Zero-shot protein stability prediction by inverse folding models: a free energy interpretation
By: Jes Frellsen , Maher M. Kassem , Tone Bengtsen and more
Potential Business Impact:
Improves protein stability predictions for new medicines.
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.
Similar Papers
Protein Inverse Folding From Structure Feedback
Machine Learning (CS)
Designs proteins that fold into specific shapes.
Exploring zero-shot structure-based protein fitness prediction
Quantitative Methods
Predicts how protein changes affect health.
Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
Quantitative Methods
Fixes protein predictions for better medicine.