ProteinLynx Global SERVER™ (PLGS) v2.5.3 (Waters, Altrincham, UK) was used to analyze the acquired ion mobility enhanced MSE spectra for protein identification as well as for the label-free relative protein quantification. Data processing and parameter setups were carried out as followed by [44 (link)]. The sequence database of the reference strain E. faecalis V583 (NCBI Reference Sequence: NC_004668.1) was used for database search. The false positive rate (FPR) was set to 4% with a randomized database, appended to the original one. The parameters for protein identification were made in such a way that a peptide was required to have at least one fragment ion match, a protein was required to have at least three fragment ion matches, and two peptide matches for identification. The peptides with 50% or more probability to be present in the mixture and detected with a score above 20, as calculated by the software were selected for proteomic analysis [45 (link)]. Data sets were normalized using the ‘internal standard-normalization’ function of PLGS and label-free quantitative analysis was performed by comparing the normalized peak area/intensity of identified peptides between the samples.
Proteinlynx global server plgs
Manufactured by Waters Corporation
Sourced in United Kingdom, United States
The ProteinLynx Global Server (PLGS) is a software solution developed by Waters Corporation. It is designed to process and analyze data generated from mass spectrometry instruments used in proteomics research. The PLGS software provides tools for peptide and protein identification, quantification, and data management.
Automatically generated - may contain errors
Lab products found in correlation
Dynamx 3, by Waters Corporation (2 mentions)
Dynamx, by Waters Corporation (1 mentions)
Synapt g2 s mass spectrometer, by Waters Corporation (1 mentions)
Aquity uplc beh c18 column, by Waters Corporation (1 mentions)
Enzymate beh pepsin column, by Waters Corporation (1 mentions)
Acquity uplc m class, by Waters Corporation (1 mentions)
Hdx manager, by Waters Corporation (1 mentions)
Nanoacquity uplc xevo qtof ms system, by Waters Corporation (1 mentions)
Markerview software, by AB Sciex (1 mentions)
15 protocols using proteinlynx global server plgs
1
Quantitative Proteomics of Enterococcus faecalis
2
Quantitative Proteomics of E. coli K12
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Raw data was searched against a Uniprot database for E. coli K12 (2022_01 release) with inclusion of RNT1_ASPOR (Guanyl-specific ribonuclease T1) and RNAS1_BOVIN (Ribonuclease pancreatic) using ProteinLynx Global Server (PLGS) (version 3.0.3, Waters Corporation, Milford, MA, USA). The accepted false discovery rate was set to 0.01 obtained with parallel searching in a randomized database of the protein entries above. Minimum peptide matches per protein were 2 and minimum fragment ion matches per peptide and proteins 1 and 3, respectively. One missed cleavage per peptide was allowed. Trypsin was set as digest reagent, carbamidomethyl cysteine as fixed modification and methionine oxidation as variable modification.
Label-free quantification was done with TOP3 quantification using ISOQuant 1.8 including nonlinear retention time alignment, signal clustering based on accurate mass, retention and drift time, annotation of signal clusters using PLGS identifications, intensity normalization and protein isoform and homology filtering (37 (link),38 (link)). The software settings are described inSupplementary Table S5 . The resulting relative protein abundances were log2-transformed and further processed in R with the package limma (39 (link)) to estimate significant fold-changes in protein abundance.
Label-free quantification was done with TOP3 quantification using ISOQuant 1.8 including nonlinear retention time alignment, signal clustering based on accurate mass, retention and drift time, annotation of signal clusters using PLGS identifications, intensity normalization and protein isoform and homology filtering (37 (link),38 (link)). The software settings are described in
3
HDX-MS Analysis of Fts Proteins
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Peptide peak assignment was performed by the ProteinLynx Global Server (PLGS, version 3.0.2, Waters) with the primary sequence of the target proteins. Only the peptide that matched the following criteria were processed: PLGS score >6, precursor ion (MH+) mass error <10 ppm, and the number of product ions per amino acid >0.33. The HDX-MS data of the free and complexed Fts-proteins were processed by the DynamX (Version 3.0.0, Waters) software, and the results were manually inspected. An E coli proteome (GCF_012978145.1) database was used to validate the peptide assignment. The reference mass of each peptide was generated by the same HDX-MS procedure while replacing all the D2O with deionized water. There is no bimodal distribution in the analyzed peptides. The average deviation of the deuterium uptake was below 0.1 Da. Back exchange level was estimated to be an average rate of 50% by analyzing fully deuterated peptides and was not corrected when carrying out comparisons of deuterium uptake.
