The raw data were analyzed using Proteome Discoverer 1.4 (Thermo Fisher Scientific). The software was connected to a Mascot Search Engine server version 2.2.4 (Matrix Science, London, UK) and to a Sequest Search Engine version 28.0 (Thermo Fisher Scientific). The confidence value for each peptide was calculated based on the agreement between the experimental and theoretical fragmentation patterns. Each protein was assigned a confidence score (0% to 100%) based on the confidence scores of its constituent peptides based on unique spectral patterns. Proteins with confidence scores of greater than 90% and with at least 1 peptide of 95% identification confidence were used for further quality control and differential expression analyses. Each protein also received quantitative scores for each of the eight-iTRAQ tags to calculate the relative expression levels. In this experiment, the relative expression levels of proteins in different samples were calculated using a normal sample as the reference sample.
Mascot search engine server
Manufactured by Matrix Science
Sourced in United Kingdom
The Mascot Search Engine server is a high-performance computing platform designed for mass spectrometry data analysis. It provides a centralized solution for processing and interpreting proteomic data from various analytical instruments. The server is built to handle large-scale computational tasks associated with protein identification and characterization.
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Lab products found in correlation
5 protocols using mascot search engine server
1
Quantitative Proteome Analysis using iTRAQ
2
Top-Down Analysis of ApoA1 Identification
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To establish the ApoA1 full-length identity in HCs, precursor ions for masses similar to full-length ApoA1 were submitted for a second analysis using a tandem mass spectrometry (MS/MS) acquisition method. MS/MS spectra were submitted to Bruker Daltonics BioTools 3.2 SR4 software (Bruker, Bellirica, MA) for protein identification using the Mascot Search Engine server, version 2.2.4 (Matrix Science, London, UK). Spectra were searched against human databases (Uniprot) with the following parameters: top-down analysis, no enzymatic digestion, and peptide and fragment tolerance set to 15 ppm and 0.05 Da, respectively.
3
Protein Identification and Quantification
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For the identification of proteins in SDS-PAGE gels the bands were excised from G-250 stained gels, washed, dried, reduced, alkylated, and trypsin digested. Peptides were extracted with three steps of 50% acetonitrile/0.5% trifluoroacetic acid, and desalted with a Zip-Tip C18. Samples were analyzed by LC-MSMS using a Q Exactive™ nano HPLC-ESI-Orbitrap mass spectrometer (Thermo Fisher Scientific). Protein identification was performed with Proteome Discoverer 1.4 (Thermo Fisher Scientific) connected to a Mascot search engine server (Matrix Science, London, UK). Only proteins identified with at least two unique peptides with 0.99 confidence (FDR = 0.01) were considered.
For large-scale protein analysis by shotgun proteomics, lyophilized AWF aliquots were resuspended in 8 M urea; aliquots containing 20 µg of protein were reduced, alkylated, trypsin digested, and desalted. LC-MSMS analysis was performed as described previously. A label-free-quantification method was performed using Proteome Discoverer 1.4. Four biological replicas for each leaf stage were analyzed. Identified proteins were submitted to a Student's t-test (P ≤ 0.05) with Perseus 1.6.1.3 (Max Planck Institute of Biochemistry). Only proteins exhibiting at least a ±100% fold change in their amount were considered.
For large-scale protein analysis by shotgun proteomics, lyophilized AWF aliquots were resuspended in 8 M urea; aliquots containing 20 µg of protein were reduced, alkylated, trypsin digested, and desalted. LC-MSMS analysis was performed as described previously. A label-free-quantification method was performed using Proteome Discoverer 1.4. Four biological replicas for each leaf stage were analyzed. Identified proteins were submitted to a Student's t-test (P ≤ 0.05) with Perseus 1.6.1.3 (Max Planck Institute of Biochemistry). Only proteins exhibiting at least a ±100% fold change in their amount were considered.
4
iTRAQ-based Quantitative Proteomics of Maize
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Peptides were labelled with iTRAQ reagents (AB Sciex, USA) according to the manufacturer’s instructions. Two replications for each condition were labelled with the iTRAQ tags (114 and 115 for N supplied, and 116 and 117 for N deprived samples, respectively). Samples were analysed separately by LC-MS/MS. Labelled peptides were vacuum concentrated, fractionated, and subjected to MS analyses.
The raw LC-MS/MS files were analysed using Proteome Discoverer 1.4 (Thermo Fisher Scientific), connected to a Mascot Search Engine server (Matrix Science, London, UK). The spectra were searched against a Zea mays L. protein database (Sun et al., 2009 (link)) (http://ppdb.tc.cornell.edu/ ). FDRs were calculated using the Proteome Discoverer. MS/MS spectra containing fewer than five peaks or with a total ion count below 50 were excluded. Only proteins that were identified in all three independent experiments were considered. The quantification was performed normalizing the results on the median value of all measured iTRAQ reporter ratios. A fold change (relative to the control) of ≥1.3 or ≤0.77 indicated an increased or decreased protein, respectively.
The raw LC-MS/MS files were analysed using Proteome Discoverer 1.4 (Thermo Fisher Scientific), connected to a Mascot Search Engine server (Matrix Science, London, UK). The spectra were searched against a Zea mays L. protein database (Sun et al., 2009 (link)) (
5
Protein Identification via Mass Spectrometry
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In order to permit peptide identification two proteomic tools, Proteome Discoverer software (version 1.4, Thermo Fisher Scientific, Waltham, MA, USA) and the Mascot Search engine server (version 2.2.4, MatrixScience, London, UK) were utilized for processing raw LC-MS/MS data files given by Xcalibur software (version 2.2 SP1, Thermo Fisher Scientific, Waltham, MA, USA). Protein identification against the SwissProt database (version 20180703; 597363 sequences; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland) was performed using the following parameters: no enzyme, precursor tolerance 10 ppm and fragment tolerance 0.6 Da. Oxidation of methionine was set as variable modification. The Percolator algorithm was used to assess the false-discovery rate and filter the results (FDR < 0.01). Proteins were grouped in protein families according to the principle of maximum parsimony.
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