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基于核糖体分析的深度蛋白质组覆盖可辅助基于质谱的蛋白质和肽发现,并提供替代翻译产物和近同源翻译起始事件的证据。

Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events.

机构信息

Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.

出版信息

Mol Cell Proteomics. 2013 Jul;12(7):1780-90. doi: 10.1074/mcp.M113.027540. Epub 2013 Feb 21.

Abstract

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing and highly sensitive mass spectrometry (MS) instrumentation. Recently, a strategy, termed ribosome profiling (or RIBO-seq), based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both Swiss-Prot- and RIBO-seq-derived translation products, applicable for MS/MS spectrum identification. To record the impact of using the constructed deep proteome database, we performed two alternative MS-based proteomic strategies as follows: (i) a regular shotgun proteomic and (ii) an N-terminal combined fractional diagonal chromatography (COFRADIC) approach. Although the former technique gives an overall assessment on the protein and peptide level, the latter technique, specifically enabling the isolation of N-terminal peptides, is very appropriate in validating the RIBO-seq-derived (alternative) translation initiation site profile. We demonstrate that this proteogenomic approach increases the overall protein identification rate 2.5% (e.g. new protein products, new protein splice variants, single nucleotide polymorphism variant proteins, and N-terminally extended forms of known proteins) as compared with only searching UniProtKB-SwissProt. Furthermore, using this custom database, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated upstream ORFs. Notably, the characterization of these new translation products revealed the use of multiple near-cognate (non-AUG) start codons. As deep sequencing techniques are becoming more standard, less expensive, and widespread, we anticipate that mRNA sequencing and especially custom-tailored RIBO-seq will become indispensable in the MS-based protein or peptide identification process. The underlying mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000124.

摘要

越来越多的研究涉及基因和蛋白质表达数据的综合分析,利用下一代转录组测序和高灵敏度质谱(MS)仪器等新技术。最近,一种基于核糖体保护的 mRNA 片段深度测序的策略,即核糖体图谱(或 RIBO-seq),间接监测蛋白质合成,已被描述。我们设计了一种蛋白质基因组学方法,构建了一个定制的蛋白质序列搜索空间,由瑞士Prot 和 RIBO-seq 衍生的翻译产物构建,适用于 MS/MS 谱鉴定。为了记录使用构建的深度蛋白质组数据库的影响,我们采用了两种替代的基于 MS 的蛋白质组学策略,如下所示:(i)常规的Shotgun 蛋白质组学和(ii)N 端联合分数对角色谱(COFRADIC)方法。虽然前者技术可以在蛋白质和肽水平上进行全面评估,但后者技术,特别是能够分离 N 端肽,非常适合验证 RIBO-seq 衍生的(替代)翻译起始位点谱。我们证明,与仅搜索 UniProtKB-SwissProt 相比,这种蛋白质基因组学方法可将整体蛋白质鉴定率提高 2.5%(例如新的蛋白质产物、新的蛋白质剪接变体、单核苷酸多态性变体蛋白和已知蛋白质的 N 端延伸形式)。此外,使用此定制数据库,对 N 端 COFRADIC 数据的鉴定导致了除鉴定四个翻译的上游 ORF 之外,还检测到 16 个导致 N 端延伸的蛋白质变体的替代起始位点。值得注意的是,这些新翻译产物的特征表明使用了多个近同(非 AUG)起始密码子。随着深度测序技术变得越来越标准、更便宜和广泛,我们预计 mRNA 测序,特别是定制的 RIBO-seq,将成为基于 MS 的蛋白质或肽鉴定过程中不可或缺的一部分。基础质谱蛋白质组学数据已被存入 ProteomeXchange 联盟,数据集标识符为 PXD000124。

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