4
Proteome Profiling of Sweet Orange
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Spectral processing and database searching were performed using the ProteinLynx Global Server (PLGS; version 3.0.2; Waters) as described in Oliveira et al. (2022 ). For protein identifications, the sweet orange (C. sinensis) protein sequence database from National Center for Biotechnology Information Search database was used (NCBI; https://www.ncbi.nlm.nih.gov ). The mass spectrometry proteomic data have been deposited with ProteomeXchange (Deutsch et al., 2020 ) consortium via the PRIDE (Perez‐Riverol et al., 2019 ) partner repository with the dataset identifier PXD034419. To ensure the quality of the results after data processing, only the proteins that were present or absent (for unique proteins) in all three runs of biological replicates were considered in the differential accumulation analysis using Student's t‐test (two‐tailed; p < 0.05). The protein sequences were submitted to a BLAST search against the NCBI nonredundant green plant protein database (taxa: 33090, Viridiplantae).
5
Automated Hydrogen-Deuterium Exchange Mass Spectrometry
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H/DX-MS experiments were performed on a fully automated system equipped with a Leap robot (HTS PAL; Leap Technologies, NC), a Waters ACQUITY M-Class UPLC, a H/DX manager (Waters Corp., Milford, MA) and a Synapt G2-S mass spectrometer (Waters Corp., Milford, MA), as described elsewhere (Zhang et al., 2014 (link)). The protein samples were diluted in a ratio of 1:20 with deuterium oxide containing PBS buffer (pH 7.4) and incubated for 0 s, 10 s, 1 min, 10 min, 30 min or 2 hr. The exchange was stopped by diluting the labeled protein 1:1 in quenching buffer (200 mM Na2HPO4 × 2 H2O, 200 mM NaH2PO4 × 2H2O, 250 mM Tris (2-carboxyethyl)phosphine, 3 M GdmCl, pH 2.2) at 1°C. Digestion was performed on-line using an immobilized Waters Enzymate BEH Pepsin Column (2.1 × 30 mm) at 20°C. Peptides were trapped and separated at 0°C on a Waters AQUITY UPLC BEH C18 column (1.7 µm, 1.0 × 100 mm) by a H2O to acetonitrile gradient with both eluents containing 0.1% formic acid (v/v). Eluting peptides were subjected to the Synapt TOF mass spectrometer by electrospray ionization. Samples were pipetted by a LEAP autosampler (HTS PAL; Leap Technologies, NC). Data analysis was conducted with the Waters Protein Lynx Global Server PLGs (version 3.0.3) and DynamX (Version 3.0) software package.
6
Peptide and Protein Identification Using PLGS
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For peptide and protein identification raw data files were processed using ProteinLynx Global SERVER (PLGS) version 3.0.3 (Waters Corporation, Milford, MA, USA). The following parameters were used to generate peak lists: low and elevated energy thresholds, 135 and 20 counts, respectively; reference mass correction window, 0.25 Da at 556.2771 Da/e. Processed data was analyzed using the following parameters: trypsin or chymotrypsin was selected as a primary digest reagent, one missed cleavage was permitted, carbamidomethylation of cysteines was set as a fixed modification, deamidation of asparagine and glutamine, oxidation of methionine were set as variable modifications. Minimal identification criteria included 2 fragment ions per peptide, 5 fragment ions per protein and a minimum of 1 peptide per protein. The false discovery rate (FDR) was set to 2%.
