The Computed Atlas of Surface Topography of proteins (CASTp) server uses the alpha shape method (4 ) developed in computational geometry to identify topographic features, to measure area and volume and to compute imprint (5–8 (link)). The alpha shape method has also been applied in other studies of cavities and channels in protein structures (9 (link),10 (link)). The secondary structures are calculated using DSSP (11 (link)). Residue annotations of proteins are obtained from UniProt database (12 (link)) and mapped to PDB structures with residue-level information from the SIFTS database (13 (link)). The biological assemblies are extracted from the .mmicf files of the PDB database. Only the assemblies with biological significance and designated by the authors of the PDB structures (http://mmcif.wwpdb.org ) are processed and listed on the CASTp server.
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Membrane Proteins
Membrane Proteins
Membrane Proteins: Integral or peripheral proteins that are part of, or associated with, the membrane structure of a cell.
They play critical roles in a wide range of biological processes, including cell signaling, transport, and adhesion.
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They play critical roles in a wide range of biological processes, including cell signaling, transport, and adhesion.
Unlock insights into this fascinating class of biomolecules with PubCompare.ai's AI-driven research protocol optimization.
Locate protocols from literature, pre-prits, and patents, and use AI-driven comparisons to identify the best protocols and products for your Membrane Protein studies.
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Most cited protocols related to «Membrane Proteins»
Biopharmaceuticals
Dental Caries
Imprinting (Psychology)
Membrane Proteins
Protein Annotation
Proteins
SwissDock is based on the docking software EADock DSS (20 ). Its algorithm consists of the following steps. First, a large number of BMs (typically from 5000 to 15 000) are generated, either in a user-defined box (local docking) or in the vicinity of the target cavities of the entire protein surface (blind docking). Simultaneously, their CHARMM (26 (link)) energies are estimated on a grid. Then, BMs with the most favorable energies are ranked, taking account of the solvent effect using the FACTS implicit solvation model (27 (link)), and clustered. Finally, the most favorable clusters are dumped into the result file. This unique combination of features allows accurate docking assays to be carried out within minutes.
Biological Assay
Dental Caries
Membrane Proteins
Protein Targeting, Cellular
Solvents
Visually Impaired Persons
The WNN procedure begins by first applying standard analytical workflows to each modality independently and constructing KNN graphs for each one. In this manuscript we analyze data falling into three categories: measurements of single-cell gene expression, single-cell surface protein expression, and single-cell chromatin accessibility (ATAC-seq). For most analyses in this manuscript, we use a default value of k = 20, which is also the default value of k in the standard Seurat clustering workflow. For the analysis of the multimodal PBMC atlas, due to the substantial size of the dataset, we used a value of k = 30. In Figure S2 , we show that we obtain very similar results from the WNN procedure when varying k across a series of values ranging from 10 to 50.
For clarity, we overview the analytical workflows for each data type below:
For clarity, we overview the analytical workflows for each data type below:
Single-cell gene expression: We analyze scRNA-seq data using standard pipelines in Seurat which include normalization, feature selection, and dimensional reduction with PCA. We then construct a KNN graph after dimensional reduction.
Single-cell cell surface protein level expression: We analyze single-cell protein data (representing the quantification of antibody-derived tags (ADTs) in CITE-seq or ASAP-seq data) using a similar workflow to scRNA-seq. We normalize protein expression levels within a cell using the centered-log ratio (CLR) transform, followed by dimensional reduction with PCA, and subsequently construct a KNN graph. Unless otherwise specified, we do not perform feature selection on protein data, and use all measured proteins during dimensional reduction.
Single-cell chromatin accessibility: We analyze single-cell ATAC-seq data using our previously described workflow (Stuart et al., 2019 (link)), as implemented in the Signac package. We reduced the dimensionality of the scATAC-seq data by performing latent semantic indexing (LSI) on the scATAC-seq peak matrix, as suggested by Cusanovich et al. (2018) (link). We first computed the term frequency-inverse document frequency (TF-IDF) of the peak matrix by dividing the accessibility of each peak in each cell by the total accessibility in the cell (the “term frequency”), and multiplied this by the inverse accessibility of the peak in the cell population. This step ‘upweights’ the contribution of highly variable peaks and down-weights peaks that are accessible in all cells. We then multiplied these values by 10,000 and log-transformed this TF-IDF matrix, adding a pseudocount of 1 to avoid computing the log of 0. We decomposed the TF-IDF matrix via SVD to return LSI components, and scaled LSI loadings for each LSI component to mean 0 and standard deviation 1.
