A unified predictor of protein stability changes across all mutation types via implicit structure learning
TL;DR
UniStab is introduced, an end-to-end framework for predicting stability changes across all mutation types by leveraging the implicit geometric reasoning of a pre-trained folding model and demonstrates state-of-the-art performance, particularly in the challenging scenarios of multi-point mutations and indels.
Abstract
Prediction of protein stability change caused by amino acid substitutions or indels (insertions/deletions) is crucial for protein engineering. While current models excel at single-point substitutions, they struggle with multi-point mutations and indels due to simplistic additivity assumptions and the inability to model backbone conformational changes. To address these limitations, we introduce UniStab, an end-to-end framework for predicting stability changes across all mutation types. By leveraging the implicit geometric reasoning of a pre-trained folding model, UniStab effectively captures non-additive epistatic interactions and local backbone rearrangements without the prohibitive cost of explicit structure generation. Evaluated on a comprehensive benchmark, UniStab demonstrates state-of-the-art performance, particularly in the challenging scenarios of multi-point mutations and indels. Beyond predictive accuracy, UniStab provides interpretable structural insights and effectively guides the design of stabilized variants, facilitating its potential utility in rational protein engineering.