Work

Self-distillation and antisymmetric constraints enable accurate prediction of protein stability changes from single mutations

Year: 2026

Type: article

Abstract: Accurately predicting protein stability changes upon residue-level mutations is essential for protein engineering and design. However, previous computational methods are typically designed for specific tasks and struggle to generalize across different stability systems, preventing the accurate guidance of protein mutagenesis. Sequence-based methods are easier to apply to large-scale proteins since they do not rely on high-quality 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 DeepTherm, a sequence-based deep learning solution to model changes in folding free energy (ΔΔ G ) and melting temperature (Δ T m ) resulting from single-point mutations. DeepTherm transfers a protein large language model as a core component and incorporates a cross-attention mechanism to capture the contact-map changes around mutated positions. To address data imbalance and the antisymmetric nature of mutation effects, DeepTherm employs a self-distillation inference strategy under the supervision of an antisymmetric constraint. Benchmarking across multiple datasets shows that DeepTherm achieves state-of-the-art accuracy in both ΔΔ G and Δ T m predictions. Furthermore, practical evaluations confirm DeepTherm’s capability in accurately ranking protein stability on large-scale datasets and effectively identifying critical structural contacts impacting stability. Detailed analysis on cDNA display proteolysis data further highlights the critical importance of self-distillation and antisymmetric constraints in enhancing predictive performance. The source code can be found in https://github.com/CSUBioGroup/DPStab .

Source: Tsinghua Science & Technology

DOI: 10.26599/tst.2026.9010100

Authors: Wenkang Wang, Wentao Yu, Qiurong Yang, Ruiqing Zheng, Min Li

Institutions: Central South University, National University of Singapore, University of Michigan, Cancer Science Institute of Singapore

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Central South University

Award: G8663586063