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ATAC-Seq
Cell Membrane Proteins
Cells
Chromatin
Gene Expression
Immunoglobulins
Membrane Proteins
Multimodal Imaging
Protein Domain
Proteins
Single-Cell RNA-Seq
Staphylococcal Protein A
To account for proteins targeted to some of the common bacterial hyperstructures and host-destined SCLs, new subcategory localizations have been introduced in PSORTb 3.0, as listed in Table 1 . This represents, to our knowledge, the first implementation of subcategories for primary SCL localizations, for an SCL predictor. These subcategory localizations for a protein were identified using the SCL-BLAST module, which infers localization by homology using criteria that are of measured high precision (Nair and Rost, 2002 (link)). Proteins detected to have a secondary localization are also predicted as one of the four main categories for Gram-positive bacteria or one of five main compartments for Gram-negative bacteria (or similarly for those bacteria with atypical cell structures). Any protein exported past the outer-most layer of the bacterial cell is considered as extracellular, whereas proteins localized to one of the membranes that are part of a hyperstructure (such as the flagellum) are identified both as an inner or outer membrane protein as well as a protein of that hyperstructure. The basal components of the flagellum are not annotated as such, since they are often homologous to proteins that are not part of the flagellar apparatus (for example, a general ATPase).
New subcategory SCLs predicted by PSORTb 3.0
| SCL subcategories | Description |
|---|---|
| Host-associated | Any proteins destined to the host cell cytoplasm, cell membrane or nucleus by any of the bacterial secretion systems |
| Type III secretion | Components of the Type III secretion apparatus |
| Fimbrial | Components of a bacterial or archaeal fimbrium or pilus |
| Flagellar | Components of a bacterial or archaeal flagellum |
| Spore | Components of a spore |
Adenosine Triphosphatases
Archaea
Bacteria
Bacterial Fimbria
Cell Nucleus
Cells
Cellular Structures
Cytoplasm
Flagella
Gram-Positive Bacteria
Gram Negative Bacteria
Membrane Proteins
Plasma Membrane
Proteins
secretion
Spores
Staphylococcal Protein A
Tissue, Membrane
The implementation of the new version of PSORTb is similar to version 2.0 (Gardy et al., 2005 (link)), with the following changes: motifs that provided false prediction results were either updated or removed. SCL-BLASTdbs for both Gram-positive and Gram-negative options were updated with the newly expanded dataset. The transmembrane α-helix predictor module HMMTOP (Tusnady and Simon, 2001 (link)) was replaced with S-TMHMM, an open source transmembrane α-helix predictor (Viklund and Elofsson, 2004 (link)). The program was modified such that the software reports the number of helices predicted. As with the PSORTb 2.0 set-up, this module first examined if an alpha helix was predicted in the first 70 amino acid residues; if so, this helix would be subtracted. It then examined the rest of the protein sequence, returning a positive prediction if more than two helices were found, to ensure high precision. Although this leads to membrane-associated proteins being under-predicted by this module, such proteins are instead predicted by the SCL-BLAST module and SVMs (mentioned below).
All SVMs, except for the Gram-negative outer membrane SVM module and Gram-positive cytoplasmic SVM module, were retrained with the new dataset following the protocols of PSORTb 2.0 paper (Gardy et al., 2005 (link)). The aforementioned two SVMs were not updated because the new SVMs did not improve significantly in performance when retrained. For PSORTb 2.0, we made use of an implementation of generalized suffix tree (Wang et al., 1994 ) to extract frequent subsequences, which occur in more than a predefined fraction of total number of proteins of interest. These frequent subsequences were used as features to discriminate localizations of related proteins. The implementation first sampled a subset of related proteins, then extracted frequent subsequences from this subset and finally checked whether these frequent subsequences were frequent in all related proteins. This method may miss some frequent subsequences or produce false positives. To overcome this issue, we used another augmentation of generalized suffix tree (Matias et al., 1998 ). The algorithm guarantees returning all the frequent subsequences and its running time is in the order of the total length of the related protein sequences.
