Mass balance approximation of unfolding boosts potential-based protein stability predictions

Predicting protein stability changes upon single-point mutations is crucial in computational biology, with applications in drug design, enzyme engineering, and understanding disease mechanisms. While deep-learning approaches have emerged, many remain inaccessible for routine use. In contrast, potential-like methods, including deep-learning-based ones, are faster, user-friendly, and effective in estimating stability changes. However, most of them approximate Gibbs free-energy differences without accounting for the free-energy changes of the unfolded state, violating mass balance and potentially reducing accuracy. Here, we show that incorporating mass balance as a first approximation of the unfolded state significantly improves potential-like methods. While many machine-learning models implicitly or explicitly use mass balance, our findings suggest that a more accurate unfolded-state representation could further enhance stability change predictions.