We used two experimental yeast PPI data sets6 (link),7 (link), a combined computational interaction map8 (link) and the entire set of physical protein-protein interactions in yeast from BioGRID9 (link) (Supplementary Table 3 ). Here we refer to these as the Gavin6 (link), Krogan7 (link), Collins8 (link) and BioGRID9 (link) data sets. The Gavin data set was obtained by considering all PPIs with a socio-affinity index larger than five6 (link). The Krogan data set7 (link) was used in two variants: the core data set (referred to as Krogan core) contained only highly reliable interactions (probability > 0.273), and the extended data set (referred to as Krogan extended) contained more interactions with less overall reliability (probability > 0.101). The socio-affinity and probability cutoffs we used have been proposed by the original authors. In the Collins data set, we used the top 9,074 interactions according to their purification enrichment score8 (link), as suggested in the original paper. When applying algorithms that cannot handle weights (MCODE, CMC, RNSC and CFinder) to the above networks, weights were ignored. The BioGRID data set was downloaded from version 3.1.77 and contained all physical interactions that involve yeast proteins only. Self-interactions and isolated proteins were filtered from all the data sets. As BioGRID provides weights for only 18.05% of the interactions, we treated the entire BioGRID network as unweighted, keeping the weighted interactions but disregarding their weights.
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Yeast Proteins
Yeast Proteins
Yeast Proteins: A comprehensive overview of the diverse proteins found in yeast, a eukaryotic microorganism widely used in scientific research.
Yeast proteins play crucial roles in cellular processes, metabolism, and gene expression, making them a valuable subject of study.
Experiance the power of PubCompare.ai's AI-driven platform to optimize your yeast protein research and accelertate your discoveries.
Yeast proteins play crucial roles in cellular processes, metabolism, and gene expression, making them a valuable subject of study.
Experiance the power of PubCompare.ai's AI-driven platform to optimize your yeast protein research and accelertate your discoveries.
Most cited protocols related to «Yeast Proteins»
Physical Examination
Prepulse Inhibition
Proteins
Saccharomyces cerevisiae
SET protein, human
Yeast Proteins
AnGeLi utilizes the S. pombe Ensembl annotation database (version 27) as the source for gene features (Kersey et al., 2010 (link)), which is based on PomBase (McDowall et al., 2015 (link)) and is implemented in the annmap core Bioconductor/R package (Gentleman et al., 2004 (link); Yates et al., 2008 (link)). The database was used to derive the following: list of genes, exons, proteins, and their chromosomal positions as well as transcript biotypes (i.e., protein-coding, ncRNA, etc.). Applying customized R and Perl scripts, these data were used to compute relative and absolute distances from centromere and telomeres. Similarly, these data were used to compute intron locations, intron number per gene, average intron length and total transcript length. The GC content of the first intron was computed using the ‘geecee’ function within the EMBOSS (Rice et al., 2000 (link)). The protein sequence data was downloaded from PomBase (McDowall et al., 2015 (link)), and protein features such as molecular weight, isoelectric point, charge, and number of amino acids were also calculated using the EMBOSS suite (pepstats function). Amino acid compositions were calculated using a customized Perl script. The fold-index for each fission yeast protein was computed using a modified Perl script available from http://bip.weizmann.ac.il/fldbin/findex (Prilusky et al., 2005 (link)). S. pombe GO annotations and the generic GO OBO flat file were downloaded from ftp://ftp.geneontology.org . A recursive algorithm was used to map genes to all corresponding ancestor terms in the ontology. Pfam domains (Finn et al., 2014 (link)) were retrieved from the xmapcore database (Yates et al., 2008 (link)). For phenotype mappings (Harris et al., 2013 (link)), we used the phenotype annotation ‘phaf’ file available from ftp://ftp.ebi.ac.uk/pub/databases/pombase/pombe/Phenotype_annotations/phenotype_annotations.pombase.phaf.gz , fypo OBO file available from https://cdn.rawgit.com/pombase/fypo/master/release/fypo.obo . We only considered GO terms, Pfam domains and phenotypes that were associated with at least two genes. The manually curated human and budding yeast orthologs of fission yeast proteins (Wood, 2006 ) were retrieved from ftp://ftp.ebi.ac.uk/pub/databases/pombase/pombe/orthologs/cerevisiae-orthologs.txt . Physical and genetic interaction data were downloaded from BioGRID (Breitkreutz et al., 2008 (link)) and processed using customized Perl scripts. All binary and metric data were combined into a single table using an R script (similar to Table 1 ) prior to conversion into Perl associative array data structures. Pairwise relationships were stored directly in Perl data structures.