7
Identification of Pathogenic Naegleria Proteins
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Immunogenic proteins bands recognized by the antibodies were matched with the corresponding protein bands on images of stained Coomassie Blue gel to excise the protein bands of interest. After comparison with immunoblotting results, six polypeptide bands were manually excised from gels and then sent for protein identification analysis by nanoLC–ESI-MSMS (USAI-Facultad de Química-UNAM).
The MSMS peptide mass data were automatically searched against the UniProt protein database (https://www.uniprot.org/blast/ ) using the global ProteinLynx version 2.4 server and software (Waters Corporation), with a Protein Lynx Global Server (PLGS) (Waters Corporation). A PLGS score of >95% confidence was accepted as correct. The peptides were matched with the theoretical peptides of reported proteins from N. fowleri and N. gruberi.
The MSMS peptide mass data were automatically searched against the UniProt protein database (
8
Proteomic analysis of Schizophyllum commune
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Data analysis was performed using ProteinLynx Global Server (PLGS) version 2.5.2 (Waters Corporation). The thresholds for low/high energy scan ions and peptide intensity were set at 300, 30 and 750 counts, respectively. The processed data were searched against the S. commune protein database obtained from the Joint Genome Institute, and combined with a subdatabase containing common contaminants (human keratins and trypsin). The database searching was performed at a False Discovery Rate (FDR) of 2%, following searching parameters were applied for the minimum numbers of: fragments per peptide (1), peptides per protein (1), fragments per protein (3), and maximum number of missed tryptic cleavage sites (1). Searches were restricted to tryptic peptides with a fixed carbamidomethyl modification for Cys residues. For functional description of proteins, the Functional Catalog (FunCat) was used as classification system (Ruepp et al., 2004 (link)).
9
Quantitative Proteomic Analysis of Fluoride Exposure
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The peptide identification was performed on a nanoAcquity UPLC-Xevo QTof MS system (Waters Corporation, Manchester, UK), as previously described32 (link). Differences in expression among groups were obtained using the ProteinLynx Global Server (PLGS) software provided by Waters Corporation and are expressed as p < 0.05 for downregulated proteins and 1 − p > 0.95 for upregulated proteins. Bioinformatics analysis was performed for comparison of the groups exposed to F relative to the control group (Tables S1 –S6 ), as reported earlier32 (link)–35 (link).
10
Peptide Identification from Maize Proteome
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Raw DDA data were processed and searched against a subdatabase containing common contaminants (human keratins and trypsin) using the ProteinLynx Global Server (PLGS), version 2.5.2 (Waters). The following search parameters were applied: fixed precursor ion mass tolerance of 10 ppm for the survey peptide, fragment ion mass tolerance of 0.02 Da, an estimated calibration error of 0.002 Da, one missed cleavage, fixed carbamidomethylation of cysteines and possible oxidation of methionine.
Spectra that remained unmatched in database searches were interpreted de novo to yield peptide sequences, and were subjected to homology-based searches using the MS BLAST program (Shevchenko et al., 2001 (link)) on a local server. MS BLAST searches were performed against a subdatabase containing plant proteins (downloaded on February 2, 2019). Concomitantly, pkl files of MS/MS spectra were generated and searched against plant protein subdatabases from NCBInr (downloaded on January 15, 2019, containing 7,456,519 sequences) using MASCOT software, version 2.6.2. The most representative proteins of Z. mays from each group with at least two peptide hits and supported by both identification strategies were chosen as candidates.
Spectra that remained unmatched in database searches were interpreted de novo to yield peptide sequences, and were subjected to homology-based searches using the MS BLAST program (Shevchenko et al., 2001 (link)) on a local server. MS BLAST searches were performed against a subdatabase containing plant proteins (downloaded on February 2, 2019). Concomitantly, pkl files of MS/MS spectra were generated and searched against plant protein subdatabases from NCBInr (downloaded on January 15, 2019, containing 7,456,519 sequences) using MASCOT software, version 2.6.2. The most representative proteins of Z. mays from each group with at least two peptide hits and supported by both identification strategies were chosen as candidates.
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