We performed a laboratory analysis to construct an experimental dataset of proteins from a Gram-negative bacterium, Pseudomonas aeruginosa PA01, which was used to assess PSORTb 2.0, PSORTb 3.0, PA 2.5 and PA 3.0. This represents an independent dataset that includes hypothetical and uncharacterized proteins with previously unknown SCLs. P.aeruginosa is a bacterium noted for its diverse metabolic capacity and large genome/proteome size, and so represents an excellent organism with which to generate such a dataset (Stover et al., 2000 (link)). To generate this experimental dataset, we extracted protein samples from the cytoplasmic, periplasmic and secreted fractions of P.aeruginosa PA01. The resulting proteins in each fraction were digested to peptides and differentially labeled using formaldehyde isotopologues (Chan and Foster, 2008 (link)) prior to analysis by liquid chromatography–tandem mass spectrometry (LC–MS/MS), exactly as previously described (Chan et al., 2006 (link)). Abundance ratios between SCL were calculated using MSQuant (http://msquant.sourceforge.net/ ). To ensure a high-quality dataset with minimal contaminating proteins from other subcellular compartments, proteins that were only found in the cytoplasmic fraction and never in the other two soluble fractions were used to assess PSORTb 3.0 and PA 3.0 prediction results. This dataset was also felt to be most appropriate for assessment, since our analysis had suggested that most proteins of previously unknown localization in the old version of PSORTb were most likely cytoplasmic proteins. Further details on the experimental protocols for this proteomics analysis of the subcellular fractions can be found in Supplementary Material—methods for mass spectrometry protein identification.
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Anatomy
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Cell Component
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Periplasm
Periplasm
The periplasm is the compartment between the inner and outer membranes of Gram-negative bacteria.
This space contains a peptidoglycan layer and various proteins involved in processes such as nutrient transport, cell signaling, and stress response.
Understanding the periplasm is crucial for optimizing research on bacterial physiology and the development of antimicrobial therapies.
PubCompare.ai can help researchers explore protocols and find the most reproducible methods for studying this important bacterial structure.
This space contains a peptidoglycan layer and various proteins involved in processes such as nutrient transport, cell signaling, and stress response.
Understanding the periplasm is crucial for optimizing research on bacterial physiology and the development of antimicrobial therapies.
PubCompare.ai can help researchers explore protocols and find the most reproducible methods for studying this important bacterial structure.
Most cited protocols related to «Periplasm»
Bacteria
Cytoplasm
Feelings
Formaldehyde
Gram Negative Bacteria
Liquid Chromatography
Mass Spectrometry
Peptides
Periplasm
Proteins
Proteome
Proto-Oncogene Mas
Pseudomonas aeruginosa
Spectrometry
Staphylococcal Protein A
Subcellular Fractions
Tandem Mass Spectrometry
To complement incomplete annotations in the background database, a homology-ontology annotation retrieved by BLAST should be accompanied by an accurate subcellular localization prediction for each homologous sequence. CELLO has been shown to be helpful for the prediction of subcellular localizations of the proteins found in a proteomic data. [28] (link) Using multiple, integrated machine-learned classifiers, CELLO predicts which of four subcellular localizations in archaea and in Gram-positive bacteria, five subcellular localizations in Gram-negative bacteria, and twelve subcellular localizations in eukaryotes that the targeted protein might be found in, with the four archaeal and Gram-positive bacterial localizations being the extracellular space, the cell wall, the cytoplasmic membrane, and the cytoplasm; the five Gram-positive bacterial localizations being the extracellular space, the outer membrane, the periplasmic and cytoplasmic (inner) membranes, and the cytoplasm; and the 12 eukaryotic localizations being chloroplasts, the cytoplasm, the cytoskeleton, the endoplasmic reticulum, the extracellular/secretory space, the Golgi, lysosomes, mitochondria, the nucleus, peroxisomes, the plasma membrane, and vacuoles. Due to subcellular data increased exponentially over the years, CELLO has been trained on latest models and denoted as update version wrapping in CELLO2GO. And the resultant datasets used for prediction and evaluation is from PSORTb3.0 [23] (link).
