BlindDDG: A Blind Evaluation-Based Method for Protein Mutation Stability Prediction

Abstract

Protein thermodynamic stability changes upon mutation profoundly affect protein structure and biological function. Recent advances in deep learning and protein language models have provided powerful tools for computational analysis of mutation-induced stability changes. However, most existing methods focus on quantitative regression of free-energy changes, which places strong dependence on precise experimentally measured ΔΔG values, although reliably determining the direction of stability change is itself an important objective in many practical applications. Here, we propose BlindDDG, a novel framework that reformulates protein mutation stability prediction from quantitative ΔΔG estimation to direction-oriented stability change classification. First, we introduce a blind-evaluation strategy that models mutation-induced changes by learning the representation difference between wild-type and mutant proteins, rather than relying solely on their individual intrinsic properties. This formulation focuses on the direction of stability changes and reduces the dependence on precise quantitative ΔΔG measurements. Second, a Siamese architecture with shared parameters is employed to encode the wild-type and mutant states and capture subtle mutation-associated differences. Third, a local structure-aware cross-attention mechanism is developed to integrate mutation-centered structural information with global protein language model representations, enabling effective multimodal feature interaction. Experimental results on the independent S669 benchmark demonstrate that BlindDDG achieves strong classification performance, obtaining an F1 score of 0.5449, an MCC of 0.3929, an accuracy of 0.7721, and a recall of 0.5417 for stabilizing mutation identification. BlindDDG outperforms all evaluated methods in F1 score, MCC, accuracy, and recall, highlighting its effectiveness in capturing mutation-induced stability changes, particularly for imbalanced stability classification.