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Proteome

The proteome refers to the complete set of proteins expressed by a genome, cell, tissue, or organism at a given time.
This dynamic entity reflects the gene expression and cellular activities occurring within a biological system.
Proteome analysis involves the large-scale identification, quantification, and characterization of proteins to gain insights into cellular functions, signaling pathways, and disease mechanisms.
Advanced techniques like mass spectrometry and bioinformatics enable comprehensive proteome profiling, which is crucial for understading biological processes and developing targeted therapies.
Reserachers can leverage AI-powered platforms like PubCompare.ai to streamline their proteome research, easily locating relevant protocols and identifying the most effective methods and products.

Most cited protocols related to «Proteome»

All protein interaction data sets from MIPS [13 (link)], Gene Ontology [43 (link)] and PreBIND were collected as described previously [6 (link)]. The YPD protein interaction data are from March 2001 and were originally requested from Proteome, Inc. . Other interaction data sets are from BIND . A BIND yeast import utility was developed to integrate data from SGD [12 (link)], RefSeq [44 (link)], Gene Registry , the list of essential genes from the yeast deletion consortium [11 (link)] and GO terms [43 (link)]. This database ensures proper matching of yeast gene names among the multiple data sets that may use different names for the same genes. The yeast proteome used here is defined by SGD and RefSeq and contains 6,334 ORFs including the mitochondrial chromosome. Before performing comparisons, the various interaction data sets were entered into a local instance of BIND as pairwise protein interaction records. The MIPS complex catalogue was downloaded in February 2002.
The protein interaction data sets used here were composed as follows. 'Gavin Spoke' is the spoke model of the raw purifications from Gavin et al [7 (link)]. 'Y2H' is all known large-scale [2 (link)-5 (link),10 (link)] combined with normal yeast two-hybrid results from MIPS. 'HTP Only' is only high-throughput or large-scale data [2 (link)-7 (link),10 (link)] The 'Benchmark' set was constructed from MIPS, YPD and PreBIND as previously described [6 (link)]. 'Pre HTMS' was composed of all yeast sets except the recent large-scale mass spectrometry data sets [6 (link),7 (link)]. 'AllYeast' was the combination of all above data sets. All data sets are non-redundant.
Publication 2003
Deletion Mutation DNA, Mitochondrial Genes Genes, Essential Hybrids Macrophage Inflammatory Protein-1 Mass Spectrometry Open Reading Frames Proteins Proteome Saccharomyces cerevisiae SET protein, human
An Escherichia coli K12 strain was grown in standard LB medium, harvested, washed in PBS, and lysed in BugBuster (Novagen Merck Chemicals, Schwalbach, Germany) according to the manufacturer's protocol. HeLa S3 cells were grown in standard RPMI 1640 medium supplemented with glutamine, antibiotics, and 10% FBS. After being washed with PBS, cells were lysed in cold modified RIPA buffer (50 mm Tris-HCl, pH 7.5, 1 mm EDTA, 150 mm NaCl, 1% N-octylglycoside, 0.1% sodium deoxycholate, complete protease inhibitor mixture (Roche)) and incubated for 15 min on ice. Lysates were cleared by centrifugation, and after precipitation with chloroform/methanol, proteins were resuspended in 6 m urea, 2 m thiourea, 10 mm HEPES, pH 8.0. Prior to in-solution digestion, 60-μg protein samples from HeLa S3 lysates were spiked with either 10 μg or 30 μg of E. coli K12 lysates based on protein amount (Bradford assay). Both batches were reduced with dithiothreitol and alkylated with iodoacetamide. Proteins were digested with LysC (Wako Chemicals, GmbH, Neuss, Germany) for 4 h and then trypsin digested overnight (Promega, GmbH, Mannheim, Germany). Digestion was stopped by the addition of 2% trifluroacetic acid. Peptides were separated by isoelectric focusing into 24 fractions on a 3100 OFFGEL Fractionator (Agilent, GmbH, Böblingen, Germany) as described in Ref. 45 (link). Each fraction was purified with C18 StageTips (46 (link)) and analyzed via liquid chromatography