A Bayesian network combines all module predictions and generates one final localization result based on the performance accuracies of each of the updated modules.
All SVMs, except for the Gram-negative outer membrane SVM module and Gram-positive cytoplasmic SVM module, were retrained with the new dataset following the protocols of PSORTb 2.0 paper (Gardy et al., 2005 (link)). The aforementioned two SVMs were not updated because the new SVMs did not improve significantly in performance when retrained. For PSORTb 2.0, we made use of an implementation of generalized suffix tree (Wang et al., 1994 ) to extract frequent subsequences, which occur in more than a predefined fraction of total number of proteins of interest. These frequent subsequences were used as features to discriminate localizations of related proteins. The implementation first sampled a subset of related proteins, then extracted frequent subsequences from this subset and finally checked whether these frequent subsequences were frequent in all related proteins. This method may miss some frequent subsequences or produce false positives. To overcome this issue, we used another augmentation of generalized suffix tree (Matias et al., 1998 ). The algorithm guarantees returning all the frequent subsequences and its running time is in the order of the total length of the related protein sequences.
A Bayesian network combines all module predictions and generates one final localization result based on the performance accuracies of each of the updated modules.
Amino Acids
Amino Acid Sequence
Cytoplasm
Helix (Snails)
Membrane Proteins
Proteins
Staphylococcal Protein A
Tissue, Membrane
Trees
Most recents protocols related to «Membrane Proteins»
Example 6
TbpB and NMB0313 genes were amplified from the genome of Neisseria meningitidis serotype B strain B16B6. The LbpB gene was amplified from Neisseria meningitidis serotype B strain MC58. Full length TbpB was inserted into Multiple Cloning Site 2 of pETDuet using restriction free cloning ((F van den Ent, J. Löwe, Journal of Biochemical and Biophysical Methods (Jan. 1, 2006)).). NMB0313 was inserted into pET26, where the native signal peptide was replaced by that of pelB. Mutations and truncations were performed on these vectors using site directed mutagenesis and restriction free cloning, respectively. Pairs of vectors were transformed into E. coli C43 and were grown overnight in LB agar plates supplemented with kanamycin (50 μg/mL) and ampicillin (100 μg/mL).
tbpB genes were amplified from the genomes of M. catarrhalis strain 035E and H. influenzae strain 86-028NP and cloned into the pET52b plasmid by restriction free cloning as above. The corresponding SLAMs (M. catarrhalis SLAM 1, H. influenzae SLAM1) were inserted into pET26b also using restriction free cloning. A 6His-tag was inserted between the pelB and the mature SLAM sequences as above. Vectors were transformed into E. coli C43 as above.
