PPAC:使用蛋白质大语言模型预测由氨基酸突变引起的蛋白质-蛋白质亲和力变化
Leilei Zhang1, Xiaofei Zhou2, Lu Liang2
1Nankai University, College of Pharmacy, Jinnan District, No.38 Tongyan Rd Haihe Education Garden, Tianjin 300350, China.
ACS omega
|November 24, 2025
概括
蛋白质大语言模型 (PLMs) 准确地预测了氨基酸突变如何影响蛋白质结合. 这种新的方法优于传统方法,有助于药物设计和理解蛋白质相互作用.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 药物发现 药物发现 药物发现
背景情况:
- 准确预测氨基酸突变对蛋白质-蛋白质结合自由能量的影响,对于药物设计和功能生物学至关重要.
- 传统方法在捕捉复杂的突变效应时经常面临局限性.
研究的目的:
- 开发和验证一种使用蛋白质大语言模型 (PLM) 的新方法,用于预测蛋白质-蛋白质结合自由能的突变效应.
- 为蛋白质相互作用分析建立一个新的最先进的预测模型 (SOTA).
主要方法:
- 利用三个先进的PLM (Esm2,EsmC,ProtT5) 来生成基于序列的蛋白质表示.
- 将PLM表示集成到七个不同的模型架构中,并通过5倍交叉验证选择最佳组合.
- 在大型数据集上训练最终模型 (PPAC),并对9,558个数据点进行评估.
主要成果:
- 基于PLM的方法在预测突变效应方面明显优于传统方法.
- 实现了最先进的 (SOTA) 预测性能.
- 该PPAC模型表现出高精度,并确定了对蛋白质相互作用至关重要的关键残留物.
结论:
- PLM提供了一个强大的工具来表征蛋白质变体,并预测结合的自由能量变化.
- 开发的PPAC模型在蛋白质相互作用建模和药物设计方面取得了重大进展.
- 这种方法提高了参与蛋白质结合的关键残留物的识别,为功能生物学提供了宝贵的见解.
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