combined with electrospray tandem mass spectrometry on an LTQ Orbitrap (Thermo Fisher) with lock mass calibration (47 (link)). All raw files were searched against the human and E. coli complete proteome sequences obtained from UniProt (version from January 2013) and a set of commonly observed contaminants. MS/MS spectra were filtered to contain at most eight peaks per 100 mass unit intervals. The initial MS mass tolerance was 20 ppm, and MS/MS fragment ions could deviate by up to 0.5 Da (48 (link)). For quantification, intensities can be determined alternatively as the full peak volume or as the intensity maximum over the retention time profile, and the latter method was used here as the default. Intensities of different isotopic peaks in an isotope pattern are always summed up for further analysis. MaxQuant offers a choice of the degree of uniqueness required in order for peptides to be included for quantification: “all peptides,” “only unique peptides,” and “unique plus razor peptides” (42 (link)). Here we chose the latter, because it is a good compromise between the two competing interests of using only peptides that undoubtedly belong to a protein and using as many peptide signals as possible. The distribution of peptide ions over fractions and samples is shown in supplemental Fig. S1.
Publication 2014
Acids Antibiotics, Antitubercular Biological Assay Buffers Cells Centrifugation Chloroform Cold Temperature Deoxycholic Acid, Monosodium Salt Digestion Dithiothreitol Edetic Acid Escherichia coli Escherichia coli K12 Glutamine HeLa Cells HEPES Homo sapiens Immune Tolerance Iodoacetamide Ions Isotopes Liquid Chromatography Methanol Peptides Promega Protease Inhibitors Proteins Proteome Radioimmunoprecipitation Assay Retention (Psychology) Sodium Chloride Staphylococcal Protein A Tandem Mass Spectrometry Thiourea Tromethamine Trypsin Urea
A gene tree is the canonical representation of the evolutionary relationships between the genes in a gene family. Thus, ortholog inference from gene trees is an important goal. However, no automated software tools are available that provide genome-wide ortholog inference from gene trees. A number of challenges had to be addressed to enable this. These included the efficient partitioning of genes into small, non-overlapping sets such that all orthologs of a gene are contained in the same set as the original gene; scalable and accurate inference of gene trees from these gene sets; automatic rooting of these gene trees without a user-provided species tree; and robust ortholog inference in the presence of imperfect gene tree inference. The OrthoFinder workflow was designed to address each of these challenges and is described in detail below.
By default, OrthoFinder infers orthologs from the orthogroup trees (a gene tree for the orthogroup) using the steps shown in Fig. 2. Input proteomes are provided by the user using one FASTA file per species. Each file contains the amino acid sequences for the proteins in that species. Orthogroups are inferred using the original OrthoFinder algorithm [10 (link)]; an unrooted gene tree is inferred for each orthogroup using DendroBLAST [24 (link)]; the unrooted species tree is inferred from this set of unrooted orthogroup trees using the STAG algorithm [33 ]; this STAG species tree is then rooted using the STRIDE algorithm by identifying high-confidence gene duplication events in the complete set of unrooted orthogroup trees [22 (link)]; the rooted species tree is used to root the orthogroup trees; orthologs and gene duplication events are inferred from the rooted orthogroup trees by a novel hybrid algorithm that combines the “species-overlap” method [31 ] and the duplication-loss-coalescent model [32 (link)] (described below); and comparative statistics are calculated. All major steps of the algorithm are parallelized to allow optimal use of computational resources. Only the orthogroup inference was provided in the original implementation of OrthoFinder [10 (link)]; all other subsequent steps are new and described below.
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Publication 2019
Amino Acid Sequence Biological Evolution Gene Duplication Genes Genes, vif Genome Hybrids Proteins Proteome Trees

