PTMs_Closed_Search:多个翻译后修改 封闭搜索使用减少的搜索空间和转移的FDR
Yury Yu Strogov1, Sergey A Spirin2,3,4, Mark V Ivanov5
1Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia.
Proteomes
|February 20, 2026
概括
本研究引入了针对MS/MS数据的翻译后修改 (PTM) 分析的优化Python管道,改善了标识和蛋白质覆盖. 这种新方法提高了错误发现率 (FDR) 估计,以实现更可靠的PTM检测.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 目前在MS/MS数据中进行的翻译后修改 (PTM) 搜索使用开放修改搜索 (OMS) 或封闭搜索 (CS) 算法.
- OMS允许许多PTM和未知的质量转移,而CS更敏感,但PTM范围有限.
- 需要更全面和敏感的PTM识别方法.
研究的目的:
- 为自动化,顺序的PTM搜索开发一个优化的Python算法.
- 为了提高错误发现率 (FDR) 估计对PTM分析的意义.
- 改进MS/MS数据中的类鉴定和蛋白质覆盖率.
主要方法:
- 基于IdentiPy搜索引擎的优化Python算法被开发用于自动连续的PTM搜索.
- 该算法利用公共数据库和定制蛋白质列表进行PTM注释.
- 修改的FDR计算与spline近似和错误传播被实施用于数据过.
主要成果:
- 该管道展示了与未经修改的和蛋白质的现有方法相似的结果.
- 确定了13种不同类型的PTM,增加了相对蛋白质覆盖范围.
- 与单独的FDR计算相比,基于spline的FDR过方法产生了更多的鉴定.
结论:
- 开发的管道为MS/MS数据中的多个PTM搜索提供了强大的解决方案.
- 它可以作为独立应用程序使用,也可以集成到现有的数据分析平台中.
- 该方法提高了PTM识别的准确性和效率.
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