Computational protocol: Comparative Proteomic Analysis of Proteins Involved in the Tumorigenic Process of Seminal Vesicle Carcinoma in Transgenic Mice

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Protocol publication

[…] The raw files of spectra were converted to mgf files with Mascot Daemon (data import filter: mass range, 600–5400; grouping tolerance, 1.4) and merged into a single file for searching by the MASCOT (version 2.1, Matrix Science Ltd., London, UK) software platform based on the IPI mouse database (v 3.36). The following MASCOT parameter settings were used; the peptide tolerance was 15 ppm with 2+ and 3+ peptide charges and the MS/MS tolerance was 0.6 Da. Two missed cleavages by trypsin were allowed, carbamidomethyl (C) was used as a fixed modification and oxidation (M) and deamidated (NQ) were used as variable modifications. The significance threshold for the identification was set to P < .01. [...] A software program IDEAL-Q (ID-based Elution time Alignment by Linear regression Quantification) was developed in-house to analyze LC-MS/MS data for label-free quantitative analysis []. The program was used to process the LC-MS data and the search results obtained from the Mascot search engine to extract the quantification information. The whole quantitative analysis consisted of the following tasks. (I) Data preparation and construction of the protein list. The raw data files generated from the mass instrument were converted into the mzXML data format by the ReAdW program (http://tools.proteomecenter.org/wiki/index.php?title=Software:ReAdW). The data of each fractional LC-MS/MS runs coming from the same sample were merged and then searched by the Mascot search engine to establish a protein list, which contained identified proteins and their related peptide information. The mzXML files coupled with the peptide and protein identification results were input to the IDEAL-Q program. (II) Extracting quantitative information from each LC-MS run. For quantitative analysis of a peptide in an LC-MS run, we extracted the LC-MS data within the range of ±1.5 minutes of its elution time and ±3.5 Da of the precursor m/z value. The peak clusters located within the selected elution time and the precursor m/z value from the extracted data underwent a peptide validation process. For peptide validation, the following three criteria were applied: signal-to-noise ratio (S/N), charge state (CS), and isotope pattern (IP). The S/N criterion checks whether the precursor peak has a valid S/N ratio (>2). The CS criterion eliminated the peak clusters with an incorrect charge state by examining whether the distance between adjacent peaks is equal to 1/z (tolerance ±1/10 1/z). Finally, the IP criterion examined the correlation between isotopic distribution of the observed peak intensities and the theoretical isotopic distribution of the peptide. The correlation was then evaluated by a Chi-square goodness of fit test (<0.218). The purpose of peptide validation was to filter out false peptide signals; only the peptide passing the validation criteria were processed to subsequent quantification. We used the extracted ion chromatogram (XIC) to determine peptide abundance in an LC-MS run. (III) Peptide abundance and peptide ratio processing. First we determined the abundances of valid peptides in each LC-MS run. Then we calculated the peptide abundance in a fraction by averaging the peptide abundances of all repeated runs. We summed the peptide abundances in all fractions to represent the peptide quantity in the sample. Following, the peptide ratio between samples could be calculated. (IV) Protein abundance processing. We selected nondegenerate unique peptides and performed Dixon's test to eliminate outliers of peptide ratios for each protein. We then used the top three highly abundant peptides of one protein to represent the quantity of this protein by a weighted average. […]

Pipeline specifications

Software tools Mascot Server, IDEAL-Q, ReAdW
Application MS-based untargeted proteomics
Organisms Mus musculus, Homo sapiens
Diseases Carcinoma, Neoplasms