ProtPhage:用于菌体病毒蛋白识别和功能注释的深度学习框架.
Yuehua Ou1, Qiyi Chen1, Ningyu Zhong1
1College of Computer Science and Software Engineering, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen, Guangdong, 518060, China.
Briefings in bioinformatics
|June 14, 2025
概括
ProtPhage 通过使用先进的语言模型和新的损失函数,增强了菌体病毒蛋白的识别. 这提高了对抗生素耐药性策略和菌体生物学研究的预测.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 基因组学就是基因组学.
背景情况:
- 抗生素耐药性病原体构成了全球健康威胁.
- 菌体 (菌体) 为抗生素提供了一个可行的替代品.
- 鉴定菌病毒蛋白 (PVPs) 对于理解菌与宿主相互作用至关重要,但由于序列多样性和数据限制,具有挑战性.
研究的目的:
- 开发一个新的框架,ProtPhage,用于准确识别和菌病毒蛋白 (PVPs) 的功能注释.
- 解决PVP预测的挑战,包括高序列多样性和阶级不平衡.
- 为了提高少数类的预测,例如小囊蛋白.
主要方法:
- 利用ProtT5蛋白语言模型进行增强的序列表示.
- 整合了一个不对称的损失函数,以有效地处理数据集中的类不平衡.
- 评估ProtPhage性能与现有方法相比,使用准确度,精度,回忆和F1分数等指标.
主要成果:
- ProtPhage显著改善了"小体"类的预测,比现有的最佳模型高出33.07%的F1得分.
- 与当前最先进的方法相比,在多个评估指标上表现出卓越的表现.
- 成功地将ProtPhage应用于Mycobacterium菌体PDRPxv基因组的案例研究,验证了其实际用途.
结论:
- ProtPhage在计算菌生物学的PVP识别和注释方面建立了一个新的基准.
- 该框架显示了更广泛应用的潜力,包括菌体-宿主预测.
- 先进的深度学习技术,如ProtT5和不对称损失,对于分析复杂的生物数据是有效的.
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