Cells were harvested by centrifugation at 4000 g and were twice washed with 1 mL PBS to remove any remaining growth media. Cells were then incubated with either 0.05-0.1 mg/mL biotinylated human transferrin (Sigma-aldrich T3915-5 MG), α-TbpB (1:200 dilution from rabbit serum for M. catarrhalis and H. influenzae; 1:10000 dilution from rabbit serum for N. meningitidis), or α-LbpB (1:10000 dilution from rabbit serum-obtained a gift from J. Lemieux) or α-fHbp (1:5000 dilution from mouse, a gift from D. Granoff) for 1.5 hours at 4° C., followed by two washes with 1 mL of PBS. The cells were then incubated with R-Phycoerythrin-conjugated Streptavidin (0.5 mg/ml Cedarlane) or R-phycoerythrin conjugated Anti-rabbit IgG (Stock 0.5 mg/ml Rockland) at 25 ug/mL for 1.5 hours at 4° C. The cells were then washed with 1 mL PBS and resuspended in 200 uL fixing solution (PBS+2% formaldehyde) and left for 20 minutes. Finally, cells were washed with 2×1 mL PBS and transferred to 5 mL polystyrene FACS tubes. The PE fluorescence of each sample was measured for PE fluorescence using a Becton Dickinson FACSCalibur. The results were analyzed using FLOWJO software and were presented as mean fluorescence intensity (MFI) for each sample. For N. meningtidis experiments, all samples were compared to wildtype strains by normalizing wildtype fluorescent signals to 100%. Errors bars represent the standard error of the mean (SEM) across three experiments. Results were plotted statistically analysed using GraphPad Prism 5 software. The results shown in
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ADRB2 protein, human
Agar
Ampicillin
anti-IgG
Cells
Centrifugation
Cloning Vectors
Culture Media
Escherichia coli
Fluorescence
Formaldehyde
Genes
Genome
Haemophilus influenzae
Homo sapiens
Kanamycin
Lipoproteins
Membrane Proteins
Moraxella catarrhalis
Mus
Mutagenesis, Site-Directed
Mutation
Neisseria
Neisseria meningitidis
Phycoerythrin
Plasmids
Polypeptides
Polystyrenes
prisma
Rabbits
Serum
Signaling Lymphocytic Activation Molecule Family Member 1
Signal Peptides
Strains
Streptavidin
Technique, Dilution
Transferrin
Translocation, Chromosomal
Virulence Factors
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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Antigens
Antigens, Bacterial
Bacterial Proteins
Epitopes
Filtration
Iron
Klebsiella pneumoniae
Membrane Proteins
OmpC protein
Proteins
Proteome
Pseudomonas aeruginosa
Tissue, Membrane
vaccin
Cell surface biotinylation was conducted as described (Maupin et al., 2022 (link); Shiratori et al., 2018 (link)). In brief, cells were rinsed with PBS and surface proteins were biotinylated by incubating cells with 0.25 mg/ml EZ-Link Sulfo-NHS-SS-Biotin (A39258; Thermo Fisher Scientific) in PBS for 30 min with horizontal motion at 4°C. After labeling, plates were washed with quenching buffer (PBS containing 100 mM glycine) for 20 min at 4°C, then rinsed once with PBS. Cells were then lysed in IP Lysis Buffer and lysates were cleared by centrifugation. Cell lysates of equivalent amounts of protein were equilibrated overnight with Pierce Streptavidin Magnetic Beads (#88817; Thermo Fisher Scientific) at 4°C. After repeated washing steps, biotinylated proteins were released from the beads by heating to 95°C with 2× Laemmli buffer, separated by SDS-PAGE, and analyzed by Western blotting.
Biotinylation
Buffers
Cells
Centrifugation
Glycine
Laemmli buffer
Membrane Proteins
Proteins
SDS-PAGE
Streptavidin
sulfosuccinimidyl-2-(biotinamido)ethyl-1,3-dithiopropionate
For subcellular fractionation, a modified version of the protocol described by Wang et al.43 was used. P. aeruginosa strains were grown in LB medium overnight. The cultures were inoculated into fresh LB with the OD600 adjusted to 0.05 and subcultivated at 37 °C with shaking to an OD600 = 1.5. 10 ml of each strain were harvested by centrifugation at 4500 × g for 10 min. The cell pellets were resuspended in 500 µl sucrose-EDTA solution (2.5 mM EDTA and 20% (w/v) sucrose in PBS, pH 7.3) and incubated at room temperature for 20 min. In total, 500 µl ice-cold H2O were added and the samples were incubated for 5 min at 4 °C with gentle shaking (550 rpm). After centrifugation for 20 min at 4 °C and 7000 × g, the supernatant containing all periplasmic proteins was removed and filtered through a syringe filter with 0.2 µm pore size. To obtain the cytosolic and membrane-bound proteins, the pellets were resuspended in 375 µl H2O and 125 µl 4x Laemmli solution with 10% β-mercaptoethanol and then incubated for 10 min at 95 °C.
