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SEPepQuant 增强了 shotgun 蛋白质组学中可能的同工型调控检测。

SEPepQuant enhances the detection of possible isoform regulations in shotgun proteomics.

机构信息

Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.

出版信息

Nat Commun. 2023 Sep 19;14(1):5809. doi: 10.1038/s41467-023-41558-2.

Abstract

Shotgun proteomics is essential for protein identification and quantification in biomedical research, but protein isoform characterization is challenging due to the extensive number of peptides shared across proteins, hindering our understanding of protein isoform regulation and their roles in normal and disease biology. We systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico and experimental data, and then present SEPepQuant, a graph theory-based approach to maximize isoform characterization. Using published data from one induced pluripotent stem cell study and two human hepatocellular carcinoma studies, we demonstrate the ability of SEPepQuant in addressing the key limitations of existing methods, providing more comprehensive isoform-level characterization, identifying hundreds of isoform-level regulation events, and facilitating streamlined cross-study comparisons. Our analysis provides solid evidence to support a widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant has broad applications to biological and translational research.

摘要

shotgun 蛋白质组学在生物医学研究中对于蛋白质的鉴定和定量至关重要,但由于蛋白质之间共享大量肽段,蛋白质同工型的特征分析具有挑战性,这阻碍了我们对蛋白质同工型调控及其在正常和疾病生物学中作用的理解。我们使用计算机模拟和实验数据系统地评估了基于 shotgun 蛋白质组学的蛋白质同工型特征分析的挑战和机遇,然后提出了 SEPepQuant,这是一种基于图论的方法,可以最大限度地进行同工型特征分析。我们使用一个诱导多能干细胞研究和两个人类肝癌研究的已发表数据,证明了 SEPepQuant 能够解决现有方法的关键限制,提供更全面的同工型水平特征分析,鉴定数百个同工型水平的调控事件,并促进简化的跨研究比较。我们的分析为蛋白质同工型调控在正常和疾病过程中的广泛作用提供了确凿的证据,SEPepQuant 在生物学和转化研究中有广泛的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da55/10509223/bf252159a4cd/41467_2023_41558_Fig1_HTML.jpg

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