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T4SEpp:一个管道整合蛋白质语言模型来预测细菌类型IV分泌的效应器

Yueming Hu¹, Yejun Wang^2,3, Xiaotian Hu¹

¹Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.

Computational and structural biotechnology journal

|February 8, 2024

概括

识别细菌IV型分泌因子 (T4SE) 是了解病原性的关键. 一种名为T4SEpp的新模型使用蛋白质语言模型准确预测T4SE,有助于研究疾病机制.

科学领域:

微生物学和分子生物学
生物信息学和计算生物学

背景情况:

病原性细菌利用IV型分泌系统 (T4SSs) 将效应蛋白 (T4SE) 注入宿主细胞,推动疾病的发生.
准确识别T4SE对于阐明细菌毒性机制至关重要,但仍然是一个重大的计算挑战.

研究的目的:

开发和评估一种用于预测细菌IV型分泌因子 (T4SE) 的新型计算模型.
利用先进的蛋白质语言模型来加强T4SE识别和致病性研究.

主要方法:

开发了T4SEpp,一个集成三个模块的模型:对已知T4SEEs/domains的同质性搜索,信号序列特征分析和预训练的蛋白质语言模型.
利用来自六个预训练的蛋白质语言模型的全长嵌入功能来训练和比较分类器性能.
使用独立验证数据集对最先进的工具进行T4SEpp的评估.

主要成果:

在独立的数据集上,T4SEpp实现了高预测性能,准确度为~0.98和特异性为~0.99.
该模型的性能优于现有的最先进的T4SE预测软件.
T4SEpp在*Helicobacter pylori*中发现了13种T4SE,其中包括12种新型潜在作用因子,其中11种可能与人体蛋白质相互作用.

结论:

T4SEpp提供了一种强大而准确的解决方案,用于识别细菌T4SE,大大推进了细菌病原性研究.
在*H. pylori*中发现的潜在T4SE需要进一步研究它们在疾病中的作用.
T4SEpp是免费可用的,促进了微生物病原和效应物识别的更广泛研究.

关键词:

深度学习是一种深度学习.杆菌T4SEEs是Helicobacterpylori的一种类型.蛋白质语言模型的模型在T4SE预测预测.在T4SEpppp.这就是T4SS.

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