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Publication 2014
4,4'-dibenzamido-2,2'-stilbenedisulfonic acid Amino Acid Sequence Clone Cells Cloning Vectors DNA Replication Escherichia coli Eukaryota Genome Oligonucleotide Primers Plasmids Proteins Proteome
We constructed sets of fungal proteomes of increasing size for performance testing. Ensembl Genomes was interrogated on 6 November 2017 using its REST API [44 (link)] to identify all available fungal genomes. To achieve an even sampling of species, we selected 1 species per genera and excluded genomes from candidate phyla or phyla with fewer than 3 sequenced genomes. This gave a set of 272 species which were downloaded from the Ensembl FTP site [45 (link)]. We created datasets of increasing size by randomly selecting 4, 8, 16, 32, 64, 128, and 256 species such that the last common ancestor was the same for each dataset. Each dataset was analyzed using a single Intel E5-2640v3 Haswell node (16 cores) on the Oxford University ARCUS-B server using 16 parallel threads for OrthoFinder with DIAMOND (arguments: “-S diamond -t 16 -a 16”). The complete datasets for all analyzed species subsets are available for download from Zenodo at 10.5281/zenodo.1481147. All methods submitted to Quest for Orthologs that provided a user-runnable implementation of the method were tested on the same fungi datasets and the same ARCUS-B server nodes and run in parallel using 16 threads (when supported by the method).
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Publication 2019
A-272 Diamond Fungi Genome Genome, Fungal Proteome

Most recents protocols related to «Proteome»

The reference proteomes of K. pneumoniae (strain ATCC 700721/MGH 78578) and P. aeruginosa (strain ATCC 15692/DSM 22644/CIP 104116/JCM 14847/LMG 12228/1C/PRS 101/PAO1) were downloaded from the UniProt webserver (https://www.uniprot.org/) under the proteome ID of UP000000265 and UP000002438, respectively. As mentioned in the introduction section, the current study aims to design an epitope-based vaccine through the filtration of protein candidates belonging to the outer membrane and iron uptake proteins. Therefore, we selected nine K. pneumoniae protein candidates namely FepA, FepB, FepC, FhuA, FhuF, FuR (iron uptake proteins), OmpA, OmpC, and OmpF (outer membrane proteins), and filtered them through their antigenicity score estimated by VaxiJen v2.0 (Doytchinova and Flower, 2007 (link)) with the cutoff score of 0.4 (the threshold value of bacterial antigenic proteins). The assessment of the antigenicity score revealed that there were 8 antigenic proteins, out of the selected 9 ones therefore we selected the top 2 proteins (one protein from each category) based on their antigenicity score where the final 2 protein candidates of K. pneumoniae were FepA and OmpF with antigenicity scores of 0.76 and 0.81 respectively. Moving to P. aeruginosa, we followed the same approach where six protein candidates namely FoxA, FpvA, HasR, HitA (iron uptake proteins), OprF, and OprH (outer membrane proteins) were filtered and 2 proteins (also one from each category) namely HasR and OprF with the antigenicity scores of 0.59 and 0.8 respectively were selected as our final candidates for P. aeruginosa.
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Publication 2023
Antigens Antigens, Bacterial Bacterial Proteins Epitopes Filtration Iron Klebsiella pneumoniae Membrane Proteins OmpC protein Proteins Proteome Pseudomonas aeruginosa Tissue, Membrane vaccin
This study included five ICC patient cohorts. (1) The FU-iCCA cohort enrolled 262 ICC patients from Zhongshan Hospital, Fudan University [22 (link)]. Multi-omics data of this cohort, including data of whole-exome sequencing (WES), RNA sequencing, and proteome, were analyzed. (2) The second cohort recruited 259 patients with pathologically confirmed ICC undergoing curative resection in Zhongshan Hospital (ZSH cohort) from June 2012 to December 2017. All enrolled patients received no anti-cancer therapy prior to surgery. All tumor specimens from the ZSH cohort were formalin-fixed and paraffin-embedded and collected for tissue microarrays (TMA) construction. The ZSH cohort was used to validate the findings from the FU-iCCA cohort. The baseline characteristics of the FU-iCCA cohort and the ZSH cohort are detailed in Table 1. Serological tests were performed within 3 days before the operation. The clinical stage was evaluated based on the American Joint Committee on Cancer (AJCC) 8th edition [23 (link)]. (3) The third cohort included five ICC patients from the single-cell RNA sequencing dataset GSE138709 [24 (link)]. (4) We extracted the single-cell data of ten ICC patients for the immune checkpoint blockade (ICB) clinical trial (ICB cohort) from GSE151530 and divided them into two groups (baseline group and ICB-treated group) [25 (link)]. (5) The fifth cohort recruited an independent cohort of 33 ICC patients receiving surgical resection from January 2019 to June 2019 in Zhongshan Hospital. CD73 expression between matched tumor and para-tumor tissues was compared by RT-PCR assays.

