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Proteome discover 2

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Proteome Discover 2.4 is a software solution designed for the analysis and management of proteomics data. It provides a comprehensive platform for the identification, quantification, and characterization of proteins from complex biological samples.

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40 protocols using proteome discover 2

1

Urinary Proteome Analysis by Mass Spectrometry

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A total of 44 urinary proteins resuspended in the buffer solution were digested overnight with Trypsin (Promega, United States) at 37°C temperature, acidified with TFA, redissolved with acetonitrile (ACN, Sigma, United States), eluted using two solvent buffer (A: 99.9% water and 0.1% formic acid; B: 79.9% ACN, 20% water, and 0.1% formic acid), and analyzed by using a Q Exactive HF quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) coupled to UltiMate 3,000 HPLC and UHPLC Systems (Thermo Fisher Scientific). Differential proteins were identified and quantified against the complete human proteins in the Uniprot database (2020.07.02) using Proteome Discover 2.4 software (Thermo Fisher Scientific) with SEQUEST and Mascot search engine (version 2.3.01, Matrix Science, London, United Kingdom). Then bioinformatics and statistical analysis were essentially performed as described previously: (1) differentially abundant proteins from discovery proteomics were selected using t-test after log2 transformed ratio based on the following criteria: p < 0.05 and FC < 0.83 or > 1.20; (2) the demographic data were presented as mean ± SEM, and statistical analysis was performed by the two-tailed t-test and one-way ANOVA for the comparison between groups; p < 0.05 was statistically significant. All procedure has been reported in details in our prior study (Chen et al., 2021 (link)).
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2

GCF Proteomic Analysis of Periodontitis

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GCF collected from healthy individuals and periodontitis patients was pooled and subjected to proteomic analysis. A total of 25 µg protein from the pooled GCF was used for proteomic analysis, and all sample preparation procedures for protein precipitation, peptide digestion, and fractionation were analyzed with a Q Exactive HF-X hybrid quadrupole-Orbitrap mass spectrometer. Peptide mass analysis was performed according to a previous method [12 (link)]. Data analysis was performed using Proteome Discover 2.4 software (Thermo Fisher, Waltham, MA, USA) for protein identification and label-free quantification. SEQUEST-HT, part of the Proteome Discoverer 2.4 software, was used for database searching against the UniProt human database. GCF proteomic analysis was conducted at the Proteomics Core Facility at the National Cancer Center (Goyang, Republic of Korea).
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3

Vitreous Proteome Profiling Protocol

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Sample processing for proteome analysis was performed according to the previous report [24 (link)]. Briefly, vitreous samples were digested with Trypsin/Lys-C Mix (Promega, #V5073). The resulting peptides were analyzed using UltiMate 3000 RSLCnano-flow HPLC (Thermo Fisher Scientific, Waltham, MA, USA) equipped with 0.075 mm × 250 mm AURORA column (IonOpticks, Melbourne, Australia) combined with Orbitrap Fusion Lumos Mass Spectrometer (Thermo Fisher Scientific). From the datasets obtained from LC-MS/MS, the proteins were identified by searching the SwissProt Human Database using the Mascot (Matrix Science, London, UK) or Sequest HT (Thermo Fisher Scientific) search engine. Protein identification and quantification was performed using the Proteome Discover 2.4 software (Thermo Fisher Science).
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4

Proteome Analysis of Differential Proteins

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Proteome Discover 2.4 software (Thermo Fisher, Waltham, MA, USA) was used to process the raw data (the specific parameter settings are shown in Table 2), and the data were compared with the uniprot reviewed_yes+taxonomy_10090.fasta database. After the original data were retrieved from the database, trusted proteins were screened according to Score Sequest HT > 0 and unique peptide ≥ 1, with the blank values removed. The statistical significance of the data was determined using Student’s t-test. Proteins with a p-value < 0.05 and a fold change >1.2 (upregulated) or a fold change <1/1.2 (downregulated) were defined as differentially expressed. Percolator was used to determine the False Discovery Rate (FDR) of a spectrum match. To perform GO/KEGG functional enrichment analyses, we used species proteins as the background list and identified differential proteins as the candidate list. We then used a hypergeometric distribution test to determine the significance of function enrichment in the differential protein list, followed by correction using Benjamini and Hochberg’s multiple tests to obtain the FDR. The KEGG database was used for pathway analyses of the DEPs. The analyses and spectrum matching of the proteomes were carried out by OE Biotech Co. Ltd., located in Shanghai, China (methodological details can be found in Supplementary Methods S1).
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5

