But for the CA representation used by our EBM, it seems like the entropy due to local backbone and side chain fluctuations might be much more tractable?

I might be wrong, but it seems like the difficulty of learning chain entropy would be very related to the level of coarse graining. If you’re looking at a reaction coordinate like fraction of native contacts, each macrostate contains a massive number of chain confrontations

Thanks for the insight! I’ll definitely keep this is mind for future analyses. For what it’s worth i might caution against interpreting the decoy ranking plot as a “folding landscape,” since all those decoys are basically folded, just in wrong conformations.

Also let me know if any of that sounds wrong! Feedback from someone with your level of MD/StatMech knowledge is very much appreciated 🙂

Good question! Ideally it should be a free energy because it learns the PMF over coarse-grained states (at the temperature of the MD data), integrating out fine-grained DOFs. To test if this is true in practice maybe we could examine systems where entropy over the fine DOFs plays a big role?

If this sounds interesting to you, check out the preprint on bioRxiv: www.biorxiv.org/content/10.6....

All of our code and model weights are available at github.com/jproney/Prot.... Thanks for listening!

This research builds on my undergraduate work using AlphaFold2 for structure scoring. Compared to AF2Rank, ProteinEBM is more efficient and versatile, and has a firmer theoretical foundation. We see ProteinEBM as an important step toward developing physically-grounded ML models for protein science.

And finally, we combined large-scale ProteinEBM sampling with AF2Rank to create an ab initio structure prediction protocol that beats massive sampling from both AlphaFold2 and AlphaFold3 in the MSA-free regime.

When used for sampling fast-folding proteins, ProteinEBM produces energy funnels with minima very close to the native structures

ProteinEBM can also rank the effects of mutations on stability, with accuracy comparable to sequence-supervised models like ProteinMPNN, despite not being trained to predict sequences.

ProteinEBM performs very well at ranking the correctness of candidate protein structures, and compares favorably to Rosetta in terms of ranking correlations.

Introducing ProteinEBM: a fast, transferable Energy-Based Model for protein conformations. ProteinEBM is trained using energy-based score matching. After training on sequence-structure pairs and MD data, the model energy should match the log data density (i.e., the free energy landscape)!

A very general approach to protein modeling is the development of energy functions that describe protein conformational landscapes. With a good enough energy, you can use optimization to predict structures, simulate dynamics, estimate conformational preferences, predict stabilities, and more.