This seems super cool, excited to give it a read!

Awesome! I'm excited to give these a read and try this out!

Congrats Roland :)!

Thanks! I spoke with Ivan earlier this year about our intersections, I gave a talk on this paper and our pylambdaopt work at the Folding@NYC meeting in Jan. I'm really excited for that as well and generally for your tools. I think they are very powerful and beneficial for the field 💪!

Expanded ensemble was also able to well capture average residue-wise mutation effects, potentially allowing for prediction of beneficial position-wise mutation sites.

We found that, although Flex-ddG was more accurate, this accuracy came from a conservative prediction tendency (predicting most mutations to be neutral). Expanded ensemble however, was better able to predict significantly stabilizing or destabilizing mutations.

Additionally, we re-ran Rosetta-based SSM using the Flex-ddG protocol.

Using Bayesian inference of the convoluted high-throughput FACS data from the publication of these designs [Chevalier, A.; Silva, D. A.; Rocklin, G. J.; Nature 2017, 550, 74–79 doi.org/10.1038/natu... ] we estimated the experimental binding affinities of all site-saturated mutants in the 3 binders.

We used the massive amount of statistics from these calculations to quantify sources of uncertainty. Our uncertainties were distributed bimodally with a mean of ~2 kcal/mol. Sources of larger uncertainties include number of alchemical atoms, charge changes, and slow DOFs.