图形神经网络与预训练的蛋白语言模型集成,用于预测人-病毒蛋白-蛋白相互作用
Linyang Jiang1, Xiaodi Yang2, Xiaokun Guo1
1State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Briefings in bioinformatics
|September 8, 2025
概括
DeepGNHV使用一种新的框架准确预测人类病毒蛋白质-蛋白质相互作用 (PPI). 该工具显示了新病毒的强烈概括,并有助于识别HPV的宿主因素.
科学领域:
- 计算生物学 计算生物学
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
背景情况:
- 了解人-病毒蛋白-蛋白相互作用 (PPI) 对于对抗病毒感染至关重要.
- 现有的方法与新型病毒威胁作斗争,需要先进的预测工具.
研究的目的:
- 开发DeepGNHV,用于预测人类病毒PPI的先进框架.
- 为了提高涉及新兴病毒病原体的相互作用的检测.
主要方法:
- 集成了一个预训练的蛋白质语言模型,具有AlphaFold2衍生的结构特征.
- 利用图表注意力网络进行端到端的PPI预测.
- 在扰乱和高可信度预测中验证了稳定性.
主要成果:
- 与最先进的方法相比,DeepGNHV表现出优越的预测性能.
- 获得了强大的概括能力,用于预测与新型病毒蛋白的相互作用.
- 确定了与高风险人类乳头瘤病毒 (HPV) 相互作用的特定宿主因素.
结论:
- DeepGNHV为人类病毒PPI预测提供了一个强大的和可通用的方法.
- 该框架有助于理解病毒感染机制,并制定有针对性的干预措施.
- 案例研究确定了特定的PPI所涉及的关键残留物,为分子机制提供了洞察力.
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