修正折叠态失衡问题，提升蛋白质稳定性预测精度
Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
- 引入质量守恒校正项，改进未折叠态能量估计
- 校正后模型在多个数据集上准确率提升显著
- 适合需要快速精准预测的药物与酶设计场景
单点突变引起的蛋白质稳定性变化预测在计算生物学中至关重要，尤其在药物研发、酶重构和遗传病分析中。尽管深度学习方法推动了该领域进展，但其资源消耗大，难以融入常规工作流。相比之下，势能类方法速度快、直观且高效，但通常忽略未折叠态的自由能变化，违背质量守恒，影响准确性。本研究证明，引入质量守恒校正（MBC）以考虑未折叠态的能量变化，可显著提升此类方法性能。虽然许多机器学习模型部分建模了该平衡，但对未折叠态的精细化表征仍有改进空间。
原文摘要 · Abstract (English)
The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pushed the field forward, their use in standard workflows remains limited due to resource demands. Conversely, potential-like methods are fast, intuitive, and efficient. Yet, these typically estimate Gibbs free energy shifts without considering the free-energy variations in the unfolded protein state, an omission that may breach mass balance and diminish accuracy. This study shows that incorporating a mass-balance correction (MBC) to account for the unfolded state significantly enhances these methods. While many machine learning models partially model this balance, our analysis suggests that a refined representation of the unfolded state may improve the predictive performance.
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