SOPE-MsL:协同优化的蛋白质语言模型嵌入与多尺度学习用于可解释的蛋白质-小分子结合点预测
Zhen Feng1,2,3, Gen Li4, Xiaoya Guan5
1Hua Tuo Chinese Medicine Research Institute of Anhui Province, Bozhou 236800, China.
Journal of chemical information and modeling
|February 12, 2026
概括
我们开发了SOPE-MsL,这是一种新的计算方法,它融合了多个蛋白质语言模型嵌入,以准确地预测结合部位. 这种方法增强了对药物设计中蛋白质-小分子相互作用的理解.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 药物发现 药物发现 药物发现
背景情况:
- 蛋白质-小分子相互作用对于细胞功能和药物开发至关重要.
- 识别结合残留物是理解分子识别和合理药物设计的关键.
- 目前的实验方法昂贵且有限,而计算方法缺乏全面的残留物表示.
研究的目的:
- 开发一种准确和可解释的计算方法来预测蛋白质结合点.
- 为了利用多个蛋白质语言模型 (PLM) 嵌入的协同效应.
- 改进现有的结构感知序列分析方法.
主要方法:
- SOPE-MsL集成了PLM嵌入式融合与多尺度学习,用于绑定站点预测.
- 系统评估确定ProstT5和Ankh嵌入为一个有效的对.
- 采用了结合多尺度卷积运算和注意力机制的网络,以及加权的焦点损失来处理类不平衡.
主要成果:
- 在跨多个数据集的绑定站点预测中,SOPE-MsL实现了竞争性表现.
- 通过t-SNE和SHAP分析,协同嵌入融合在单个模型表示上表现出优势.
- 该方法提供了残留水平的解释性.
结论:
- SOPE-MsL提供了一个强大的和可解释的工具,用于识别蛋白质-小分子相互作用地点.
- 该方法推进了用于药物发现的结构意识序列分析.
- 嵌入多尺度学习的PLM融合是绑定站点预测的一个有希望的策略.
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