Tandem mass spectra were searched against the UniProt database of Candida albicans (https://www.uniprot.org/proteomes/UP000000559 ; 2019/06/16) using Proteome Discoverer (PD) 2.2 (Thermo) and the algorithms of Mascot 2.4.1 (Matrix Science, UK), Sequest HT (version of PD2.2) and MS Amanda 2.0. Two missed cleavages were allowed for the tryptic digestion. The precursor mass tolerance was set to 10 ppm and the fragment mass tolerance was set to 0.02 Da. Modifications were defined as dynamic Met oxidation, and protein N-term acetylation as well as static Cys carbamidomethylation. A strict false discovery rate (FDR) < 1% (peptide and protein level) was required for positive protein hits. If only 1 peptide per protein has been identified the hit was accepted if the Mascot score was >30 or the MS Amanda score was >300 or the Sequest score was >4. The Percolator node of PD2.2 and a reverse decoy database were used for q-value validation of spectral matches. Only rank 1 proteins and peptides of the top-scored proteins were counted. Label-free protein quantification was based on the Minora algorithm of PD2.2 using a signal-to-noise ratio >5. Imputation of missing quan values was applied by setting the abundance to 75% of the lowest abundance identified for each sample.
Sequestht
Manufactured by Matrix Science
Sourced in United Kingdom
SequestHT is a laboratory instrument designed for high-throughput sample processing. It facilitates efficient extraction, purification, and concentration of target analytes from complex matrices. The core function of SequestHT is to automate and streamline sample preparation workflows to support various analytical applications.
Automatically generated - may contain errors
Lab products found in correlation
Mascot, by Matrix Science (6 mentions)
Proteome discoverer 2, by Thermo Fisher Scientific (3 mentions)
Mascot 2, by Matrix Science (1 mentions)
Proteome discoverer, by Thermo Fisher Scientific (1 mentions)
Proteome discoverer software, by Thermo Fisher Scientific (1 mentions)
Proteome discoverer platform, by Thermo Fisher Scientific (1 mentions)
Xcalibur, by Thermo Fisher Scientific (1 mentions)
Proteome discoverer software version 2, by Thermo Fisher Scientific (1 mentions)
7 protocols using sequestht
1
Label-Free Quantitation of Candida albicans Proteome
2
Quantitative Proteomics of Mouse Samples
3
Proteomic Identification of CENP-A
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Protein identification was performed against the UniProt database entry for CENP-A using Proteome Discoverer 2.1 (Thermo Fisher Scientific) equipped with SEQUEST HT and Mascot (Matrix Science, Boston, MA). Search settings included tryptic or chymotryptic digest with up to two missed cleavages or nonspecific cleavage. Carbamidomethylation of cysteine was set as a static modification, while dynamic modifications included Met oxidation, Asp, Glu deamidation, Ser, Thr, Tyr phosphorylation, Lys acetylation, Lys ubiquitination, Arg and Lys methylation. Only matches with XCorrs greater than 2.0 or ion scores greater than 20 were considered. All the spectra matches were manually validated.
4
Quantitative Proteome Analysis Using TMT-10 Plex
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Proteome Discoverer™ software (version 2.1, Thermo Fisher Scientific™) was used to search the raw data files for each sample set for data analysis of Orbitrap Fusion data. The data were compared to all Homo sapiens sequences retrieved from SwissProt database using the search engines SequestHT (version 2018) and Mascot (version 2.4, Matrix Science, London, UK) (version 2018). The names of proteins were determined, and the gene symbol was obtained from the Uniprot database (http://www.uniprot.org ), which contains 95,106 human proteins, including isoforms and unreviewed (Homo sapiens). Table 2 reports the parameters for data processing. Using the abundances of the sample controls as the denominators, TMT-10 plex kit calculated the quantitative ratios in two sets.
