mCNN-GenEfflux:通过使用生成蛋白与多个窗口卷积神经网络相结合,增强预测Efflux蛋白及其超级家族
Muhammad Hussain1, Yu-Yen Ou2, Quang Thai Ho3
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan.
Computational biology and chemistry
|July 21, 2025
概括
一个新的GenEfflux框架使用ProtGPT2生成的序列和多窗口CNN来改进细菌流量输送器分类,增强抗生素耐药性研究.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 流体输送器是细菌抗生素耐药性的关键,可以去除有害化合物.
- 存在五个家族 (ABC,MFS,MATE,RND,SMR),但序列的变化和糟糕的注释阻碍了分类.
- 传统的方法难以应对废流输送器家族的复杂性.
研究的目的:
- 为了提高排放输送器分类的准确性.
- 为应对序列可变性和有限的功能注释所带来的挑战.
- 开发一种新的框架,将生成模型与先进的CNN集成在一起.
主要方法:
- 利用ProtGPT2生成多样化的排泄蛋白序列,模仿自然变异.
- 使用多窗口卷积神经网络 (mCNN) 来进行特征提取.
- 集成生成序列扩展与位置特定评分矩阵 (PSSMs) 用于在GenEfflux框架中的进化分析.
主要成果:
- 在所有流量载体类别中,GenEfflux显著优于基线深流量模型.
- 乙类敏感性从0.5385提高到0.9999;MCC从0.4397增加到0.9327.
- C类准确度从0.8977上升到0.9668;MCC从0.7668提高到0.9331.
结论:
- 由ProtGPT2生成的序列具有功能相关性,并提高了分类性能.
- 该GenEfflux框架提供了一个强大的方法,用于排泄蛋白质分析.
- 这项工作促进了对抗生素耐药性机制的理解,并有助于开发新的策略.
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