btw in the name of getting NMR dynamics data out there, RelaxDB is available at the Dyna-1 huggingface! Go check it out huggingface.co/gelnesr/Dyna-1

Did some quick curation for ~30 of 133 proteins in our new dataset RelaxDB, which is the first of its kind to gather experimental info on timescales of motions per residue. The x-axis is intentionally silly to make a point - these dynamics expts do not use more than 10% D2O in samples.

@dereklowe.bsky.social The paper you cite discusses difference when entire solvent is D2O. It could be that D2O partitions away from protein when it’s there at 5% since the paper’s main point is H2O has increased interactions with proteins. Curious if anyone’s investigated that!

this is why many protein nmr spectroscopists only use 5-10% D2O! I hope this doesn’t deter people from what imo is a super undervalued source of data

If we wanna make accurate boltzmann samplers, we gotta know what distributions they should be sampling 😁 These change in the presence/absence of ligands, and there are multiple systems for which this is well-understood by this point experimentally.

BioEmu actually doesn't pass this test for AdK: majority of samples are in closed state (1AKE), which is the same intrinsic bias that AF2 + random sampling gets.

I don't know the other apo/holo systems bioEmu looks at as well, but same story: sampled density is primarily at the holo state.

You can see this in this FRET data for AdK here: in ligand-free form, the open state is more populated than closed.

I am sure future improvements exist over what we did! We removed deuterated samples (incomplete back-exchange) and entries with more than 12 15N assignments missing in a row. This is why we call this a "bold assumption", to me the proof is in the pudding, that we got a model with signal at all!

Hi Gabe! Yeah we thought of af pair (no MSA) as upper limit but what you’re proposing would also be control for pair rep.
To me, the kicker things we want to predict are coordinated motions that have low prob. Saw hints of that with CypA. I don’t think people would think of that as flexibility …

Thank you so much to the amazing Doro Kern for dreaming big w me. Thank you to @ramith.fyi, Hasindu, and @sokrypton.org for pushing these ideas in early days! last but not least, thanks to @jcchildsfund.bsky.social and @hhmi.org for funding :))

Tremendous thank you to partner-in-crime @ginaelnesr.bsky.social. This collaboration started when she offered to clean a metadata spreadsheet, and she ended up pushing the deep learning we tested so much further than I could have alone!

Moral of the story: useful data is out there at many degrees of quality, but we need to know how to interpret the data. We're so excited to see where these models and data go next!
Paper: rb.gy/de5axp
Dyna-1 colab (thanks to @ginaelnesr.bsky.social ) and RelaxDB: github.com/WaymentSteel...

Dyna-1 has predictive power in the big dogs of dynamics experiments: CPMG relaxation-dispersion. Dyna-1 predicted high p(exchange) in some aa's that typical data treatment says has no Rex, but more careful consideration says is real (NMR aficionados: unsuppressed R2).

There are so many interesting things that Dyna-1 predicts, but I wanna talk about a trend it didn't predict in RelaxDB! We realized that in many RelaxDB datasets where Dyna-1 did poorly, the Rex came from phosphate buffer binding/unbinding to the protein, an experimental artefact.