English

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

Machine Learning 2025-10-28 v2 Artificial Intelligence Biomolecules Machine Learning

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.

Keywords

Cite

@article{arxiv.2506.05596,
  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},
  journal= {arXiv preprint arXiv:2506.05596},
  year   = {2025}
}