AcidBasePred:基于深度学习的蛋白酸碱耐受性预测平台

AcidBasePred: a protein acid-base tolerance prediction platform based on deep learning

摘要:

酶的结构和活性受环境pH值的影响。了解酶对极端pH值的适应机制并进行区分，对于阐明酶的分子机制和工业应用具有重要意义。本研究利用ESM-2蛋白质语言模型对最适pH值大于等于9和/或小于等于5的微生物的分泌蛋白进行编码，分别获得了47 725条和66 079条数据。在此基础上，本研究构建了一个基于氨基酸序列判别蛋白酸碱耐受性的深度学习模型。该模型准确率显著超过其他方法，在测试集上的整体准确率为94.8%，精确率为91.8%、召回率为93.4%。同时搭建了一个web预测平台(https://enzymepred.biodesign.ac.cn)，用户可以直接提交酶的蛋白质序列，预测其酸碱耐受性。本研究加速了酶在生物技术、制药和化工等多个领域的应用进程，为工业酶的快速筛选与优化提供了强有力的工具。

关键词:酶;蛋白质序列;酸碱耐受性;深度学习;预测平台

Abstract:

The structures and activities of enzymes are influenced by pH of the environment. Understanding and distinguishing the adaptation mechanisms of enzymes to extreme pH values is of great significance for elucidating the molecular mechanisms and promoting the industrial applications of enzymes. In this study, the ESM-2 protein language model was used to encode the secreted microbial proteins with the optimal performance above pH 9 and below pH 5, which yielded 47 725 high-pH protein sequences and 66 079 low-pH protein sequences, respectively. A deep learning model was constructed to identify protein acid-base tolerance based on amino acid sequences. The model showcased significantly higher accuracy than other methods, with the overall accuracy of 94.8%, precision of 91.8%, and a recall rate of 93.4% on the test set. Furthermore, we built a website (https://enzymepred.biodesign.ac.cn), which enabled users to predict the acid-base tolerance by submitting the protein sequences of enzymes. This study has accelerated the application of enzymes in various fields, including biotechnology, pharmaceuticals, and chemicals. It provides a powerful tool for the rapid screening and optimization of industrial enzymes.

Key words:enzyme;protein sequence;acid-base tolerance;deep learning;prediction platform