Structural Propensity Database of Proteins

Nice work lowering the entry barrier for machine learning in this space, which appears to be the aim of your company, but it's a bit of a tease to claim your data representation is great for supporting ML and stop short of doing any of that in the manuscript. I take it that's the next step?

On that note, is there any reason why the propensity classes alpha/beta/coil are still so widely used? Especially coil/turn/"other". It seems to me that these are ancient artifacts of structural biology that could definitely restrict human understanding of protein dynamics. Perhaps there is nuance in the chemical shift data that may help the design of better structural classes using ML.

BTW, greetings from GROMACS group in Groningen :) I happend to do my PhD in NMR and Molecular Dynamics.

Coming back to your comments about the canonical secondary structures; I couldn't agree more with you. The problem is quite simple, how are we going to convince the >90% of structural biochemistry society to simply accept the fact proteins are bloody dynamic and X-ray / eye candy structures may have quite little to do with the "real" picture at room temperature?

Interesting approach to getting at some dark spaces in our current understanding. What kind of computation time does it take to run against a single amnio acid chain? Can you get much (nice disorder predictions?) out of an amino acid sequence alone?

Our protein design work has its origins coming from a different angle. But there are some interesting thoughts we've had about utilizing (well, dreamed up) technologies like yours to quickly make up a lot of ground between structural prediction, and empirical functional design.

Starting to see such 21st century data standards for bio data! Yay for everyone!

All models are wrong but some are useful. My PhD was in empirical protein dynamics (solution NMR, CD, DSF, etc..) and the long and short of it is that disordered states are particularly difficult to distinguish from one another. When you consider that an even partially disordered ensemble has essentially an infinite number of nearly degenerate conformations inter-converting on timescales ranging from picoseconds to milliseconds, lumping them into coil/turn/other turns out to be just the law of large numbers in action (reversion to the mean, etc.).

That and the biophysical properties conferred by partially disordered proteins makes them a motherfucker to work with outside of some archetypal domains. I liked to explain it like this. Imagine you have a piece of string three feet long. Along the length of that string you have ~1 inch segments consisting of velcro (both kinds), zippers, magnets, balloons, strawberry jello, and marshmallows--all randomly distributed along the length. Now try to fold all of that up so the jello, velcro, and balloons are on the inside. That's a simple model of a protein. Now make it start opening and closing. Now put 5 of them next to each other.