Archaea
Cell Nucleus
Cell Wall
Chloroplasts
Cytoplasm
Cytoskeleton
Endoplasmic Reticulum
Eukaryota
Eukaryotic Cells
Extracellular Space
Golgi Apparatus
Gram-Positive Bacteria
Gram Negative Bacteria
Homologous Sequences
Lysosomes
Mitochondria
Periplasm
Peroxisome
Plasma Membrane
Proteins
secretion
Tissue, Membrane
Vacuole
The ΔrG′m calculated for all reactions contained in the reconstruction is based on the reference state of 1 mM concentrations for all species except H+, water, H2 and O2. The reference concentrations for H2 and O2 are the saturation concentrations for these species in water at 1 atm and 298.15 K. All ΔrG′m values reported in this work also include the energy contribution of the transmembrane electrochemical potential and proton gradient for all reactions involving transport across the cytoplasmic membrane assuming a periplasmic pH of 7.7 and a cytoplasmic pH of 7.2. All ΔrG′m calculated for reactions in the iAF1260 model are listed in Supplementary information .
We also determined the direction of flux required in the reactions contained in iAF1260 to achieve near optimal growth (90–100%) on each of 174 carbon sources using FVA (Mahadevan and Schilling, 2003 (link)) and the BOFCORE. It is worthwhile to note that the same set of reactions can or cannot be utilized in FVA simulations when examining approximately 5–95% of the optimal flux value achievable for the BOFCORE under glucose aerobic conditions (one exception is the cytochrome oxidase bo and oxygen transport reactions, which are needed for generating the necessary energy to achieve approximately 80% or greater of the BOFCORE flux). During the FVA of conditions corresponding to glucose aerobic growth, the reactions CAT, SPODM and SPODMpp were constrained to zero to prevent generation of cellular energy equivalents through reactions involved in E. coli's response to oxidative stress, and the reaction formate hydrogenlyase, which appears to be involved in regulating cytosolic pH (Mnatsakanyan et al, 2004 (link)), was also constrained to zero to prevent the production of significant amounts of hydrogen gas that is not typically observed for most buffered experiments around pH 7. The results of the FVA indicated that some of the reactions in the reconstruction consistently operated in the reverse direction. During the calculation of ΔrG′m for these reactions, the forward direction of each reaction was redefined to be in the direction of flux required for near optimal growth to occur. Because of this adjustment, all negative ΔrG′m and ΔrG′ values reported (seeFigure 2 ) indicate reactions that are thermodynamically feasible in the direction of flux while positive values indicate thermodynamically infeasible reactions.
We also determined the direction of flux required in the reactions contained in iAF1260 to achieve near optimal growth (90–100%) on each of 174 carbon sources using FVA (Mahadevan and Schilling, 2003 (link)) and the BOFCORE. It is worthwhile to note that the same set of reactions can or cannot be utilized in FVA simulations when examining approximately 5–95% of the optimal flux value achievable for the BOFCORE under glucose aerobic conditions (one exception is the cytochrome oxidase bo and oxygen transport reactions, which are needed for generating the necessary energy to achieve approximately 80% or greater of the BOFCORE flux). During the FVA of conditions corresponding to glucose aerobic growth, the reactions CAT, SPODM and SPODMpp were constrained to zero to prevent generation of cellular energy equivalents through reactions involved in E. coli's response to oxidative stress, and the reaction formate hydrogenlyase, which appears to be involved in regulating cytosolic pH (Mnatsakanyan et al, 2004 (link)), was also constrained to zero to prevent the production of significant amounts of hydrogen gas that is not typically observed for most buffered experiments around pH 7. The results of the FVA indicated that some of the reactions in the reconstruction consistently operated in the reverse direction. During the calculation of ΔrG′m for these reactions, the forward direction of each reaction was redefined to be in the direction of flux required for near optimal growth to occur. Because of this adjustment, all negative ΔrG′m and ΔrG′ values reported (see
Bacteria, Aerobic
Carbon
Cells
Cytoplasm
Cytosol
formate hydrogenlyase
Glucose
Hydrogen
Membrane Potentials
Oxidase, Cytochrome-c
Oxidative Stress
Oxygen
Periplasm
Plasma Membrane
Protons
Reconstructive Surgical Procedures
The SCL predictor for archaea was implemented with similar components as the Gram-positive predictor, producing predictions for four localizations and two subcategory localizations (flagellum and fimbrium), but using the archaeal training dataset mentioned above. Any motifs that reduced the precision for archaeal SCL prediction were removed.