To obtain the periplasmic proteins, 250 µl of 14.3% aqueous trichloroacetic acid solution (w/v in H2O) were added to 1 ml of the supernatants containing the periplasmic proteins and incubated on ice for 30 min. After centrifugation for 5 min at 4 °C and 14,000 × g, the supernatant was discarded. The pellets were washed twice by adding 400 µl acetone, centrifuging at 14,000 × g and 4 °C for 5 min and discarding the acetone. The pellets were dried at 95 °C for 1 min, then resuspended in 36 µl H2O. 12 µl 4x Laemmli buffer with 10% β-mercaptoethanol were added and the samples incubated for 10 min at 95 °C prior to loading on an SDS polyacrylamide gel.
To obtain the periplasmic proteins, 250 µl of 14.3% aqueous trichloroacetic acid solution (w/v in H2O) were added to 1 ml of the supernatants containing the periplasmic proteins and incubated on ice for 30 min. After centrifugation for 5 min at 4 °C and 14,000 × g, the supernatant was discarded. The pellets were washed twice by adding 400 µl acetone, centrifuging at 14,000 × g and 4 °C for 5 min and discarding the acetone. The pellets were dried at 95 °C for 1 min, then resuspended in 36 µl H2O. 12 µl 4x Laemmli buffer with 10% β-mercaptoethanol were added and the samples incubated for 10 min at 95 °C prior to loading on an SDS polyacrylamide gel.
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2-Mercaptoethanol
Acetone
Cells
Centrifugation
Cold Temperature
Cytosol
Edetic Acid
Fractionation, Chemical
Laemmli buffer
Membrane Proteins
Pellets, Drug
Periplasmic Proteins
polyacrylamide gels
Pseudomonas aeruginosa
Sucrose
Syringes
Trichloroacetic Acid
Liver tissues were lysed using RIPA lysis buffer and centrifuged at 16,000g for 15 min. LX-2 cells were lysed in RIPA buffer (Cell Signaling Technology, Beverly, MA, USA) containing a cocktail of protease inhibitors (Calbiochem part of Merck KGaA, Darmstadt, Germany) on ice. The supernatants were then obtained after centrifugation at 16,200g for 15 min at 4℃. The protein concentrations of the cell lysates or liver tissues were measured by Bradford assay (Pro-Measure, iNtRON Biotechnology, Seoul, Korea) and the proteins were dissolved in the SDS sample buffer. Western blot analysis was conducted as previously described (Song et al. 2016 (link)). Samples were separated using sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). The separated proteins were transferred to nitrocellulose membranes (GE Healthcare, Madison, WI, USA) and blocked with 5% skim milk. The proteins on the membrane were incubated with the primary antibodies (Supplementary Table 1) overnight. Horseradish peroxidase-conjugated IgG antibodies (Cell Signaling Technology, Beverly, MA, USA) were used as the secondary antibodies. After 2 h incubation with the secondary antibody, immune complexes were detected using the Immobilon Western Chemiluminescent HRP Substrate (Merck Millipore, Billerica, MA, USA).
Antibodies
Biological Assay
Buffers
Centrifugation
Complex, Immune
Immobilon
Immunoglobulins
Introns
Liver
Membrane Proteins
Milk, Cow's
Nitrocellulose
Protease Inhibitors
Proteins
Radioimmunoprecipitation Assay
SDS-PAGE
Tissue, Membrane
Tissues
Western Blot
Top products related to «Membrane Proteins»
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PVDF membranes are a type of laboratory equipment used for a variety of applications. They are made from polyvinylidene fluoride (PVDF), a durable and chemically resistant material. PVDF membranes are known for their high mechanical strength, thermal stability, and resistance to a wide range of chemicals. They are commonly used in various filtration, separation, and analysis processes in scientific and research settings.
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The Mem-PER Plus Membrane Protein Extraction Kit is a laboratory product designed to isolate and purify membrane proteins from various cell and tissue samples. The kit utilizes a detergent-based approach to extract and separate membrane proteins from other cellular components.
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The BCA protein assay kit is a colorimetric-based method for the quantitative determination of total protein concentration in a sample. It uses bicinchoninic acid (BCA) to detect and quantify the presence of protein.
