NAD_MCNN:结合蛋白质语言模型和多窗口卷积神经网络来预测NAD+结合部位的脱乙酶
Van-The Le1, Yu-Chen Liu1, Yan-Yun Chang1
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
Chemical biology & drug design
|April 4, 2025
概括
我们开发了一种计算方法,以快速识别NAD+与Sirtuins结合的地方,这些蛋白质对衰老和新陈代谢至关重要. 这种方法加速了对与年龄相关疾病的新治疗点的发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 衰老研究研究 衰老研究
背景情况:
- 锡尔图因是NAD+依赖的脱乙酶,对衰老,新陈代谢和寿命至关重要.
- 绘制NAD+结合部位的地图对于理解Sirtuin功能至关重要,但在实验上具有挑战性.
- 目前用于识别NAD+结合位点的实验方法是耗时的.
研究的目的:
- 开发一种高效的计算方法,用于预测sirtuins中的NAD+结合点.
- 提高对Sirtuin在衰老和新陈代谢中的作用的理解.
- 促进对与年龄有关的疾病的新型治疗点的识别.
主要方法:
- 整合预训练的蛋白质语言模型.
- 多窗口卷积神经网络 (CNN) 的应用.
- 基于蛋白质序列信息的NAD+结合部位的计算预测.
主要成果:
- 在预测NAD+结合位点方面取得了最先进的性能.
- 高AUC值:0.9733的人类素和0.9701其他依赖NAD的脱酶.
- 成功捕获了序列信息和与酶功能相关的各种局部模式.
结论:
- 开发的计算方法提供了一种快速而准确的方法来识别NAD+结合部位.
- 提供了关于Sirtuins在衰老和新陈代谢中的生物学作用的宝贵见解.
- 介绍了一种新的策略,用于发现与衰老相关的疾病中的治疗点.
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