NeurIPS 2025poster0 citations

Zero-shot protein stability prediction by inverse folding models: a free energy interpretation

Jes Frellsen, Maher M. Kassem, Tone Bengtsen, Lars Olsen, Kresten Lindorff-Larsen, Jesper Ferkinghoff-Borg, Wouter Boomsma

Abstract

Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse folding model and the free-energy considerations underlying thermodynamic stability remains incompletely understood. A better understanding would be of interest not only from a theoretical perspective, but also potentially provide the basis for stronger zero-shot stability prediction. In this paper, we take steps to clarify the free-energy foundations of inverse folding models. Our derivation reveals the standard practice of likelihood ratios as a simplistic approximation and suggests several paths towards better estimates of the relative stability. We empirically assess these approaches and demonstrate that considerable gains in zero-shot performance can be achieved with fairly simple means.

protein stabilityinverse folding modelszero-shot predictionfree energyinterpretability
BibTeX
@inproceedings{
frellsen2025zeroshot,
title={Zero-shot protein stability prediction by inverse folding models: a free energy interpretation},
author={Jes Frellsen and Maher M. Kassem and Tone Bengtsen and Lars Olsen and Kresten Lindorff-Larsen and Jesper Ferkinghoff-Borg and Wouter Boomsma},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=5nBwN6XQOD}
}