The largest database of trusted experimental protocols

23 protocols using proteome discover

1

Peptide Identification and Validation for Protein Sequencing

Check if the same lab product or an alternative is used in the 5 most similar protocols
For peptide assignments of each digest, the resulting data file was searched against a database including only the protein being sequenced using Byonic version 2.16.16 (Protein Metrics). While the apomyoglobin data files were assigned with the standalone Byonic program, the adalimumab data file was searched in Proteome Discover version 2.2.0.386 (Thermo Scientific) using the Byonic search node. As standard searches in Byonic cannot contain both mixed analyzer times and mixed data sets, the Orbitrap and ion trap scans were separated into two different searches. Both searches used a 1% FDR to filter identified peptides. Detailed search parameters are presented in the Supporting Information.
Following automated assignment, a subset of peptides identified in the adalimumab data set were selected for further manual inspection. This subset was selected based on both their score output from the Byonic search as well as their notated sequence coverage within the Byonic viewer. To annotate spectra, all MS2 spectra taken under each respective peak were averaged in Qual Browser 4.0.27.10 and peaks within the averaged spectrum were then assigned manually.
+ Open protocol
+ Expand
2

Proteomic Analysis of HDL Proteins

Check if the same lab product or an alternative is used in the 5 most similar protocols
Agarose gel bands representing HDL or the band above HDL (15 lanes) were scraped from the gel plastic backing and transferred to plastic Eppendorf tubes. Samples were subjected to in-gel reduction by dithiothreitol and carbamidomethylation by iodoacetamide, followed by overnight digestion with sequencing-grade trypsin. Protein digests were filtered to separate peptides from gel fragments and desalted using ZipTip C18 columns (Millipore, Burlington, MA, USA). Samples were re-suspended in 0.1% formic acid and analyzed by nanoLC-MS/MS on an Orbitrap Elite mass spectrometer (Thermo Scientific, Waltham, MA, USA). Mass data was searched against the Swiss-Prot database using Mascot search engine and Proteome Discover software (Thermo, Waltham, MA, USA). Identifications were validated using Scaffold (Proteome Software) with both peptide and protein thresholds set to 95% confidence and a minimum of 2 peptides for protein identification.
+ Open protocol
+ Expand
3

Proteomic Analysis of Rhesus Macaque Samples

Check if the same lab product or an alternative is used in the 5 most similar protocols
For bioinformatics analysis, the LC-MS/MS data were analyzed using the Thermo Fisher Scientific Proteome Discover software suite 2.2 with the SEQUEST search engine. Data were compared with those obtained from the rhesus macaque unreviewed FASTA database downloaded from UniProt (released on July 21, 2017). Significantly changed proteins were identified using the following thresholds: downregulation, 0.67 (2−0.58) and upregulation, 1.50 (20.58). The scatter graph was drawn using the ggplot2 package from the Bioconductor R toolset. The heatmap was generated using HemI software. Gene ontology (GO) analysis was performed using PANTHER1. Pathway analysis was performed using the Wiki pathway database of the WEB-based Gene SeT AnaLysis Toolkit2 (see workflow in Figure 1A). The STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database3 was used for predicting protein networks. All STRING network analyses were performed using protein accessions as input and with the confidence level threshold set at high (0.7).
The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE (Vizcaíno et al., 2016 (link)) partner repository under the dataset identifier PXD012306.
+ Open protocol
+ Expand
4

Peptide Identification and Quantification in Maize

Check if the same lab product or an alternative is used in the 5 most similar protocols
Multiple strategies were employed to identify peptides. Proteome Discover (ThermoFisher version 2.1) was used to identify peptides from LC-MS/MS data. Enzyme specificity was set to unspecific. The mass tolerance for fragments and peptides were 20 ppm and 0.02 Da, respectively. The sequences were searched against the Zea mays sequences in the UniProt database (http://www.uniprot.org/, accessed on 2 March 2021, UPID: UP000007305) and concatenated to the database of proteomics research. The fixed modifications were carbamidomethyl-Cys, 6-plex TMT at the N-termini and Lys-6-plex TMT, but Met oxidation was variable. Each peptide quantification value was exported to an Excel output file, and the average peptide ratios or fold change (FC = treatment/control) were determined by dividing the quantification value of each peptide in the treated samples by the quantification value of control samples. Differentially expressed peptides (DEPs) represented peptides with FC ≥ 1.25 (up-regulated) or ≤0.8 (down-regulated), along with significantly different abundances (p-value < 0.05). The peptidomics data were uploaded to iProX database (iProX ID: IPX0005992000).
+ Open protocol
+ Expand
5

Phosphoproteomics of C. elegans Worms

Check if the same lab product or an alternative is used in the 5 most similar protocols
The uniprot worms database (20161228, 17,392 sequences) was searched for raw data using Proteome Discover (Version 1.4, Thermo Fisher Scientific, Bremen, Germany) followed by scoring with the Mascot server (Version 2.3, Matrix Science, London, UK). The searched parameters were set as follows: 10 ppm for the precursor ion mass deviation and 0.05 Da for the allowed fragment mass deviation; up to two miscleavage sites; cysteine carbamidomethylation, iTRAQ 8-plex on lysine and the N-terminal set as fixed modifications; methionine oxidation; and STY (serine, threonine and tyrosine) phosphorylation as variable modifications. The Target Decoy PSM verification program incorporated in the Proteome Discoverer was uitilized to verify the search results using only matches with FDR ≤ 0.01. Functional classifications of phosphoproteins were analyzed using the David database, as previously described [64 (link)]. Subcellular locations were predicted through Wolf PSORT [65 (link)]. Protein–protein interactions were detected by STRING [32 (link)]. The motifs of protein phosphorylation were derived from our phosphoproteomic data using the Motif-X algorithm [66 (link)]. The kinase-substrate phosphorylation network (KSPN) was constructed by submitting differentially expressed phosphopeptides by using iGPS 1.0 software (the CUCKOO workgroup, DICP, Dalian, China) [35 (link)].
+ Open protocol
+ Expand
6