Sourced in China, United States, Switzerland, Germany, Japan, United Kingdom
RIPA lysis buffer is a detergent-based buffer solution designed for the extraction and solubilization of proteins from cells and tissues. It contains a mixture of ionic and non-ionic detergents that disrupt cell membranes and solubilize cellular proteins. The buffer also includes additional components that help to maintain the stability and activity of the extracted proteins.
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The Protease Inhibitor Cocktail is a laboratory product designed to inhibit the activity of proteases, which are enzymes that can degrade proteins. It is a combination of various chemical compounds that work to prevent the breakdown of proteins in biological samples, allowing for more accurate analysis and preservation of protein integrity.
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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.
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The Pierce BCA Protein Assay Kit is a colorimetric-based method for the quantification of total protein in a sample. It utilizes the bicinchoninic acid (BCA) reaction, where proteins reduce Cu2+ to Cu+ in an alkaline environment, and the resulting purple-colored reaction is measured spectrophotometrically.
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The BCA Protein Assay Kit is a colorimetric detection and quantification method for total protein concentration. It utilizes bicinchoninic acid (BCA) for the colorimetric detection and quantification of total protein. The assay is based on the reduction of Cu2+ to Cu1+ by protein in an alkaline medium, with the chelation of BCA with the Cu1+ ion resulting in a purple-colored reaction product that exhibits a strong absorbance at 562 nm, which is proportional to the amount of protein present in the sample.
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The Plasma Membrane Protein Extraction Kit is designed for the isolation of plasma membrane proteins from various cell types. It utilizes a series of centrifugation steps to fractionate cellular components and enrich for plasma membrane proteins.
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RIPA buffer is a detergent-based cell lysis and extraction reagent. It is used to extract and solubilize proteins from cells and tissues for analysis. The buffer contains a combination of ionic and non-ionic detergents, as well as other components that aid in the solubilization and stabilization of proteins.
More about "Membrane Proteins"
Membrane proteins are a diverse class of biomolecules that are crucial for a wide range of biological processes.
These integral or peripheral proteins are embedded in or associated with the cell membrane, playing vital roles in signaling, transport, and adhesion.
Unlocking the secrets of membrane proteins can provide valuable insights into cellular function and disease mechanisms.
PubCompare.ai's AI-driven research protocol optimization can help researchers identify the best tools and techniques for studying these fascinating biomolecules.
Explore a range of related topics, including PVDF membranes for protein transfer, the Mem-PER Plus Membrane Protein Extraction Kit for isolating membrane proteins, the BCA protein assay kit for quantifying protein concentrations, and RIPA lysis buffer with protease inhibitor cocktail for extracting and preserving membrane proteins.
The Pierce BCA Protein Assay Kit and Plasma Membrane Protein Extraction Kit are also valuable tools for membrane protein research.
By harnessing the power of PubCompare.ai, researchers can optimize their workflow, locate the best protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the most effective solutions for their membrane protein studies.
Discover the true potential of your research and take it to the next level with PubCompare.ai.
These integral or peripheral proteins are embedded in or associated with the cell membrane, playing vital roles in signaling, transport, and adhesion.
Unlocking the secrets of membrane proteins can provide valuable insights into cellular function and disease mechanisms.
PubCompare.ai's AI-driven research protocol optimization can help researchers identify the best tools and techniques for studying these fascinating biomolecules.
Explore a range of related topics, including PVDF membranes for protein transfer, the Mem-PER Plus Membrane Protein Extraction Kit for isolating membrane proteins, the BCA protein assay kit for quantifying protein concentrations, and RIPA lysis buffer with protease inhibitor cocktail for extracting and preserving membrane proteins.
The Pierce BCA Protein Assay Kit and Plasma Membrane Protein Extraction Kit are also valuable tools for membrane protein research.
By harnessing the power of PubCompare.ai, researchers can optimize their workflow, locate the best protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the most effective solutions for their membrane protein studies.
Discover the true potential of your research and take it to the next level with PubCompare.ai.