通过对比学习和代理注意力来增强酶委员会数量预测
Wendi Zhao1,2,3,4, Qiaoling Han1,2,3,4, Fan Yang1,2,3,4
1School of Technology, Beijing Forestry University, Beijing, China.
Proteins
|April 2, 2025
概括
使用对比学习和代理注意力,ProteEC-CLA准确地预测了酶功能. 这种新的方法提高了酶注释效率和研究和药物发现的精度.
科学领域:
- 生物化学 生物化学
- 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:
- 准确的酶功能预测对于了解疾病和药物标识是至关重要的.
- 目前的酶委员会 (EC) 数量预测方法在数据库覆盖和序列信息挖掘方面面临局限性,影响注释效率和精度.
研究的目的:
- 引入ProteEC-CLA,这是一个新的模型,用于使用对比学习和代理注意力来预测酶委员会 (EC) 数.
- 通过先进的序列特征提取和分析,提高酶功能注释的准确性和效率.
主要方法:
- ProteEC-CLA使用对比学习来创建正负样本对,改善序列特征提取和未标记数据的利用.
- 整合ESM2预训练的蛋白质语言模型产生了用于功能相关性分析的信息序列嵌入.
- 纳入了Agent Attention机制,以增强在酶序列中的本地细节和全球特征的捕获.
主要成果:
- 在两个独立的数据集上,ProteEC-CLA表现出了卓越的性能.
- 在标准数据集上,在EC4级达到98.92%的准确性.
- 在一个具有挑战性的集群分割数据集上,ProteEC-CLA获得了93.34%的准确性和94.72%的F1分数.
结论:
- ProteEC-CLA准确地预测EC数量到第四级,仅使用酶序列作为输入.
- 该模型显著提高了酶功能注释的效率和精度.
- 在酶学研究和应用中,ProteEC-CLA是非常有效的工具.
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