Amino Acids
Amino Acid Sequence
Centromere
Chromosomes
Exons
Generic Drugs
Genes
Homo sapiens
Introns
Phenotype
Physical Examination
Proteins
Reproduction
Rice
RNA, Untranslated
Schizosaccharomyces pombe
Synapsin I
Telomere
Yeast Proteins
We prepared a data set of 759 presequence containing mitochondrial proteins by combining the data sets of TargetP and Predotar (containing proteins from various eukaryotes) with presequences identified via recent mitochondrial N-terminal proteome measurements on S. cerevisiae (7 (link)), and on A.thaliana and O.sativa (22 (link)). Based on an initial inspection of the data, when developing MitoFates we decided to discard any putative mature N-termini from these studies that cannot be explained as the product of cleavage by MPP with an arginine at the −2 position (possibly followed by secondary cleavage by Icp55 or Oct1). We made this decision because for the rest of the data we failed to discern any overall pattern in either the local sequence surrounding the putative cleavage sites or the distance from the original N-termini. Presumably, this non-R-2 site data includes proteins processed by proteases such as IMP and m-AAA, possibly some noncanonical MPP cleavage and probably some nonspecific degradation products. Although we did not include these sites when developing MitoFates itself, we did include them in an exploratory clustering experiment described below. Note that we did include plant mature N-termini with an arginine at the −3 position as they could plausibly be explained as the product of canonical MPP cleavage followed by an additional cleavage of one N-terminal residue by a plant counterpart to yeast Icp55. For negative examples, we used 6310 nonmitochondrial proteins with clear UniProt annotation of subcellular localization and 108 noncleaved yeast mitochondrial proteins (7 (link)). These sequences (taken from UniProt (23 (link)) ver. 2012 10) were selected such that no pair shared more than 80% mutual sequence identity within the positive or negative data sets. To compare the prediction performance of MitoFates with previous methods, we prepared an independent test data set consisting of 78 mitochondrial proteins possessing a presequence and 8934 nonmitochondrial proteins; in such a way that the sequence identity between training and test data sets and within the positive and negative data sets is less than 25%.
Arginine
Cytokinesis
Eukaryota
Mitochondria
Mitochondrial Proteins
Peptide Hydrolases
Plants
POU2F1 protein, human
Protein Annotation
Proteins
Proteome
Saccharomyces cerevisiae
Yeast Proteins
The MIPS complexes, SGD complexes and those derived from high-throughput experiments were obtained from the sources listed in Table 1 .
Table 1 . The Jaccard index was used to quantify the overlap between complexes. For a pair of complexes i from CYC2008 and j from one of the other sets of complexes, the Jaccard index Jij = q/(q + r + s) is computed, where q is the number of subunits common to both i and j, while r and s are the number of subunits unique to i and j, respectively. Complex j is considered as the maximal match of i if Jij ⩾ Jix for any complex x ≠ j in the complex set.
To quantify the modularity of the complexes in each set, the overlap among complexes in the same set was calculated as detailed in Pu et al. (8 (link)). Briefly, the average overlap per complex, Overlap_C, is computed as (2 × Noc)/Nc, where Noc is the total number of unordered pairs of overlapping complexes in the network and Nc is the total number of complexes. The average number of proteins shared between overlapping complexes was computed as where nij is the number of proteins shared between pairs of complexes i and j, i ≠ j. Nc is the total number of complexes and Noc is the total number of pairs of overlapping complexes as mentioned above.