Database retrieval parameter
| Database | SwissProt database (version 2018) |
| Enzyme name | Trypsin |
| Maximum missed cleavage | 2 |
| Static modification of cysteine | Carbamidomethylation (C) |
| Dynamic modifications | TMT 10-plex (N-term, K), Oxidation (M), Deamidated (N, Q), Glu- > pyro-Glu (N-term E), Gln- > pyro-Glu (N-term Q) and Acetyl (Protein N-Terminus) |
| Precursor mass tolerance | 10 ppm |
| Fragmentation mass tolerance | 0.02 Da |
| FDR and result display filtered | Protein, Peptide and PSM FDR < 1%, Master Proteins |
5
Quantitative Proteomic Workflow Analysis
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The
raw files acquired by the MS system were processed using the Proteome
Discoverer platform (version 1.4, Thermo Scientific). An integrated
workflow using the algorithms Sequest HT and Mascot (version 2.4,
Matrix Science) was employed. Either a human UniProtKB database (Release
2013_6; 88 295 human sequences) or a database consisting of
the aforementioned human proteins and all protein sequences derived
from 21 microbial genomes (Supporting Information Table S-1) were used. The latter database was used to identify human
and microbial proteins present in UP samples. MS search parameters
similar to published previously27 are described
in detail inSupporting Information . For
protein quantification of the data sets, the MaxQuant software suite
(version 1.4.2) was used.32 (link) Most of the
default settings provided in this software suite were accepted, and
data were processed using both the label-free quantitation (LFQ) and
the intensity-based absolute quantitation (iBAQ) tools. The LFQ algorithms
provide relative quantification of the integrated MS1 peak
areas from the high resolution MS data. The iBAQ algorithms sum the
integrated peak intensities of the peptide ions for a given protein
divided by the number of theoretically observable peptides, which
are calculated by in silico digestion of protein sequences including
all fully tryptic peptides with a length of 6–30 amino acids.33 (link)
raw files acquired by the MS system were processed using the Proteome
Discoverer platform (version 1.4, Thermo Scientific). An integrated
workflow using the algorithms Sequest HT and Mascot (version 2.4,
Matrix Science) was employed. Either a human UniProtKB database (Release
2013_6; 88 295 human sequences) or a database consisting of
the aforementioned human proteins and all protein sequences derived
from 21 microbial genomes (
and microbial proteins present in UP samples. MS search parameters
similar to published previously27 are described
in detail in
protein quantification of the data sets, the MaxQuant software suite
(version 1.4.2) was used.32 (link) Most of the
default settings provided in this software suite were accepted, and
data were processed using both the label-free quantitation (LFQ) and
the intensity-based absolute quantitation (iBAQ) tools. The LFQ algorithms
provide relative quantification of the integrated MS1 peak
areas from the high resolution MS data. The iBAQ algorithms sum the
integrated peak intensities of the peptide ions for a given protein
divided by the number of theoretically observable peptides, which
are calculated by in silico digestion of protein sequences including
all fully tryptic peptides with a length of 6–30 amino acids.33 (link)
6
Proteomic Analysis of Cajanus Legumes
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Proteome Discoverer software version 2.1 (Thermo Fisher Scientific, Waltham, MA, USA) was used to search mass spectrometric data files generated from Xcalibur™ software (Thermo Fisher Scientific, Waltham, MA, USA). SequestHT and Mascot (Matrix Science, London, UK) search engines were used to process the data against Cajanus cajan and Cajanus scarabaeoides sequences downloaded from the Uniprot database (47,669 sequences, accessed on 19 July 2020 [54 ]).
Trypsin enzyme with two maximum missed cleavages, with a 20 ppm precursor mass tolerance and 0.02 Da fragment mass tolerance, was used for peptide identification and quantification. The parameters used for dynamic modifications were oxidation of methionine, deamination of asparagine, glutamine and pyroglutamate, acetylation of protein N-terminus, Met-loss+Acetyl (Sequest) and TMT6plex tag on lysine residues, and the peptide N-terminus. Static modification parameter was carbamido-methylation of cysteine. False discovery rate (FDR) was set at <1%, while the display filters were Protein, Peptide, and PSM Master Proteins only.
Trypsin enzyme with two maximum missed cleavages, with a 20 ppm precursor mass tolerance and 0.02 Da fragment mass tolerance, was used for peptide identification and quantification. The parameters used for dynamic modifications were oxidation of methionine, deamination of asparagine, glutamine and pyroglutamate, acetylation of protein N-terminus, Met-loss+Acetyl (Sequest) and TMT6plex tag on lysine residues, and the peptide N-terminus. Static modification parameter was carbamido-methylation of cysteine. False discovery rate (FDR) was set at <1%, while the display filters were Protein, Peptide, and PSM Master Proteins only.
7
Proteomics Analysis of Avocado
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Raw data were processed with Proteome Discoverer 2.1 (PD, Thermo Fisher Scientific, USA). The subsequent search was carried out with SEQUEST HT, MASCOT (version 2.4.1, Matrix Science), and AMANDA against the avocado proteins databases. Parameters in the search included full-tryptic protease specificity, two missed cleavage allowed. Static modifications covered carbamidomethylation of cysteine (+57.021 Da) and TMT 6-plex N-terminal/lysine residues (+229.163 Da). Dynamic modifications comprised methionine oxidation (+15.995 Da) and deamidation in asparagine/glutamine (+0.984 Da). We used for the TMT6-plex quantification method ± 10 ppm mass tolerance, highest confidence centroid, and a precursor co-isolation filter of 45%. Protein identification was carried out with tolerances of ±10 ppm and ±0.6 Da. Peptide hits were filtered for a maximum of 1% FDR using the Percolator algorithm. Functional annotation of proteins was carried out by Blast2Go software (https://www.blast2go.com/ ), and gene ontology (GO) enrichment was carried out by David bioinformatic source (https://david.ncifcrf.gov/ ) using Arabidopsis homologs. We used the REVIGO web server (http://revigo.irb.hr/ ) for GO clustering and visual representation of biological processes (Data S2 ).
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