预测与心血管疾病相关的微生物中的蛋白质-蛋白质相互作用,使用深度否定自编码器和进化信息
Senyu Zhou1, Jian Luo1, Mei Tang1
1Cardiovascular Department, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha, Changsha Hospital of Hunan Normal University), Changsha, China.
Frontiers in pharmacology
|March 26, 2025
概括
一个新的计算模型,DAEPPI,使用进化数据准确预测蛋白质与蛋白质相互作用 (PPI). 该工具有助于了解心血管疾病机制,并确定潜在的治疗点.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 蛋白质组学是指蛋白质组学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物过程至关重要,特别是在与心血管疾病相关的微生物中.
- 实验性PPI检测是耗时和昂贵的,需要计算解决方案.
研究的目的:
- 引入一个新的计算模型,DAEPPI,用于预测PPI.
- 评估DAEPPI在心血管疾病研究中的性能和适用性.
主要方法:
- 开发了DAEPPI,使用了深度否定自动编码器和CatBoost算法.
- 利用来自蛋白质序列的进化信息进行预测.
- 验证了酵母和人类数据集的模型.
主要成果:
- 实现了高预测准确度:97.85%的酵母和98.49%的人类数据.
- 与现有方法相比,表现出优越的性能.
- 展示了心血管疾病研究中的潜在应用.
结论:
- DAEPPI是PPI预测的一个强大而可靠的工具.
- 该模型可以推进蛋白质组学研究,并有助于确定心血管疾病的治疗点.
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