Label-free Proteome Quantification

Check if the same lab product or an alternative is used in the 5 most similar protocols
All generated raw files were submitted to Proteome Discover (PD, version 2.3, Thermo Fisher Scientific, Germany) with label-free quantitation (LFQ) analysis. The human and mouse protein sequence databases were downloaded from UniProt database in May 2019 (http://www.uniprot.org). Database searching was performed with the following parameters: cysteine carbamidomethylating (C, +57.0215 Da) as a fixed modification; methionine oxidation (M, +15.9949 Da) and N-terminal acetylation (+42.010565 Da) as variable modifications; up to two missed cleavage sites were permitted for trypsin digestion; the tolerances of precursor and fragment masses were set at 10 ppm and 0.02 Da, respectively; 1% protein false discovery rate (FDR) was used as the filter for both protein and peptide identification, and at least two peptide-spectrum matches (PSMs) were required for a peptide identification. Label-free method in PD was used for relative quantification of proteins among samples. LFQ was performed for calculation of protein abundances. Protein ratios were calculated as the median of all pairwise ratios calculated between the three replicates of all peptide abundances.
+ Open protocol
+ Expand
7

Proteomic Analysis of Ψ-Containing mRNAs

Check if the same lab product or an alternative is used in the 5 most similar protocols
The Ψ-PTC mRNAs were compared to uridine containing (non-Ψ) PTC mRNAs (Supplementary Table S8). Flag-eGFP peptides translated in HEK-293T cells were purified with anti-Flag M2 magnetic beads (Sigma). Pulled down proteins were extensively washed with 50 mM ammonium acetate and were directly digested on the beads adding Trypsin and alkylated with iodoacetamide (55 mM). Peptides were analyzed using a Dionex, UltiMate 3000 nano-HPLC system (Germering, Germany) coupled via nanospray ionization source to a Thermo Scientific Q Exactive HF mass spectrometer (Vienna, Austria) using instrument settings as described previously (44 (link)). The database search was performed using ProteomeDiscover (Version 2.1, Thermo Scientific). Sensitivity of the assay was calculated by the median abundance of the 10 least abundant peptides in the least sensitive sample, out of the total abundance of all of the C-terminal peptides in any of the samples.
+ Open protocol
+ Expand
8

Proteomic Analysis of Lacusarx Virus

Check if the same lab product or an alternative is used in the 5 most similar protocols
Micrographs of Lacusarx virus and identification of phage proteins from purified CsCl gradient centrifugation was performed as described previously30 (link). Briefly, 75 µl of the purified phage solution was mixed with 75 µl of 1% SDS and incubated for 30 min at 80 °C followed by TCA precipitation. The proteins were re-solubilized in 8 M urea, 45 mM DTT, and 50 mM Tris, pH 8.0, reduced and alkylated. The proteins were digested with trypsin and the resulting peptides were analysed using a Dionex 3000 RSLC UHPLC system (ThermoFisher Scientific, Hvidovre, Denmark) with an Aeris PEPTIDE 1.7 µm XB-C18, 150 × 2.1 mm column (phenomenex, Vaerloese, Denmark) coupled with a Q Exactive mass spectrometer (ThermoFisher Scientific, Hvidovre, Denmark). The resulting data was analysed with Proteome Discover (version 1.4, ThermoFisher Scientific, Hvidovre, Denmark) using a homemade protein database based on the obtained DNA sequences. The search results were filtered in Proteome Discover with the integrated Target decoy PSM validator algorithm to a q-value of <0.01, which ensures a peptide-spectrum match false discovery rate less than 0.01.
+ Open protocol
+ Expand
9

Quantitative Proteomics Analysis of Cells

Check if the same lab product or an alternative is used in the 5 most similar protocols
Raw MS data from 22 fractions were searched against Swiss-Prot database (March 8, 2015; Homo sapiens; 54,7964 sequences—after human taxonomy filter applied—20,203) using the MASCOT software (Matrix Science Ltd, version 2.2) through Proteome Discover software (Thermo Fisher Scientific, version 1.4). Mascot significance threshold was set to 0.05. Trypsin/P was specified as the cleavage enzyme allowing up to two missed cleavages. The database search included the following parameters: MS1 Tolerance: 10 ppm, MS2 Tolerance: 0.06 Da, fixed modification: Carbamidomethyl (C). Variable Modification: Oxidation (M), Dioxidation (M), Acetyl (N-term), Gln->pyro-Glu (N-term Q), hydroxyl (P), TMT10plex (N-term), and TMT10plex (K). The data were filtered by applying a 1% false discovery rate. Quantified proteins were filtered if the absolute fold-change difference between conditions to respective DMSO of the same biological replicate was ≥1.3 for upregulated proteins and ≤0.7 for downregulated proteins.
+ Open protocol
+ Expand
10

Proteome Quantification Using DIA-MS

Check if the same lab product or an alternative is used in the 5 most similar protocols
Data‐independent acquisition MS was used for proteins identification as we have previously described.19 In brief, samples were dissolved in 2% SDS lysis buffer and tryptic digestion through the FASP method. Proteome Discover (Thermo Fisher Scientific) and Spectronaut (Biognosys) software was used to quantify.
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!