IQSPred-PLM:基于蛋白质语言模型的可解释的定量感应预测模型
Yusen Su1, Qingyang Guo1, Taigang Liu2
1College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China.
Interdisciplinary sciences, computational life sciences
|August 26, 2025
概括
这项研究介绍了IQSPred-PLM,这是使用蛋白质语言模型和卷积神经网络预测定量感应 (QSP) 的新模型. 该模型在识别这些关键细菌信号分子方面具有很高的准确性.
科学领域:
- 微生物学
- 生物信息学
- 计算生物学
背景情况:
- 质量感应 (QS) 是一种细胞间通信机制,可以调节细菌的合作行为.
- 质量感应 (QSP) 是重要的信号分子,特别是在阳性细菌中,影响病毒性和生物膜形成等功能.
- 现有的QSP预测工具需要提高性能和可解释性.
研究的目的:
- 开发一种新的高性能模型来预测QSP.
- 提高QSP识别的准确性和可解释性.
- 利用先进的深度学习技术进行细菌信号分子预测.
主要方法:
- 蛋白语言模型 (PLM) 的整合,特别是ESM-2,用于序编码.
- 应用多尺度残余卷积神经网络 (MSRes-CNN) 进行特征提取.
- 使用适应式重量调制 (AWM) 模块的动态功能集成,然后使用完全连接的分类网络.
主要成果:
- 在一个基准数据集上,IQSPred-PLM取得了出色的预测性能.
- 关键性能指标包括97.50%的准确性 (ACC),0.951的马修斯相关系数 (MCC) 和0.990的ROC曲线下的面积 (AUC).
- 案例研究和可解释性分析证实了该模型的有效性.
结论:
- 在QSP预测准确性和可解释性方面,IQSPred-PLM是一个显著的进步.
- 该模型的性能突显了用于生物序列分析的PLM和CNN集成的潜力.
- 这种工具有助于了解细菌的交流和制定有针对性的干预措施.
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