Glycopeptide analysis with GPQuest 2.0

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IGP data were searched with our inhouse developed glycopeptide
analysis software, GPQuest 2.03 (link),26 (link) Any
peptide-spectrum match (PSM) without glycan annotation and a glycan
intensity ratio less than 20% were filtered out with consideration of a
false discovery rate (FDR) less than 1%.
Deglycosylated peptides and global peptides were searched with
SEQUEST in Proteome Discover 2.2 (Thermo Fisher Scientific). With
consideration of static modification of carbamidomethylation (C) and
variable modifications of oxidation (O) and deamidation (N), the results
were filtered with peptide rank and peptide confidence (maximum rank, 1;
minimum confidence, high) and protein filters as minimal of 2 peptides per
protein. The FDR rate was set to 1%. MS/MS spectra were extracted with a
tolerance of 10 ppm from raw files.
The cleaved glycans were collected and desalted by NuTip Carbon
(Hypercarb) (Glygen, MD). Glycans were analyzed by a Shimadzu MALDI
(matrix-assisted laser desorption/ionization) mass spectrometer. Glycan
composition was determined by GlycoWorkbench data analysis
software27 (link) and
verified by MS/MS if necessary.
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6

Peptide Inference with MS Blender

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Peptide inference was performed with MSGF+, X!Tandem, and Comet-2013020, each run with 10ppm precursor tolerance, and allowing for fixed cysteine carbamidomethylation (+57.021464) and optional methionine oxidation (+15.9949). Peptide search results were integrated with MSBlender (Kwon et al., 2011 (link)), https://github.com/marcottelab/msblender, https://github.com/marcottelab/run_msblender). For DSSO cross-linked experiments, inter-protein cross-links were identified using the XlinkX (Klykov et al., 2018 (link)) node in ProteomeDiscover 2.2 (ThermoScientific).
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7

Quantitative Proteomic and Phosphoproteomic Analysis

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Raw data files were processed with Proteome Discover 2.2 (Thermo Scientific) using a Sequest HT search against the Swissprot human database. Results were filtered using a 1% FDR cut off at the protein and peptide level. TMT fragment ions were quantified using summed abundances with PSM filters requiring an S/N ≥ 10 and an isolation interference cut off of 35% or 50% (proteome and phosphoproteome respectively). Normalised protein and peptide abundances were extracted from PD2.2 and further scaled and analyzed using Rstudio. Data was normalised by the pooled reference, Log2 transformed and normalised by median subtraction. Phosphoproteome data was filtered to include only phosphopeptides with a class I phosphosite localization (ptmRS score >0.75). Phosphopeptides containing identical phosphorylation site localizations with different methionine oxidation sates or peptide missed cleavages were summed together to generate one quantitative value per unique phosphosite. Phosphosites quantified in peptides with different phosphorylation multiplicity states (i.e., doubly or singly phosphorylated) were not combined together and left as separate quantified values.
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8

Quantitative Proteomics of Mutant Strains

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Peptide/protein identification and quantification were performed with Proteome Discover 2.2 (PD2.2, Thermo) by searching the raw data against database consisting of 22,110 sequences obtained from the zip file named Match_result-X101SC21061420_Z01_J001_B1_43 in ProteomeXchange Consortium with the dataset identifier PXD036035. The search parameters were as follows: mass tolerance for precursor ion and product ion was 10 ppm and 0.02 Da, respectively; fixed modification with carbamidomethyl; dynamic modification with methionine oxidation; N-Terminal modification with acetylation; allowance of 2 missed cleavage; identified peptides spectrum matches with a credibility of more than 99% and at least one unique peptide; false discovery rate set as 0.01. The mutant strains with fold changes ≥1.5 or ≤ 0.67, and p value ≤0.05 (t-test) were considered as significantly differentially expressed proteins.
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9

Quantitative TMT-based Proteomic Analysis

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Mass tag (TMT)-based peptide labeling and LC-MS/MS were used (IUSM Proteomics Core Facility as described; Mosley et al., 2009 (link); Watkins et al., 2018 (link)). RAW files were analyzed using Proteome Discover 2.2 (Thermo Fisher). The FASTA database used was a mouse proteome from Uniprot. Percolator false discovery rate was set at 0.01 and 0.05 for strict and relaxed settings, respectively.
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10

Proteome Identification and Cross-linking

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The human reference proteome was downloaded from Uniprot.org (UniProt Consortium, 2019) in August 2018 (20,858 entries). This reference proteome can be found in the MASSIVE/ProteomeXchange database in entry PXD030050, available at doi:10.25345/C5R00B. Mass spectral peptide matching was performed with MSGF+, X!Tandem, and Comet-2013020, each run with 10 ppm precursor tolerance, and allowing for fixed cysteine carbamidomethylation (+57.021464) and optional methionine oxidation (+15.9949). Peptide search results were integrated with MSBlender (Kwon et al., 2011 (link)), https://github.com/marcottelab/msblender, https://github.com/marcottelab/run_msblender). For DSSO cross-linked experiments, inter-protein cross-links were identified using the XlinkX (Klykov et al., 2018 (link)) node in ProteomeDiscover 2.2 (ThermoScientific). FDR for all analyses was kept at 1%.
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