基于组合肽库的综合贝叶斯肽检测方法的评价。

IF 1.1 4区 化学 Q4 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL
European Journal of Mass Spectrometry Pub Date : 2021-12-01 Epub Date: 2022-01-06 DOI:10.1177/14690667211066725
Miroslav Hruska, Dusan Holub
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引用次数: 0

摘要

多肽的检测是自下而上的蛋白质组学分析的核心。我们研究了一种贝叶斯方法来检测肽,将基于匹配的模型(片段、保留时间、同位素分布和前体质量)和肽先验概率模型整合在一个统一的概率框架下。为了评估这些模型及其各种组合的相关性,我们对基于致癌KRAS肽的低前体质量合成肽库进行了完整和尾完整搜索。片段匹配是迄今为止信息量最大的基于匹配的模型,而保留时间匹配是唯一具有明显影响的模型——将正确检测率提高了8%左右。从参考蛋白质组建立的肽先验概率模型大大改善了统一先验的检测,基本上将从头测序转变为参考指导的搜索。在肽谱匹配之前知道正确的序列标签对肽检测只有适度的影响,除非标签很长且具有很高的确定性。与使用PeptideProphet和Percolator估计的结果相比,该方法对分析的组合肽库得出了更精确的错误率,显示了其对同源肽检测的潜在适用性。虽然该方法需要进一步的计算发展来进行常规数据分析,但它说明了肽先验概率的价值,并提出了将其纳入肽检测的贝叶斯方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library.

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact--increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

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来源期刊
CiteScore
2.40
自引率
7.70%
发文量
16
审稿时长
>12 weeks
期刊介绍: JMS - European Journal of Mass Spectrometry, is a peer-reviewed journal, devoted to the publication of innovative research in mass spectrometry. Articles in the journal come from proteomics, metabolomics, petroleomics and other areas developing under the umbrella of the “omic revolution”.
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