Sources of information on yeast protein complexes
| Complexes | Source |
|---|---|
| MIPS | |
| SGD | |
| BioGrid | Reguly et al. (17 (link)) |
| YHTP2008 | Pu et al. (8 (link)) |
| Gavin | Gavin et al. (10 (link)) |
| Krogan | Krogan et al. (2 (link)) |
The MIPS complexes were taken at the leaf level of the hierarchical scheme, excluding homomeric complexes and complexes bearing the Systematic Analysis Code 550. The SGD complexes (file name at the SGD ftp site: go_protein_complex_slim.tab) represents mapping of gene products to the direct children of the ‘Macromolecular complex’ GO term (GOID:32991). BioGrid complexes can be found in the online
To quantify the modularity of the complexes in each set, the overlap among complexes in the same set was calculated as detailed in Pu et al. (8 (link)). Briefly, the average overlap per complex, Overlap_C, is computed as (2 × Noc)/Nc, where Noc is the total number of unordered pairs of overlapping complexes in the network and Nc is the total number of complexes. The average number of proteins shared between overlapping complexes was computed as where nij is the number of proteins shared between pairs of complexes i and j, i ≠ j. Nc is the total number of complexes and Noc is the total number of pairs of overlapping complexes as mentioned above.
Child
Genome
NADH Dehydrogenase Complex 1
Plant Leaves
Proteins
Protein Subunits
Saccharomyces cerevisiae
TNPO1 protein, human
Yeast Proteins
To test the effectiveness of various modes of composition adjustment for TBLASTN, we performed a number of tests using the yeast nuclear genome. We downloaded the yeast genome from[53 ], a site containing reference genomes curated by NCBI staff. The version of the genome that we used was created on May 16, 2005.
We aligned a set of 102 protein domains to the yeast nucleotide genome using TBLASTN. This test set was first developed for the study in [11 (link)]. An updated version was used in [4 (link)], in which a human curated list of true positive matches to the yeast proteome was used to generate ROC scores. For the tests described here, we updated the true positive list to reflect changes in the published yeast genome. The updated list contains 987 query-subject matches to 894 distinct subject sequences. The version of the test set used in this paper is provided asAdditional file 2 .
In the yeast genome, each known yeast protein is annotated with the location and strand of its coding region. These annotations allow us to adapt the test set for use with TBLASTN as follows. For TBLASTN, alignments are divided into three categories: (1) alignments that match a query to the coding region of a known true positive match; (2) alignments that match a query to a known coding region that is not a true positive match; and (3) alignments that do not match a known coding region. An alignment is said to match a query to a coding region if the subject portion of the alignment overlaps the coding region and is on the same strand.
It is not uncommon for there to be more than one alignment between a query and a coding region. Indeed this is expected; protein-protein searches also report multiple alignments between pairs of proteins. When there is more than one alignment to a coding region, only the lowest E-value alignment between a particular query and the coding region is used when computing ROC scores. No attempt is made to apply a similar rule to noncoding regions. All alignments that do not overlap a coding region are categorized as false positive matches and counted when computing ROC scores.
We made two explicit exceptions to this scheme for classifying hits. The first exception is to add a particular pseudogene (Entrez Gene ID 850644) to our list of coding regions and to make the pseudogene a true positive for one of our queries, raising the maximum possible number of true positives to 988. Each of the variants tested found an alignment to this pseudogene with E-value smaller than 10-12. The pseudogene is expressed and produces a functional protein under certain conditions [54 (link)-56 (link)]. Though this region is labeled as a pseudogene, we do not believe an alignment algorithm should be expected to distinguish it from a true gene. The second exception is to categorize a particular alignment that overlaps one true positive coding region and one false positive coding region as a true positive match. This overlap is reported by all three variants of TBLASTN.
We aligned a set of 102 protein domains to the yeast nucleotide genome using TBLASTN. This test set was first developed for the study in [11 (link)]. An updated version was used in [4 (link)], in which a human curated list of true positive matches to the yeast proteome was used to generate ROC scores. For the tests described here, we updated the true positive list to reflect changes in the published yeast genome. The updated list contains 987 query-subject matches to 894 distinct subject sequences. The version of the test set used in this paper is provided as
In the yeast genome, each known yeast protein is annotated with the location and strand of its coding region. These annotations allow us to adapt the test set for use with TBLASTN as follows. For TBLASTN, alignments are divided into three categories: (1) alignments that match a query to the coding region of a known true positive match; (2) alignments that match a query to a known coding region that is not a true positive match; and (3) alignments that do not match a known coding region. An alignment is said to match a query to a coding region if the subject portion of the alignment overlaps the coding region and is on the same strand.