Correlation between CD73 expression and clinical features of patients enrolled

CharacteristicsFU-iCCA cohort (n = 244)ZSH cohort (n = 259)
PatientsCD73 expressionPatientsCD73 expression
No%LowHighP valueNo%LowHighP value
All patients244100140104259100146113
Sex
 Female10643.462440.7589938.267320.004
 Male13856.6786016061.87981
Age
  ≤ 6011346.367460.57412548.369560.714
  > 6013153.7735813451.77757
HBsAg
 Negative17973.4104750.70517969.197820.290
 Positive6526.636298030.94931
Liver cirrhosis
 No22291.0128940.77818671.8104820.813
 Yes229.012107328.24231
Vascular invasion
 No14157.887540.11018571.4115700.003
 Yes10342.253507428.63143
LN metastasis
 No19479.5114800.38920779.9124830.022
 Yes5020.526245220.12230
Tumor size
  ≤ 5 cm10844.363450.78811644.871450.158
  > 5 cm13655.7775914355.27568
CA199
  ≤ 37 U/mL10944.775340.00111644.876400.008
  > 37 U/mL13555.3657014355.27073
CEA
  ≤ 5 ng/mL18575.8116690.00318973.0118710.001
  > 5 ng/mL5924.224357027.02842
AJCC 8th
 I–II15463.196580.04020378.4122810.021
 III–IV9036.944465621.62432

Data in bold indicated statistical significance

ZSH cohort Zhongshan Hospital cohort, LN Lymph node, CEA Carcinoembryonic antigen, AJCC American Joint Committee on Cancer

