourages all mass in one PAE bin? I added it to `boltz-binder-design` for boltz-1 and AF2: github.com/escalante-bi...

Sure, would love to! Just submitted a PR

Want to try ESMC but don't want to switch from #JAX to pytorch?

Here’s a super barebones translation to @patrickkidger.bsky.social's excellent equinox: github.com/escalante-bi...

No chunking, but this would be very easy to shard… github.com/nboyd/joltz