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语言嵌入和生物特征的对比学习用于交叉模式编码和效应器预测

Yue Peng¹, Junze Wu¹, Yi Sun¹

¹State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.

Nature communications

|February 3, 2025

概括

一个新的模型,CLEF,通过将语言模型与生物数据相结合,改善了细菌毒性蛋白的预测. 这一进步有助于理解微生物的致病性,并开发新的疗法.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

识别细菌毒性蛋白对于理解病原性和开发治疗方法至关重要.
蛋白质语言模型 (PLMs) 在预测这些蛋白质方面表现有前途,但面临准确性挑战.
现有的方法在全面的效应器预测所需的灵敏度和特异性方面扎.

研究的目的:

引入语言嵌入和生物特征 (CLEF) 的对比学习,这是一种用于增强效应器预测的新型模型.
通过使用对比学习,将预先训练的PLM表示与补充的生物特征集成.
为了提高预测细菌分泌效应蛋白的准确性和灵敏性.

主要方法:

开发了CLEF,一种采用对比学习的模型,用于合并具有多种生物特征的PLM嵌入.
利用跨模式的生物特征来丰富上下文化蛋白质嵌入.
评估了CLEF与肠道病原体最先进模型的性能.

主要成果:

在预测III型,IV型和VI型分泌效应体 (T3SE/T4SE/T6SE) 方面,CLEF显著优于现有模型.
在肠道出血性大肠杆菌中获得了所有实验验证的效应因子的认可,在沙门氏菌Typhimurium中获得了很高的比例.
实验验证确认了12个预测的T3SE和11个预测的T6SE在Edwardsiella piscicida.
在CLEF框架内集成的奥米克数据,以分析效应体-效应体相互作用并识别殖民基因.

结论:

CLEF有效地弥合了in silico PLM能力和实验生物数据之间的差距.
该模型为解决复杂的生物预测任务提供了一个强大的蓝图.
CLEF提高了对微生物病原性的理解,并促进了治疗策略的制定.

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