PHIStruct:在低序列相似性设置下改善菌体与宿主相互作用的预测,使用结构感知蛋白质嵌入
Mark Edward M Gonzales1,2, Jennifer C Ureta1,2,3, Anish M S Shrestha1,2
1Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila 1004, Philippines.
通过结构感知蛋白质嵌入,PHIStruct准确地预测菌体宿主. 这种计算工具的性能优于现有的方法,尤其是低序列相似性,推进了菌体与宿主相互作用的预测.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 微生物学 微生物学
背景情况:
- 目前用于菌体与宿主相互作用预测的仅序列模型缺乏结构信息.
- 这些模型中的蛋白质嵌入不捕捉宿主特异性的关键结构信息信号.
研究的目的:
- 开发一种新的计算工具,PHIStruct,用于预测菌体宿主.
- 将蛋白质结构信息纳入菌体与宿主相互作用预测模型.
主要方法:
- PHIStruct使用来自SaProt蛋白质语言模型的结构意识嵌入.
- 在PHIStruct中的多层感知子预测基于受体结合蛋白结构意识嵌入的宿主属 (ESKAPEE).
主要成果:
- 与现有工具相比,PHIStruct表现出卓越的精度和回忆能力.
- 它在各种信心门和序列相似性中获得了最高和最稳定的F1分数.
- 当序列相似性低于40%时,PHIStruct显示了与非结构信息化的ML工具相比,类平均F1的7%-9%的增加,以及与BLASTp相比5%-6%的增加.
结论:
- 通过整合蛋白质结构信息,PHIStruct有效预测菌体与宿主相互作用.
- 该工具提供了更好的性能,特别是在具有有限序列相似性的场景中.
- PHIStruct代表了对菌体-宿主特异性的计算预测的重大进步.
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