msCNN-PLM-FAD:通过使用蛋白质语言表征结合多个可分离窗口的卷积神经网络,增强预测FAD结合位点.
Dinh-Quy Nguyen1, Viet-Thanh Nguyen1, Cam-Hong Ly2
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; College of Information & Communication Technology, Can Tho University, Vietnam.
Analytical biochemistry
|February 28, 2026
概括
这项研究介绍了msCNN-PLM-FAD,这是一种用于预测蛋白质中的FAD结合位点的新型计算模型. 该模型通过提高FAD相关条件的预测准确度来增强疾病药物开发.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 对于理解与黄蛋白相关的疾病,FAD结合部位的分类至关重要.
- 目前用于FAD结合部位预测的现有方法存在局限性,需要改进方法.
研究的目的:
- 开发一个准确的计算模型,用于预测电子运输蛋白中的FAD结合点.
- 利用预训练的蛋白语言模型 (PLM) 和卷积神经网络 (CNN) 进行增强的预测.
主要方法:
- 使用了滑窗特征提取技术,与ESM PLM的嵌入相结合.
- 开发了msCNN-PLM-FAD模型,将基于CNN的窗口扫描与PLM嵌入式集成在一起.
- 在12850个样本的数据集上训练并验证了模型.
主要成果:
- msCNN-PLM-FAD模型实现了高性能,其AUC为0.9614和MCC为0.7491.
- 在类平衡指标,特异性 (0.9836),准确性 (0.9795) 中表现出卓越的性能,同时保持灵敏度 (0.8704).
- 超过了以前的FAD结合部位预测研究.
结论:
- msCNN-PLM-FAD模型显著提高了FAD结合部位预测的准确性.
- 这一进步可以加速开发用于治疗FAD缺陷相关疾病的药物.
- 针对细菌FAD合成的向疗法的潜力,而不会影响人类健康.
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