It is not uncommon for there to be more than one alignment between a query and a coding region. Indeed this is expected; protein-protein searches also report multiple alignments between pairs of proteins. When there is more than one alignment to a coding region, only the lowest E-value alignment between a particular query and the coding region is used when computing ROC scores. No attempt is made to apply a similar rule to noncoding regions. All alignments that do not overlap a coding region are categorized as false positive matches and counted when computing ROC scores.
We made two explicit exceptions to this scheme for classifying hits. The first exception is to add a particular pseudogene (Entrez Gene ID 850644) to our list of coding regions and to make the pseudogene a true positive for one of our queries, raising the maximum possible number of true positives to 988. Each of the variants tested found an alignment to this pseudogene with E-value smaller than 10-12. The pseudogene is expressed and produces a functional protein under certain conditions [54 (link)-56 (link)]. Though this region is labeled as a pseudogene, we do not believe an alignment algorithm should be expected to distinguish it from a true gene. The second exception is to categorize a particular alignment that overlaps one true positive coding region and one false positive coding region as a true positive match. This overlap is reported by all three variants of TBLASTN.
Genes
Genome
Homo sapiens
Nucleotides
Proteins
Proteome
Pseudogenes
Saccharomyces cerevisiae
SET protein, human
Staphylococcal Protein A
Yeast Proteins
Most recents protocols related to «Yeast Proteins»
NGS library preparation was performed as previously described7 ,10 . Briefly, DNA was extracted from yeast libraries using Zymoprep-96 Yeast Plasmid Miniprep kits or Zymoprep Yeast Plasmid Miniprep II kits (Zymo Research) according to standard manufacturer protocols. A first round of PCR was used to amplify a DNA sequence containing the protein display barcode on the yeast plasmid, as previously described7 ,10 . A second round of PCR was performed on 1 µL step 1 PCR product using Nextera i5 and i7 dual-index library primers (Illumina), as previously described7 ,10 . PCR products were pooled, and run on a 1% agarose gel, and DNA corresponding to the band at 257 base pairs was cut. DNA (NGS library) was extracted using a QIAquick Gel Extraction Kit (Qiagen) according to standard manufacturer protocols. NGS library was sequenced using an Illumina NextSeq550 and a NextSeq high output sequencing kit with 75 base pair single-end sequencing according to standard manufacturer protocols. A minimum of 200,000 reads on average per sample was collected and the pre-selection library was sampled at least ten times greater depth than other samples. Samples with fewer than 50,000 reads were discarded as failed sequencing.
Base Pairing
DNA, A-Form
DNA Library
Oligonucleotide Primers
Plasmids
Saccharomyces cerevisiae
Sepharose
Yeast Proteins
The ORF of SeHKT1;2 was inserted into the yeast protein expression vector pYES2. To test the Na+ Absorption and efflux function of SeHKT1;2, the Saccharomyces cerevisiae yeast mutant strain AXT3K (enal::Hls3::ena4,nhal::LEuZ,nhxl::KanMx4), which lacks the main plasma membrance Na+ transporters, and G19 (MATa ade2ura3leu2his3trp1 ena1Δ::HIS3Δ::ena4Δ) disrupted in the ENA1-4 genes encoding Na+ export pumps were used. The plasmids were introduced by PEG/LiAc method. Positive transformants were selected on Ura-selective medium [0.67% (w/v) yeast nitrogen base without amino acids, 0.077% (w/v) DO supplement-Ura, 2% (w/v) galactose, and 1.5% (w/v) agar]. Growth at variable Na+ concentrations with 0, 25, 30, 60, 100 or 150 mM for AXT3K and 0, 60, 100, 150, 300 mM for G19 were tested in arginine phosphate (AP) medium [8 mM phosphoric acid, 10 mM L-Arginine, 2 mM MgSO4, 0.2 mM CaCl2, 2% glucose, plus vitamins and trace elements, and 1.5% (w/v) agar, pH 6.5].
