Dataset Information

Targeted LC-MS/MS analysis of oSCRIB protein in HeLa cells.

ABSTRACT: LC-MS/MS analysis of small HeLa proteins (approximately 10–15 kDa) for detecting oSCRIB protein with Parallel Reaction Monitoring (PRM) mode.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Yuhta Nomura

LAB HEAD: Naoshi Dohmae

PROVIDER: PXD027841 | Pride | 2022-02-17

REPOSITORIES: Pride

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Publications

Discovery of a small protein-encoding cis-regulatory overlapping gene of the tumor suppressor gene Scribble in humans.

Nomura Yuhta Y Dohmae Naoshi N

Communications biology 20210917 1

Intensive gene annotation has revealed many functional and regulatory elements in the human genome. Although eukaryotic protein-coding genes are generally transcribed into monocistronic mRNAs, recent studies have discovered additional short open reading frames (sORFs) in mRNAs. Here, we performed proteogenomic data mining for hidden proteins categorized into sORF-encoded polypeptides (SEPs) in human cancers. We identified a new SEP-encoding overlapping sORF (oORF) on the cell polarity determinan ...[more]

PMID: 34535749

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Project description:The standard platform for proteomics experiments today is mass spectrometry, particularly for samples derived from complex matrices. Recent increases in mass spectrometry sequencing speed, sensitivity and resolution now permit comprehensive coverage of even the most precious and limited samples, particularly when coupled with improvements in protein extraction techniques and chromatographic separation. However, the results obtained from laborious sample extraction and expensive instrumentation are often hindered by a sub optimal data processing pipelines. One critical data processing piece is peptide sequencing which is most commonly done through database search engines. In almost all MS/MS search engines users must limit their search space due to time constraints and q-value considerations. In nearly all experiments, the search is limited to a canonical database that typically does not reflect the individual genetic variations of the organism being studied. Searching for posttranslational modifications can exponentially increase the search space thus careful consideration must be used during the selection process. In addition, engines will nearly always assume the presence of only fully tryptic peptides. Despite these stringent parameters, proteomic data searches may take hours or even days to complete and opening even one of these criteria to more realistic biological settings will lead to detrimental increases in search time on expensive and custom data processing towers. Even on high performance servers, these search engines are computationally expensive, and most users decide to dial back their search parameters. We present Bolt, a new search engine that can search more than nine hundred thousand protein sequences (canonical, isoform, mutations, and contaminants) with 31 post translation modifications and N-terminal and C-terminal partial tryptic search in a matter of minutes on a standard configuration laptop. Along with increases in speed, Bolt provides an additional benefit of improvement in high confidence identifications, as demonstrated by manual validation of unique peptides identified by Bolt that were missed with parallel searching using standard engines. When in disagreement, 67% of peptides identified by Bolt may be manually validated by strong fragmentation patterns, compared to 14% of peptides uniquely identified by SEQUEST. Bolt represents, to the best of our knowledge, the first fully scalable, cloud based quantitative proteomic solution that can be operated within a user-friendly GUI interface.

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