Consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is a prerequisite for functional analysis of biological processes. Data-independent acquisition (DIA) is a bottom-up mass spectrometry approach that provides complete information on precursor and fragment ions. However, owing to the convoluted structure of DIA data sets, confident, systematic identification and quantification of peptidoforms has remained challenging. Here, we present inference of peptidoforms (IPF), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by the DIA method SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. IPF reduced false site-localization by more than sevenfold compared with previous approaches, while recovering 85.4% of the true signals. Using IPF, we quantified peptidoforms in DIA data acquired from >200 samples of blood plasma of a human twin cohort and assessed the contribution of heritable, environmental and longitudinal effects on their PTMs.

中文翻译：

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通过SWATH-MS推断和量化大样本人群中的肽型

样品组中蛋白质翻译后修饰（PTM）的一致检测和定量是生物学过程功能分析的先决条件。数据独立采集（DIA）是一种自下而上的质谱方法，可提供有关前体和碎片离子的完整信息。但是，由于DIA数据集的结构复杂，对肽形的自信，系统的鉴定和定量仍然具有挑战性。在这里，我们介绍肽形（IPF）的推论，这是一种全自动算法，它使用光谱库来查询，验证和量化DIA数据集中的肽形。该方法是基于通过DIA方法SWATH-MS获得的数据开发的，并使用合成的磷酸肽参考数据集和富含磷酸肽的样品进行了基准测试。与以前的方法相比，IPF将错误的站点本地化减少了七倍以上，同时恢复了85.4％的真实信号。使用IPF，我们对DIA数据中的肽型进行了定量分析，该数据是从人类双生子队列的200多个血浆样本中获得的，并评估了遗传，环境和纵向效应对其PTM的贡献。