ESM2_AMP:一种可解释的蛋白质相互作用预测和生物机制发现框架
Yawen Sun1, Rui Wang1, Zeyu Luo1
1College of Life Science, Chongqing Normal University, No. 37 University Town Road, high-tech District, Chongqing 401331, P.R. China.
Briefings in bioinformatics
|August 28, 2025
概括
我们开发了ESM2_AMP, 这是一个用于预测蛋白质相互作用的深度学习框架. 我们的模型通过将高注意力序列段与已知的功能区域联系起来来提高解释性,从而揭示它们在PPI中的作用.
科学领域:
- 计算生物学
- 生物信息学
- 结构生物学
背景情况:
- 预测蛋白与蛋白相互作用对于理解生物过程和蛋白质工程至关重要.
- 对于PPI预测的深度学习模型往往缺乏解释性,阻碍了对其决策过程的理解.
研究的目的:
- 开发一个可解释的深度学习框架来预测二进制蛋白-蛋白相互作用 (PPI).
- 探索序列段特征的贡献及其与PPI中的功能区域的关系.
主要方法:
- 使用ESM2蛋白语言模型从氨基酸序列中提取细分特征.
- 在二进制 PPI 预测中集成了一个变压器模型.
- 开发了两个模型,ESM2_AMPS和ESM2_AMP_CSE,用于分析细分和特殊的代币特征.
主要成果:
- 在ESM2_AMP模型中,高度关注的细分和已知的功能区域之间存在强烈的相关性.
- 注意力权重有效地捕获了生物相关的功能和相互作用相关的信息.
- 验证了模型识别PPI关键特征和功能领域的关键作用.
结论:
- 通过ESM2_AMP框架,可以提高基于序列的PPI预测模型的解释性.
- 提供了PPI中功能序列的调节作用的生物证据.
- 提供了算法优化和计算生物学中的实验验证的见解.
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