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使用预训蛋白语言模型和合体学习模型准确预测毒性因素

Guanghui Li¹, Jian Zhou², Jiawei Luo³

¹School of Information and Software Engineering, East China Jiaotong University, Nanchang, 330013, China. ghli16@hnu.edu.cn.

|May 21, 2025

概括

一个新的计算模型,PLMVF,通过分析蛋白质序列和结构来识别细菌毒性因子 (VF). 这种方法克服了传统方法的局限性,有助于开发新的抗病毒疗法.

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.
药物发现药物发现

背景情况:

抗生素耐药性需要针对细菌毒性因子 (VFs) 的新疗法.
目前的VF识别方法主要依赖于序列相似性,无法捕获远程同源性.
序列分析的局限性阻碍了新型VF目标的发现.

研究的目的:

开发一种先进的计算方法来识别细菌毒性因子.
克服基于序列的分析在检测远程同质性的局限性.
为发现新型抗病毒药物标提供一个工具.

主要方法:

开发了一种蛋白质语言模型和集体学习方法 (PLMVF) 用于VF识别.
使用ESM-2提取的序列特征和使用ESMFold的结构特征.
集成的序列功能与预测TM-scores (从TM-predictor模型) 来进行增强的预测.

主要成果:

在毒性因子鉴定中,PLMVF的准确率达到86.1%.
该模型在多个评估指标上显著优于现有方法.
通过结构相似性预测,证明了捕获隐藏的远程同类信息的能力.

结论:

PLMVF模型提供了一种有效的计算方法来识别毒性因子.
这种工具有助于开发新的抗病毒疗法.
为控制致病性细菌感染提供了一个有前途的方法.

关键词:

组合学习学习组合学习蛋白质语言模型的模型远程同类学远程同类学在TM-score上取得的成绩.病毒性因素预测预测

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