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对蛋白质复合体突变时结合自由能量变化的预测因子的系统评估

Yu Zhang^1,2,3, Yunjiong Liu^1,2,3, Yulin Zhang^2,3

¹Institute of Artificial Intelligence, School of Informatics, Xiamen University, No. 4221, Xiang'an South Road, Xiang'an District, Xiamen City, 361005, Fujian Province, China.

Briefings in bioinformatics

|December 8, 2025

概括

预测蛋白质突变对结合自由能量 (ΔΔG) 的影响至关重要,但当前的工具在概括和复杂的情况下扎. 这项研究揭示了在识别稳定突变方面的系统偏见和不良表现,突出了对更好的数据集和算法的需求.

科学领域:

计算生物学计算生物学
生物物理学的生物物理.
蛋白质工程是指蛋白质工程.

背景情况:

来自突变的结合自由能量变化 (ΔΔG) 的准确预测对于疾病机制研究和治疗抗体设计至关重要.
破坏分子相互作用的错误突变占病原性突变的60%左右.
现有的ΔΔG预测器显示基准性能,但缺乏对复杂突变场景的概括性和适应性.

研究的目的:

系统地评估主流 ΔΔG 预测器的性能和局限性.
评估各种突变类型和蛋白质微环境中的预测者概括,度量对齐和适应性.
为改善 ΔΔG 预测方法提供见解.

主要方法:

评估了八个领先的ΔΔG预测器 (基于物理能量功能和基于机器学习的物理能量).
构建了一个独立的评估数据集.
采用多维度指标,包括回归精度和分类能力.
分析了突变类型,稳定性类别和局部蛋白质结构的性能变化.

主要成果:

>60%的评估预测因素 (5/8) 显示出偏向于高估突变不稳定性.
预测者对识别稳定突变 (ΔΔG <-0.5 kcal/mol) 的回忆能力有限 (<0.1).
预测性能受到突变部位的局部蛋白质结构的显著影响.

结论:

目前的ΔΔG预测器在一般化和适应性方面存在局限性,特别是在复杂的突变场景中.
对于未来的突破,需要平衡,标准化的数据集和先进的算法 (例如,局部-全球融合).
结果为优化和实际应用分子生物学和药物设计中的ΔΔG预测工具提供了一个参考.

关键词:

具有约束力的自由能量变化 (Δ Δ G)独立评估集是一个独立的评估集.预测器评估预测器评价蛋白质复杂的突变突变

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