Practical 4'-phosphopantetheine active site discovery from proteomic samples.

Practical 4'-phosphopantetheine active site discovery from proteomic samples.
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DOI:
10.1021/pr100953b
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发表时间:
2011-01-07
影响因子:
4.4
通讯作者:
Bafna, Vineet
Bafna, Vineet
中科院分区:
生物学2区
文献类型:
--
作者:
Meier, Jordan L.;Patel, Anand D.;Niessen, Sherry;Meehan, Michael;Kersten, Roland;Yang, Jane Y.;Rothmann, Michael;Cravatt, Benjamin F.;Dorrestein, Pieter C.;Burkart, Michael D.;Bafna, Vineet

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聚酮化合物和非核糖体肽是一类重要的小分子天然产物。由于这些化合物已被证实的生物活性,用于发现和研究负责其产生的聚酮合酶(PKS)和非核糖体肽合成酶(NRPS)的新方法仍然是一个非常感兴趣的领域,蛋白质组学方法代表了一个相对未探索的途径。虽然这些酶可以通过使用4′-磷酸泛酰巯基乙胺(PPant)翻译后修饰与蛋白质组学环境区分开来,但PPant肽的蛋白质组学检测因其低丰度和不稳定性质而受阻,这使得它们无法使用传统的数据库搜索进行分配。在这里,我们解决关键的实验和计算的挑战,以促进实际发现这一重要的翻译后修饰在鸟枪蛋白质组学分析使用低分辨率离子阱质谱仪。基于活性的富集使PKS/NRPS肽的MS输入最大化,而靶向片段化检测推定的PPant活性位点。改进的数据分析管道允许直接从MS 2数据对这些PPant肽进行实验鉴定和验证。最后,开发了一种机器学习方法,仅从MS 2片段化数据中直接检测PPant肽。通过提供新的方法来分析一个经常隐藏的翻译后修饰,这些方法代表了在蛋白质组学环境中研究天然产物生物合成的第一步。
Polyketide and nonribosomal peptides constitute important classes of small molecule natural products. Due to the proven biological activities of these compounds, novel methods for discovery and study of the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) enzymes responsible for their production remains an area of intense interest, and proteomic approaches represent a relatively unexplored avenue. While these enzymes may be distinguished from the proteomic milieu by their use of the 4′-phosphopantetheine (PPant) posttranslational modification, proteomic detection of PPant peptides is hindered by their low abundance and labile nature which leaves them unassigned using traditional database searching. Here we address key experimental and computational challenges to facilitate practical discovery of this important posttranslational modification during shotgun proteomics analysis using low-resolution ion-trap mass spectrometers. Activity-based enrichment maximizes MS input of PKS/NRPS peptides, while targeted fragmentation detects putative PPant active sites. An improved data analysis pipeline allows experimental identification and validation of these PPant peptides directly from MS2 data. Finally, a machine learning approach is developed to directly detect PPant peptides from only MS2 fragmentation data. By providing new methods for analysis of an often cryptic posttranslational modification, these methods represent a first step towards the study of natural product biosynthesis in proteomic settings.
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