Dataset Information

A proteomics data analysis protocol using Open-pFind

ABSTRACT: High-throughput proteomics based on mass spectrometry (MS) analysis has permeated biomedical science and propelled numerous research projects. pFind 3 is a database search engine for high-speed and in-depth proteomics data analysis (Chi et al. 2018). pFind 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using pFind 3 in conjunction with pParse for data preprocessing and if needed, pQuant for quantitation. This streamlined pParse-pFind-pQuant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on 15N-labeling, SILAC-labeling, or TMT/iTRAQ labeling.

ORGANISM(S): Homo Sapiens Caenorhabditis Elegans

SUBMITTER: Mengqiu Dong

PROVIDER: PXD023901 | iProX | Sun Jan 31 00:00:00 GMT 2021

REPOSITORIES: iProX

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Publications

How to use open-pFind in deep proteomics data analysis?- A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data.

Shao Guangcan G Cao Yong Y Chen Zhenlin Z Liu Chao C Li Shangtong S Chi Hao H Dong Meng-Qiu MQ

Biophysics reports 20210601 3

High-throughput proteomics based on mass spectrometry (MS) analysis has permeated biomedical science and propelled numerous research projects. pFind 3 is a database search engine for high-speed and in-depth proteomics data analysis. pFind 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step inst ...[more]

PMID: 37287487

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Project description:Cerebella from wild-type and cGKI knockout mice, on 129/Sv background, were subjected to protein digestion using trypsin o/n and chemically labeled with dimethyl labeling. After labeling the samples were mixed and subjected to both strong cation exchange and phosphopeptide enrichment. The SCX fractions and the enriched phosphopeptides were then analyzed on an Orbitrap mass spectrometer. Data analysis: For each raw data file recorded by the mass spectrometer, peak lists were generated using Proteome Discoverer (version 1.3, Thermo Scientific, Bremen, Germany) using a standardized workflow. Peak lists, generated in Proteome Discoverer, were searched against a Swiss-Prot database (version 2.3.02, taxonomy Mus musculus, 32402 protein entries) supplemented with frequently observed contaminants, using Mascot (version 2.3.02 Matrix Science, London, UK). The database search was performed by using the following paramenters: a mass tolerance of 50 ppm for the precursor masses and +/-0.6 Da for CID/ETD fragment ions and +/-0.05 Da for HCD fragments. Enzyme speciﬁcity was set to Trypsin with 2 missed cleavages allowed. Carbarmidomethylation of cysteines was set as fixed modification, oxidation of methionine and dimethyl labeling (L, I, H) of lysine residues and N termini, and phosphorylation (S, T, Y) (for the phosphoproteome analysis) were used as variable modifications. Percolator was used to ﬁlter the PSMs for <1% false discovery-rate. Phosphorylation sites were localized by applying phosphoRS (pRS) (v2.0). Triplex dimethyl labeling was used as quantification method, with a mass precision for the 2 ppm for consecutive precursor mass scan. A retention time tolerance of 0.5 min was used to account effect of deuterium on retention time. To further filter for high quality data we used the following parameters: high confidence peptide spectrum matches, minimal Mascot score of 20, minimal peptide length of 6 and only unique rank 1 peptide. For the phosphopeptides analysis, the search rank 1 peptide was added to the previous filters. For the identification and quantitation of the proteins, only the unique peptides were considered.

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.

Dataset Information

A proteomics data analysis protocol using Open-pFind

Publications

How to use open-pFind in deep proteomics data analysis?- A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data.

Similar Datasets

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