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DeepSecMS通过Cys-to-Sec代理培训推进了基于DIA的蛋白质组概况

Chenfang Si¹, Yamei Yuan^1,2, Yu Zong³

¹Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 100 Haike Rd., Shanghai, 201210, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|July 22, 2025

概括

DeepSecMS是一种新的深度学习方法,通过准确预测特征来增强蛋白的识别. 这种方法有助于发现新的蛋白和罕见的修饰.

科学领域:

生物化学生物化学
蛋白质组学是指蛋白质组学.
生物信息学是一种生物信息学.

背景情况:

单蛋白质是含有单半氨酸 (Sec) 的罕见蛋白质,对各种生物功能至关重要.
已知只有25种人类蛋白,因此需要先进的方法来进行全面的蛋白组分析.
现有的SecMS和SIS等工具为发现新的单蛋白提供了潜力.

研究的目的:

开发一种深度学习方法,DeepSecMS,用于准确识别蛋白.
增强新型蛋白和罕见的翻译后修改的发现.
改进蛋白质组的综合分析.

主要方法:

开发了DeepSecMS,这是一种用于标识蛋白的深度学习方法.
使用含氨酸的代理培训策略生成理论含氨酸的氨酸.
集成的DeepSecMS与数据独立采集 (DIA) 质谱.

主要成果:

DeepSecMS准确地预测含有Sec的关键特征,包括MS2光谱,保留时间 (RT) 和离子移动性 (IM).
与DIA方法的整合显著改善了在各种组织中已知的蛋白的识别.
识别了许多具有高得分潜力的新型氨基蛋白,扩大了已知的人类氨基蛋白组.

结论:

DeepSecMS是一个强大的工具,用于推进蛋白研究和发现新蛋白.
代理培训策略对分析其他罕见的翻译后修改具有前途.
这项工作显著提高了全面分析蛋白质组的能力.

关键词:

在SecMS中使用SecMS.数据独立获取数据独立获取数据深度学习是一种深度学习.单半氨酸是一种单半氨酸.单蛋白是一种单蛋白.

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