Accurate prediction of protein stability changes from single mutations using self-distillation and antisymmetric constraint strategies

Abstract

Computational approaches for accurately predicting protein stability changes upon residue mutations are crucial for protein engineering and design. Sequence-based methods are easier to apply to large-scale proteins since they do not rely on high-quantity structures. However, existing sequence-based approaches struggle to capture structural changes, resulting in lower performance compared to structure-based methods. In this study, we propose DPStab, a sequence-based deep learning solution that accurately predicts protein stability changes upon single residue mutations. DPStab transfers a protein large language model as a core component and incorporates a cross-attention mechanism to capture the contact changes around mutated positions for {Delta}{Delta}G and {Delta}Tm prediction. To address data imbalance and the antisymmetric nature of mutation effects, DPStab employs a self-distillation inference strategy under the supervision of an antisymmetric constraint. Benchmarking demonstrates that DPStab achieves state-of-the-art performance in both {Delta}{Delta}G and {Delta}Tm prediction. Practical evaluations confirm DPStab's capability in accurately ranking protein stability on large-scale datasets and effectively identifying critical structural contacts impacting stability. More experiments on extensive cDNA display proteolysis data demonstrate the significant contributions of self-distillation and antisymmetric constraint strategies.