Meanwhile, Yeast (Saccharomyces cerevisiae) strain CY162 (MATa, △trk1, trk2:: pCK64, his3, leu2, ura3, trp1, ade2), which is a K+-uptake-defective mutant and cannot grow without supplement K+ was used to test K+-uptake function of SeHKT1;2 gene. Positive transformants were selected on Ura-selective medium with 100 mM KCl. Yeast growth experiments were performed on arginine-phosphate (AP) medium with added K+ (1 mM, 10 mM) and supplemented with 100 mM, 150 mM and 300 mM Na+ concentrations. Control experiments were performed with the yeast modified with vector pYES2 and pYES2-SeHKT1;2 growing under 100 mM KCl.
For the yeast growth test experiment, all transformed yeasts were cultured overnight at 30°C in AP medium until the OD600 reached 0.8, and 10-fold serial diluted cultures were incubated on AP plates containing the indicated concentrations of K+ and Na+. The plates were incubated at 30°C for 5 days. Control experiments were performed with the yeast wild type modified with vector pYES2.
Meanwhile, Yeast (Saccharomyces cerevisiae) strain CY162 (MATa, △trk1, trk2:: pCK64, his3, leu2, ura3, trp1, ade2), which is a K+-uptake-defective mutant and cannot grow without supplement K+ was used to test K+-uptake function of SeHKT1;2 gene. Positive transformants were selected on Ura-selective medium with 100 mM KCl. Yeast growth experiments were performed on arginine-phosphate (AP) medium with added K+ (1 mM, 10 mM) and supplemented with 100 mM, 150 mM and 300 mM Na+ concentrations. Control experiments were performed with the yeast modified with vector pYES2 and pYES2-SeHKT1;2 growing under 100 mM KCl.
For the yeast growth test experiment, all transformed yeasts were cultured overnight at 30°C in AP medium until the OD600 reached 0.8, and 10-fold serial diluted cultures were incubated on AP plates containing the indicated concentrations of K+ and Na+. The plates were incubated at 30°C for 5 days. Control experiments were performed with the yeast wild type modified with vector pYES2.
Agar
Amino Acids, Basic
Arginine
Cloning Vectors
Galactose
Genes
Glucose
Membrane Transport Proteins
Nitrogen
phospho-L-arginine
Phosphoric Acids
Plasma
Plasmids
Saccharomyces cerevisiae
Strains
Sulfate, Magnesium
Trace Elements
tyrosinase-related protein-1
Vitamins
Yeast Proteins
Yeasts
To determine the effect of TaWD40-4B.1C and TaWD40-4B.1T on the activity of TaCAT3A in yeast, the yeast lines expressing cMyc-TaCAT3A and/or FLAG-TaDW40-4B.1C/FLAG-TaWD40-4B.1T were harvested. The total proteins of yeast lines were extracted using the One Step Yeast Active Protein Extraction Kit (Sangon Biotech, C500026), and the concentration of proteins was calculated via the Bradford method. Then 1 mg of total protein was used for measuring catalase activity using Catalase (CAT) assay kit (Nanjing Jiancheng Bioengineering Institute, A007-1-1). For the growth rate measurement, the yeast lines expressing the above-mentioned pESC vectors were incubated in liquid YNB-His−/Gal 2% medium at 30 °C till OD600 reached 0.1. Then H2O2 was added to the liquid medium to the final H2O2 concentration of 3 mM. The yeast lines were continued to incubate in the liquid medium with and without H2O2, and the OD600 values were recorded hourly. The curved lines based on OD600 values were drafted to mirror the growth rates.
Biological Assay
Catalase
Cloning Vectors
Peroxide, Hydrogen
Proteins
Yeast, Dried
Yeast Proteins
Human and yeast protein localizations were obtained from the respective project websites. For C-terminal whole-genome yeast tagging projects2 (link),94 (link), https://yeastgfp.yeastgenome.org/allOrfData.txt for S. cerevisiae and a custom web scraper from https://www2.riken.jp/SPD/01/01A01.html for S. pombe (both accessed December 2020). For the human antibody-based Human Protein Atlas/Cell Atlas project4 (link), https://www.proteinatlas.org/download/subcellular_location.tsv (accessed September 2020). Annotation terms were remapped to the most similar T. brucei structure for comparison (Supplementary Table 5 ).
