title: A proteogenomics approach integrating proteomics and ribosome profiling increases the efficiency of protein identification and enables the discovery of alternative translation start sites.
abstract: Next-generation transcriptome sequencing is increasingly integrated with MS to enhance MS-based protein and peptide identification. Recently, a breakthrough in transcriptome analysis was achieved with the development of ribosome profiling (ribo-seq). This technology is based on the deep sequencing of ribosome-protected mRNA fragments, thereby enabling the direct observation of in vivo protein synthesis at the transcript level. In order to explore the impact of a ribo-seq-derived protein sequence search space on MS/MS spectrum identification, we performed a comprehensive proteome study on a human cancer cell line, using both shotgun and N-terminal proteomics, next to ribosome profiling, which was used to delineate (alternative) translational reading frames. By including protein-level evidence of sample-specific genetic variation and alternative translation, this strategy improved the identification score of 69 proteins and identified 22 new proteins in the shotgun experiment. Furthermore, we discovered 18 new alternative translation start sites in the N-terminal proteomics data and observed a correlation between the quantitative measures of ribo-seq and shotgun proteomics with a Pearson correlation coefficient ranging from 0.483 to 0.664. Overall, this study demonstrated the benefits of ribosome profiling for MS-based protein and peptide identification and we believe this approach could develop into a common practice for next-generation proteomics.
authors.affiliation: Laboratory of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
authors.forename: Alexander
authors.initials: A
authors.lastname: Koch
authors.forename: Daria
authors.initials: D
authors.lastname: Gawron
authors.forename: Sandra
authors.initials: S
authors.lastname: Steyaert
authors.forename: Elvis
authors.initials: E
authors.lastname: Ndah
authors.forename: Jeroen
authors.initials: J
authors.lastname: Crappé
authors.forename: Sarah
authors.initials: S
authors.lastname: De Keulenaer
authors.forename: Ellen
authors.initials: E
authors.lastname: De Meester
authors.forename: Ming
authors.initials: M
authors.lastname: Ma
authors.forename: Ben
authors.initials: B
authors.lastname: Shen
authors.forename: Kris
authors.initials: K
authors.lastname: Gevaert
authors.forename: Wim
authors.initials: W
authors.lastname: Van Criekinge
authors.forename: Petra
authors.initials: P
authors.lastname: Van Damme
authors.forename: Gerben
authors.initials: G
authors.lastname: Menschaert
mesh_terms: D019295:Computational Biology / Q000379:methods*; D045325:HCT116 Cells; D006801:Humans; D014176:Protein Biosynthesis / Q000235:genetics; D011506:Proteins / Q000235:genetics / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012270:Ribosomes / Q000378:metabolism*; D053719:Tandem Mass Spectrometry
chemical_list: D011506:Proteins
keywords: Bioinformatics; N-terminomics; Proteogenomics; Ribosome profiling; Translation initiation