使用深度神经网络与多源蛋白语言模型和挤压激发注意力机制的菌体宿主预测
IEEE journal of biomedical and health informatics
|June 24, 2025
概括
菌体治疗 (PT) 为抗生素提供了一个有前途的替代方案. 这项研究介绍了PHPRBP,一种使用菌体受体结合蛋白 (RBPs) 的深度学习模型,用于准确的菌体宿主预测,克服了当前方法的局限性.
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:
- 抗微生物耐药性需要新的治疗策略,如菌体疗法 (PT).
- 准确的菌体宿主预测 (PHP) 对于有效的PT至关重要,菌体与特定的细菌病原体相匹配.
- 现有的计算PHP方法往往忽视了菌体受体结合蛋白 (RBPs) 的进化多样性和局部特征.
研究的目的:
- 开发一种新的深度神经网络,PHPRBP,用于基于菌体RBP的准确菌体宿主预测.
- 为了解决数据稀缺问题,并捕捉RBPs的各种特征,以改进PHP.
- 提高计算方法的性能,以识别特定细菌宿主的治疗菌体.
主要方法:
- 使用预训练的蛋白质语言模型 (ESM2,ProtT5) 来生成RBP的多源嵌入表示.
- 采用了自适应合成技术来增加代表性不足的数据样本,减轻数据稀缺性.
- 设计了一个深度神经网络,包含一个卷积神经网络用于局部特征提取,以及一个用于特征增强的挤压和激发注意力机制.
主要成果:
- 通过使用先进的蛋白质语言模型,PHPRBP有效地从RBP中学习了多样化和互补的特征.
- 与现有的最先进的方法相比,该模型在菌体宿主预测方面表现出卓越的性能.
- 精确的宿主预测在属和物种层面都得到了实现,验证了模型的稳定性.
结论:
- PHPRBP代表了对菌体宿主计算预测的重大进步.
- 蛋白质语言模型和注意力机制的整合提高了PHP的准确性和可靠性.
- 这种方法提供了一种更高效和有效的工具,用于推进抗菌素耐药性的菌体治疗应用.
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