E2EATP:通过蛋白质语言模型嵌入进行快速和高精度的蛋白质-ATP结合残余预测
1School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China.
Journal of chemical information and modeling
|December 21, 2023
概括
我们开发了E2EATP,这是一种深度学习模型,可以使用蛋白质语言模型准确预测蛋白质中的腺三酸盐 (ATP) 结合位点. 这种方法通过提高ATP结合位点识别的速度和准确性来增强药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
背景情况:
- 识别腺三酸盐 (ATP) 结合部位对于理解蛋白质功能和推动药物发现至关重要.
- 目前用于预测ATP结合位点的计算方法受到特征表示质量的限制,需要改进预测性能.
研究的目的:
- 引入E2EATP,这是一个端到端的深度学习模型,旨在提高ATP结合部位预测的准确性.
- 利用先进的蛋白质语言模型来提取歧视性序列信息.
主要方法:
- 使用预训练的蛋白质语言模型 (ESM2) 来从蛋白质序列中提取高级表示.
- 开发了一个与ESM2集成的残余卷积神经网络,用于ATP结合部位的预测.
- 使用加权焦点损失函数来解决模型训练期间数据不平衡的问题.
主要成果:
- 与独立数据集上的现有最先进方法相比,E2EATP表现出卓越的性能,实现了更高的马修相关系数和AUC值.
- 该模型表现出明显更快的预测速度 (每种蛋白质约为0.05秒).
- 分析证实,预训练的蛋白质语言模型是提取歧视性序列信息的关键.
结论:
- E2EATP提供了一种高度准确和高效的计算方法,用于预测蛋白质ATP结合位点.
- 该模型依赖于预训练的蛋白质语言模型,这代表了该领域的重大进展.
- E2EATP可以作为一个独立的包用于学术用途.
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