Proteinpilot software 4

Gel bands were reduced with 10 mM DTT at 56 °C for 30 min followed by alkylation with 50 mM iodoacetamide at room temperature for 30 min. The samples were digested with trypsin at 37 °C for 16 h, and the generated tryptic peptides were sequentially extracted from the gels with 5% formic acid, 5% formic acid/50% acetonitrile, and 5% formic acid/95% acetonitrile. The extracted solutions were concentrated by a centrifugal evaporator CVE-3100 (EYELA) and then analyzed by LC–MS on a maXis II quadrupole time-of-flight mass spectrometer (Bruker Daltonics) coupled to a Shimadzu Prominence UFLC-XR system (Shimadzu) with chromatographic separation using an Ascentis Express Peptide ES-C18 column (2.7 μm particle size, L × I.D. 150 mm × 2.1 mm; Supelco) (75 (link)). The mass spectrometry (MS) scan and MS/MS acquisition were performed over the m/z ranges of 50 to 2500 with a frequency of 5 Hz. The acquired MS/MS spectra were searched against the UniProtKB/Swiss-Prot database (release 2018_05) for Mus musculus species using ProteinPilot software 4.5 (AB Sciex), as described previously (24 ).

IBM SPSS Statistics 20.0 (IBM) was used to analyse the data. One-way ANOVA, followed by post-hoc Tukey honest signicant difference (HSD) test was used to determine the difference in morphology and virulence factor production of P. aeruginosa strains. The P-value or significance was set to <0.05.
Raw MS/MS data were analysed using ProteinPilot Software 4.5 (AB SCIEX). Proteins were identified using the Swiss-Prot/UniProt protein database. For protein identification, a threshold >0.05 (CI, 10 %) was applied with ProtScore at 2.0 and false discovery rate (FDR) at 1 %. DEPs were considered to be differentially expressed if their iTRAQ ratios were >1.5 (upregulation) or <0.667 (downregulation) in ciprofloxacin-exposed strains compared with the initial P. aeruginosa ATCC 9027.
A Venn diagram was constructed to analyse the common DEPs among exposed strains. Gene Ontology (GO) (PANTHER; Version 11.0, Protein Analysis Through Evolutionary Relationships; http://pantherdb.org) was used to evaluate the biological significance of the DEPs. Information on protein–protein interactions (PPIs) of the studied proteins was retrieved using the Search Tool for Retrieval of Interacting Genes/Proteins (STRING; http://string-db.org/).

Protein identification and quantification were performed with ProteinPilot™ Software 4.5 (AB SCIEX, USA) against the Cucumis melo.fasta (http://www.ncbi.nlm.nih.gov/protein/) using the Paragon algorithm. The utilized search parameters used were as follows:(1) Fixed modifications: Carbamidomethyl (C); (2) Variable modifications: Oxidation (M),Acetyl (Protein N-term); (3) Digestion: Trypsin; (4) Instrument: Triple TOF5600. For iTRAQ quantification, the peptide for quantification was automatically selected by the Pro Group algorithm to calculate the reporter peak area, error factor (EF) and the p value. The peptides and corresponding relative abundances were obtained in ProteinPilot using a confidence cutoff of >1.0 (>90%) and >1.3 (>95%) or the experiments of the green and ripe stages, respectively. Only the proteins identified with at least 2 different peptides and p < 0.05, and quantified with a ratio of >1.5 and p < 0.05, were considered to be differentially expressed proteins (FDR < 1%). The final fold change was calculated as the average value obtained from two replicates.

All assays were carried out in triplicate, and the experimental results were expressed as means ± standard deviations. Statistical analysis was performed by SPSS 16 (SPSS Inc., Chicago, IL, USA). Data were analyzed using the least significant difference (LSD) method by analysis of variance, and the value differences were considered to be significant when p < 0.05. The spectrum data from iTRAQ results were submitted for protein identification, and a database search was carried out using ProteinPilot Software 4.5 (AB SCIEX, Seattle, WA, USA) to perform database searches. The database used was the SwissProt_2013_09 (total sequence 540958). The search parameters used were as follows: Cysteine alkylation with MMTS; Trypsin Digestion; Triple TOF 5600; ID focus with Biological modifications; Search effort with thorough ID. A decoy database search strategy was used to determine the false discovery rate (FDR) for protein identification. The criteria for protein identification was set to FDR < 0.1%.

To identify the proteins, the MS/MS spectra were processed by ProteinPilot™ Software 4.5 (AB Sciex). Following criteria were upended for protein identification and quantification: MS tolerance value of 0.05 Dalton (standard deviation 0.0025 Dalton), MS/MS tolerance value of 0.10 Dalton (standard deviation 0.004 Dalton), two unique peptides identified in at least two of the three technical replicates, cysteine carbamidomethylation as fixed modification, and methionine oxidation as variable modification. The acquired MS/MS spectra were automatically searched against a total of 75,015 wheat protein sequences (Wheat_2015.4.17_UN.fasta) available in the UniProt reference database (http://www.uniprot.org/proteomes/UP000019116). Protein quantification was based on the abundances of reporter tags, which reflect the relative ratio of the peptide in the samples that were combined. In addition, the following criteria were set forth to identify differentially expressed proteins (DEPs): (i) all identified proteins must exhibit ≥95% confidence on the protein confidence threshold cutoff of 1.3 (unused), and (ii) fold-change of >2 or <0.5 were set as cutoff value to call a change in protein abundance significant.

The raw data were analyzed using LC MS/MS iTRAQ technology by ProteinPilot™ Software 4.5 (AB Sciex) [15 (link)–17 (link)]. Protein identification utilized the human SwissProt_2014_08. fasta sequence database. A standard parameter set was used for the search, which included Cys alkylation by methylmethanethiosulfonate (MMTS), biological modifications ID focus, trypsin digestion, Homo sapiens, search effort, and thorough ID. More than two unique peptides were required for protein identification. A threshold of confidence above 95% and a local FDR of less than 1% were used for both protein identification and quantitative analysis [23 (link)]. P-values < 0.05 were required for relative quantification.

Protein identification and relative iTRAQ quantification were performed with ProteinPilot™ Software 4.5 (AB SCIEX) using the Paragon™ algorithm for the peptide identification which was further processed by Pro Group™ algorithm, where isoform-specific quantification was adopted to trace the differences between expressions of various isoforms. The Pro Group™ Algorithm calculates protein ratios using only ratios from the spectra that are distinct to each protein or protein form and thus eliminates any masking of changes in expression due to peptides that are shared among proteins.
User defined search parameters were as follows: (i) Sample Type: iTRAQ™ 8-plex (Peptide Labelled); (ii) Cysteine Alkylation: MMTS; (iii) Digestion: Trypsin; (iv) Instrument: TripleTOF 5600; (v) Special Factors: None; (vi) Species: Homo sapiens; (vii) ID Focus: Biological modifications; (viii) Database: Oct2012_uniprot_sprot. fasta (40468 proteins searched); (ix) Search Effort: Thorough; (x) FDR Analysis: Yes; (xi) User Modified Parameter Files: No. For iTRAQ™ quantitation, the resulting dataset was auto bias-corrected to account for variations due to unequal sample loading. A reverse database search strategy was adopted to estimate the false discovery rate (FDR) for protein identification. Global FDR = 1% was chosen as the cut-off threshold to generate the list of identified proteins.