Evidence for involvement in human genetic disease was determined from OMIM95 (link),96 (link), accessed November 2018. Entries per gene were mapped to Ensembl gene identifications (IDs) (using the OMIM-provided mapping), and Ensembl gene IDs were mapped to Uniprot protein IDs (using the Uniprot protein mapping). Proteins were taken as involved in disease if the parent gene was annotated as associated or statistically linked with disease, involved with a known molecular mechanism or involved along with multiple genes.
Evidence for involvement in human genetic disease was determined from OMIM95 (link),96 (link), accessed November 2018. Entries per gene were mapped to Ensembl gene identifications (IDs) (using the OMIM-provided mapping), and Ensembl gene IDs were mapped to Uniprot protein IDs (using the Uniprot protein mapping). Proteins were taken as involved in disease if the parent gene was annotated as associated or statistically linked with disease, involved with a known molecular mechanism or involved along with multiple genes.
BPIFA4P protein, human
Cells
Genes
Genome
Hereditary Diseases
Homo sapiens
Immunoglobulins
Parent
Proteins
Saccharomyces cerevisiae
Yeast Proteins
Total yeast protein extracts were prepared and analysed by western blotting according to standard procedures [77 ]. Different mouse monoclonal antibodies, at the dilutions recommended by the manufacturers, were used: anti-HA (Roche, Basel Switzerland), anti-Pgk1 (Invitrogen, Schwerte, Germany), and anti-GFP (Roche, Basel Switzerland). The Nhp2 protein was detected using a specific rabbit polyclonal antibody [78 (link)]. Secondary goat anti-mouse or anti-rabbit horseradish peroxidase conjugated antibodies (Bio-Rad, Hercules, CA, USA) were also used. Protein–antibody complexes were revealed with a chemiluminescence detection kit (Super-Signal West Pico, Pierce, Waltham, MA, USA).
Anti-Antibodies
Chemiluminescence
Goat
Horseradish Peroxidase
Immunoglobulins
Monoclonal Antibodies
Mus
NHP2 protein, human
Proteins
Rabbits
Technique, Dilution
Yeast Proteins
Top products related to «Yeast Proteins»
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Y-PER Yeast Protein Extraction Reagent is a solution designed to facilitate the extraction of proteins from yeast cells. It is a ready-to-use reagent that does not require additional components for the extraction process.
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Anti-GFP is a laboratory product that binds to green fluorescent protein (GFP). Its core function is to facilitate the detection and visualization of GFP-tagged proteins in experimental settings.
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The Yeast Protein Extraction Reagent is a laboratory product designed to efficiently extract proteins from yeast samples. It is a specialized solution formulated to lyse yeast cells and release their intracellular proteins, enabling further analysis and experimentation.
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Protease inhibitor cocktail is a laboratory reagent used to inhibit the activity of proteases, which are enzymes that break down proteins. It is commonly used in protein extraction and purification procedures to prevent protein degradation.
Sourced in United States, Japan, China
The Yeast Protocols Handbook is a comprehensive guide that provides detailed protocols and information for working with yeast. It covers a wide range of yeast-related techniques and procedures, including culturing, genetic manipulation, and analytical methods. The handbook serves as a valuable reference for researchers and scientists working in the field of yeast biology and biotechnology.
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Anti-Pgk1 is a laboratory reagent used for the detection and quantification of Pgk1 (Phosphoglycerate Kinase 1) protein in biological samples. Pgk1 is an important enzyme involved in the glycolytic pathway. The Anti-Pgk1 reagent is designed to specifically bind to and identify the Pgk1 protein, enabling researchers to study its expression and activity in various experimental settings.
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ACS grade lab equipment is a classification that meets the standards set by the American Chemical Society (ACS) for high-purity, analytical-grade reagents and solvents used in chemical laboratories. This equipment is designed to provide consistent, reliable, and high-quality performance for a variety of laboratory applications.
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Trypsin is a serine protease enzyme that is commonly used in cell culture and molecular biology applications. It functions by cleaving peptide bonds at the carboxyl side of arginine and lysine residues, which facilitates the dissociation of adherent cells from cell culture surfaces and the digestion of proteins.
Sourced in China
The Yeast Total Protein Extraction Kit is a laboratory tool designed to facilitate the extraction and isolation of total proteins from yeast samples. The kit provides a standardized protocol and reagents to efficiently lyse yeast cells and recover the complete protein complement for downstream analysis.