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Publication 2023
Biological Assay Carcinoembryonic Antigen Formalin Immune Checkpoint Blockade Inpatient Joints Liver Cirrhosis Malignant Neoplasms Microarray Analysis Neoplasm Metastasis Neoplasms Nodes, Lymph NT5E protein, human Operative Surgical Procedures Paraffin Patients Proteome Reverse Transcriptase Polymerase Chain Reaction Tests, Serologic Therapeutics Tissues
In this study, the O-glycome and proteome of CaCo-2 cell line were analyzed from three biological replicates. The samples were collected and analyzed from five different time points, starting from day 5, to days 7, 14, 21, and 24, postconfluence in two groups: spontaneous differentiation and butyrate-stimulated differentiation. Differences between groups and time points were tested using two-way ANOVA with significance level of α = 0.05 both for glycomics and proteomics datasets. Data analysis and visualization was performed using in-house developed “R’’ scripts. To enable use of principal component analysis, imputation of minimum positive number (0.0001) was performed.
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Publication 2023
Biopharmaceuticals Butyrate Caco-2 Cells neuro-oncological ventral antigen 2, human Proteome
Glycopeptide compositional analysis was performed from m/z features using in-house written SysBioWare software (Vakhrushev et al., 2009 (link)). For m/z feature recognition from full MS scans Minora Feature Detector Node of the Proteome discoverer 2.3 (ThermoFisher Scientific) was used. A list of precursor ions (m/z, charge and retention time) was imported as ASCII data into SysBioWare and compositional assignment within 5 ppm mass tolerance was performed. The main building blocks used for the compositional analysis were: NeuAc, Hex, HexNAc, dHex and phosphate. The most prominent peptides corresponding to each potential glycosite were determined experimentally by comparing the yield of deamidated peptides before and after PNGase F treatment. The peptide sequence was determined by HCD MS/MS and the abundance level was calculated from PD 2.3. For N-glycopeptide compositional analysis the corresponding peptides were also added as building blocks.
A list of potential glycan and glycopeptides for each glycosite was generated and the top 10–15 of the most abundant candidates were selected for targeted MS/MS analysis to confirm the proposed structure. Each targeted MS/MS spectrum was subjected to manual interpretation. Since the same N-glycan composition may represent various isobaric structures, the final glycan structures were proposed according to literature data, predicted enzyme functions of the targeted genes, along with information in MS/MS fragments.
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Publication 2023
Enzymes Glycopeptidase F Glycopeptides Immune Tolerance Ions Operator, Genetic Peptides Phosphates Polysaccharides Proteome Radionuclide Imaging Retention (Psychology) Tandem Mass Spectrometry
Before 2 h of HDX analysis, the compound tutin (200 μM) was added into the sample, with the control sample adding an equal volume of tutin buffer. For deuterium labeling, CN (4 μM) in the buffer (20 mM Tris-HCl, 1 mM CaCl2, 0.5 mM TCEP, and 150 mM NaCl, in H2O, pH 7.5) in the presence or absence of 200 μM tutin was diluted 10-fold by the labeling buffer containing 20 mM Tris-HCl, 1 mM CaCl2, 0.5 mM TCEP, and 150 mM NaCl, in 100% D2O at pD 7.4. After incubation for 30, 90 or 300 seconds at 25 °C, the same volume of ice-cold quench buffer containing 4 M guanidine hydrochloride, 500 mM TECP and 200 mM citric acid in water solution at pH 1.8, 100% H2O, was added to quench deuterium uptake. The sample was digested with pepsin (Promega) on ice for 5 min, and removed by centrifugation. An ACQUITY UPLC BEH C18 column (2.1 μm, 1.0 mm × 50 mm, Waters) equipped with an Ultimate 3000 UPLC system (Thermo Scientific) were used for the obtained peptides separation. A Q Exactive mass spectrometer was used for mass spectrometry analysis of the peptides. Mass spectrometry data were compared with Proteome Discoverer (Thermo Scientific) to match the corresponding peptide in CN. XCALIBUR (Thermo Scientific) was used to inspected peptide peaks. In order to estimate the max deuterium uptake of peptides, a repeated experiment was performed extending incubation in D2O for 24 h. HDExaminer (Sierra Analytics) was used for calculating deuterium uptake levels. Deut % for different peptides were calculated as follows. Deuti%=#Di/(#(CONH)i#Proi1)MaxDi×100% # Di: deuterium numbers for peptide i at a certain hydrogen/deuterium exchange time; #(CONH)i : amide bond numbers of each peptide; # Proi: the proline number for peptidei; Mxx Di: maximum deuterium uptake for peptide i.
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Publication 2023
Amides Buffers Centrifugation Citric Acid Cold Temperature Deuterium Hydrochloride, Guanidine Hydrogen Mass Spectrometry Neoplasm Metastasis Pepsin A peptide I Peptides Proline Promega Proteome Sodium Chloride tris(2-carboxyethyl)phosphine Tromethamine tutin

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More about "Proteome"

The proteome, also known as the complete set of proteins expressed by a genome, cell, tissue, or organism at a given time, is a dynamic entity that reflects the gene expression and cellular activities within a biological system.
Proteome analysis, a crucial field of study, involves the large-scale identification, quantification, and characterization of proteins to gain insights into cellular functions, signaling pathways, and disease mechanisms.
Advanced techniques like mass spectrometry, powered by instruments like the Q Exactive mass spectrometer, and bioinformatics tools, such as the Mascot search engine (version 2.4) and Proteome Discoverer software (versions 1.4 and 2.2), enable comprehensive proteome profiling.
These tools are essential for understanding biological processes and developing targeted therapies.
Researchers can leverage AI-powered platforms like PubCompare.ai to streamline their proteome research.
This cutting-edge technology helps scientists easily locate relevant protocols from literature, preprints, and patents, while its intelligent comparisons identify the most effective protocols and products.
Proteome Profiler Human XL Cytokine Array Kit is one example of a product that can be used in proteome research.
By incorporating synonyms like 'proteomics' and 'protein expression,' as well as related terms like 'trypsin' and 'mass spectrometry,' this content provides a holistic overview of the proteome and the tools and techniques used in proteome research.
The inclusion of key subtopics, such as cellular functions, signaling pathways, and disease mechanisms, further enriches the understanding of this important field of study.