Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana 61801, IL, USA.
Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana 61801, IL, USA.
Talanta. 2018 May 15;182:456-463. doi: 10.1016/j.talanta.2018.01.062. Epub 2018 Jan 31.
Characterization of endogenous neuropeptides produced from post-translational proteolytic processing of precursor proteins is a demanding task. A variety of complex prohormone processing steps generate molecular diversity from neuropeptide prohormones, making in silico neuropeptide discovery difficult. In addition, the wide range of endogenous peptide concentrations as well as significant peptide complexity further challenge the structural characterization of neuropeptides. Liquid chromatography-mass spectrometry (MS), performed in conjunction with bioinformatics, allows for high-throughput characterization of peptides. Mass analyzers and molecular dissociation techniques render specific characteristics to the acquired data and thus, influence the analysis of the MS data using bioinformatic algorithms for follow-up peptide identification. Here we evaluated the efficacy of several distinct peptidomic workflows using two mass spectrometers, the Thermo Orbitrap Fusion Tribrid and Bruker Impact HD UHR-QqTOF, for confident peptide discovery and characterization. We compared the results in several categories, including the numbers of identified peptides, full-length mature neuropeptides among all identifications, and precursor proteins mapped by the identified peptides. We also characterized the peptide false discovery rate (FDR) based on the occurrence of amidation, a known post-translational modification (PTM) that has been shown to require the presence of a C-terminal glycine. Thus, amidation events without a preceding glycine were considered false-positive amidation assignments. We compared the FDR calculated by the search engine used here to the minimum FDR estimated via false amidation assignments. The search engine severely underestimated the rate of false PTM assignments among the identified peptides, regardless of the specific MS platform used.
内源性神经肽的特征在于前体蛋白翻译后蛋白水解加工产生，这是一项艰巨的任务。各种复杂的激素原加工步骤使神经肽激素产生分子多样性，使得计算机神经肽发现变得困难。此外，内源性肽的广泛浓度范围以及显著的肽复杂性进一步挑战了神经肽的结构特征。液相色谱-质谱（MS）与生物信息学相结合，可实现肽的高通量表征。质量分析仪和分子解离技术为获得的数据赋予特定特征，从而影响使用生物信息学算法对后续肽识别的 MS 数据分析。在这里，我们使用两种质谱仪，即 Thermo Orbitrap Fusion Tribrid 和 Bruker Impact HD UHR-QqTOF，评估了几种不同的肽组学工作流程的效果，以进行有信心的肽发现和表征。我们比较了几个类别中的结果，包括鉴定的肽数量、所有鉴定中全长成熟神经肽的数量，以及鉴定的肽映射的前体蛋白。我们还根据酰胺化的发生来表征肽假发现率（FDR），酰胺化是一种已知的翻译后修饰（PTM），它需要 C 末端甘氨酸的存在。因此，没有前甘氨酸的酰胺化事件被认为是假阳性酰胺化分配。我们比较了这里使用的搜索引擎计算的 FDR 与通过虚假酰胺化分配估计的最小 FDR。无论使用何种特定的 MS 平台，搜索引擎都严重低估了鉴定肽中错误 PTM 分配的速率。