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The ImageQuant LAS 4000 is a laboratory imaging system designed for the detection and quantification of radioisotope-labeled or chemiluminescent samples. The system utilizes a charge-coupled device (CCD) camera and high-performance optics to capture images of various types of gels, membranes, and other samples. The ImageQuant LAS 4000 provides researchers with a versatile and sensitive tool for a range of imaging applications.
More about "Yeast Proteins"
Yeast proteins are the diverse set of proteins found in the eukaryotic microorganism Saccharomyces cerevisiae, commonly known as baker's or brewer's yeast.
These proteins play crucial roles in various cellular processes, metabolism, and gene expression, making them a valuable subject of study in scientific research.
Yeast is a widely used model organism due to its well-characterized genome, ease of genetic manipulation, and similarity to human cellular processes.
Yeast proteins are involved in a wide range of functions, including cell growth, division, signaling, transport, and stress response.
These proteins can be extracted using specialized reagents, such as the Y-PER Yeast Protein Extraction Reagent, which helps to efficiently isolate and purify yeast proteins for further analysis.
One important class of yeast proteins are those tagged with the Green Fluorescent Protein (GFP), which allows for the visualization and localization of specific proteins within the yeast cell.
Anti-GFP antibodies are commonly used to detect and immunoprecipitate these tagged proteins.
Additionally, the Yeast Protein Extraction Reagent and Protease Inhibitor Cocktail can be used to preserve the integrity of yeast proteins during extraction and purification.
The Yeast Protocols Handbook provides comprehensive guidance on various experimental techniques, including the isolation and characterization of yeast proteins.
Another key protein in yeast is Pgk1 (Phosphoglycerate kinase 1), which is often used as a loading control in Western blot analysis.
Anti-Pgk1 antibodies can be used to detect this abundant and constitutively expressed protein.
Yeast proteins can be further studied using techniques such as mass spectrometry, protein purification, and biochemical assays.
The Yeast Total Protein Extraction Kit and ACS grade trypsin can be utilized for the efficient extraction and digestion of yeast proteins prior to mass spectrometric analysis.
The ImageQuant LAS 4000 imaging system is a powerful tool for the quantitative analysis of yeast protein expression and modification.
By leveraging the wealth of knowledge and resources available for yeast protein research, scientists can accelerate their discoveries and gain valuable insights into the fundamental mechanisms that govern eukaryotic cellular processes.
These proteins play crucial roles in various cellular processes, metabolism, and gene expression, making them a valuable subject of study in scientific research.
Yeast is a widely used model organism due to its well-characterized genome, ease of genetic manipulation, and similarity to human cellular processes.
Yeast proteins are involved in a wide range of functions, including cell growth, division, signaling, transport, and stress response.
These proteins can be extracted using specialized reagents, such as the Y-PER Yeast Protein Extraction Reagent, which helps to efficiently isolate and purify yeast proteins for further analysis.
One important class of yeast proteins are those tagged with the Green Fluorescent Protein (GFP), which allows for the visualization and localization of specific proteins within the yeast cell.
Anti-GFP antibodies are commonly used to detect and immunoprecipitate these tagged proteins.
Additionally, the Yeast Protein Extraction Reagent and Protease Inhibitor Cocktail can be used to preserve the integrity of yeast proteins during extraction and purification.
The Yeast Protocols Handbook provides comprehensive guidance on various experimental techniques, including the isolation and characterization of yeast proteins.
Another key protein in yeast is Pgk1 (Phosphoglycerate kinase 1), which is often used as a loading control in Western blot analysis.
Anti-Pgk1 antibodies can be used to detect this abundant and constitutively expressed protein.
Yeast proteins can be further studied using techniques such as mass spectrometry, protein purification, and biochemical assays.
The Yeast Total Protein Extraction Kit and ACS grade trypsin can be utilized for the efficient extraction and digestion of yeast proteins prior to mass spectrometric analysis.
The ImageQuant LAS 4000 imaging system is a powerful tool for the quantitative analysis of yeast protein expression and modification.
By leveraging the wealth of knowledge and resources available for yeast protein research, scientists can accelerate their discoveries and gain valuable insights into the fundamental mechanisms that govern eukaryotic cellular processes.