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PLM-T3SE:使用蛋白质语言模型嵌入器准确预测III型分泌效应器

Mengru Gao¹, Chen Song¹, Taigang Liu¹

¹College of Information Technology, Shanghai Ocean University, Shanghai, China.

Journal of cellular biochemistry

|August 21, 2024

概括

我们开发了PLM-T3SE,一种使用蛋白质语言模型的计算方法,用于准确识别III型分泌因子 (T3SE). 这种方法显著提高了对细菌毒性因素的预测准确度.

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:

第三类分泌效应体 (T3SE) 是由第三类分泌系统 (T3SS) 输入宿主细胞的关键细菌蛋白质.
准确识别T3SE对于了解细菌病原和宿主病原相互作用至关重要.
传统的T3SE鉴定实验方法往往耗时且复杂.

研究的目的:

开发一种高效,准确的计算方法,用于预测III型分泌因子 (T3SE).
利用预训练的蛋白质语言模型 (PLM) 的力量,提高T3SE识别.
改进现有的T3SE预测计算方法.

主要方法:

利用蛋白质语言模型 (PLM) 嵌入和位置特定得分矩阵 (PSSM) 进化特征来表示蛋白质序列.
采用极端梯度增强 (XGBoost) 算法来根据重要性进行特征选择.
开发了一个多层感知器 (MLP) 神经网络,用于使用最佳特征集进行最终的T3SE预测.

主要成果:

拟议的PLM-T3SE方法在一个独立的测试数据集上实现了98.1%的高精度.
与现有的最先进的T3SE预测模型相比,表现出卓越的性能,精度改进范围从1.8%到42.4%.
突出了PLM嵌入在特征T3SE用于计算预测的有效性.

结论:

PLM-T3SE为T3SE预测提供了一个高度准确和高效的计算工具.
蛋白质语言模型显示出识别细菌毒性因子的巨大潜力.
这些发现强调了生物信息学在研究细菌病原体方面的进步.

关键词:

预测T3SE的预测功能选择功能选择多层感知器多层感知器位置特定的评分矩阵.蛋白质语言模型

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