Two other categories were implemented for bacteria with atypical cellular structures–organisms that stain Gram-positive but have an outer membrane, and organisms that stain Gram-negative but have no outer membrane. For the former category, the Gram-negative pipeline was employed, which enables outer membrane and periplasmic localizations to be predicted. For the latter category, the Gram-positive modules were used, but the cell wall localization prediction was disabled, since the intended organisms (i.e. Tenericutes) lack cell walls.
Two other categories were implemented for bacteria with atypical cellular structures–organisms that stain Gram-positive but have an outer membrane, and organisms that stain Gram-negative but have no outer membrane. For the former category, the Gram-negative pipeline was employed, which enables outer membrane and periplasmic localizations to be predicted. For the latter category, the Gram-positive modules were used, but the cell wall localization prediction was disabled, since the intended organisms (i.e. Tenericutes) lack cell walls.
Archaea
Bacteria
Bacterial Fimbria
Cellular Structures
Cell Wall
Flagella
Periplasm
Stains
Tenericutes
Tissue, Membrane
The starting reconstructions, AJRecon and BRecon, were built on scaffolds derived from published E. coli MRS. AJRecon is a pre-publication version of iRR1083 [20 (link)], and was based on iJR904 [26 (link)]. For its scaffold, BRecon (Bumann, unpublished) employed iAF1260 [27 (link)]- a direct descendent of iJR904. The two reconstructions, differ in content due to: (1) different components being targeted for manual curation (e.g., BRecon extended Fe chelation and AJRecon extended lipid production), and (2) differences in E. coli MRs that were used as comparative genomics scaffolds for initializing the Salmonella MRs (e.g., iAF1260 accounted for the periplasm whereas its ancestor did not).
Escherichia coli
Lipids
Periplasm
Reconstructive Surgical Procedures
Salmonella
Most recents protocols related to «Periplasm»
EXAMPLE 21
In order to determine PD-1 competition efficiency of B7-H1 binding Nanobodies, the positive clones of the binding assay were tested in an ELISA competition assay setup.
In short, 2 μg/ml B7-H1 ectodomain (rhB7H1-Fc, R&D Systems, Minneapolis, US, Cat #156-B7) was immobilized on maxisorp microtiter plates (Nunc, Wiesbaden, Germany) and free binding sites were blocked using 4% Marvel in PBS. Next, 0.5 μg/ml of PD-1-biotin was preincubated with 10 μl of periplasmic extract containing Nanobody of the different clones and a control with only PD-1-biotin (high control). The PD-1-biotin was allowed to bind to the immobilized ligand with or without Nanobody. After incubation and a wash step, PD-1 binding was revealed using a HRP-conjugated streptavidine. Binding specificity was determined based on OD values compared to controls having received no Nanobody (high control). OD values for the different Nanobody clones are depicted in
Binding Sites
Biological Assay
Biotin
biotin 1
CD274 protein, human
Clone Cells
Enzyme-Linked Immunosorbent Assay
Ligands
Periplasm
Psychological Inhibition
Test Preparation
VHH Immunoglobulin Fragments
Purification of hexahistidine‐tagged proteins was performed by standard immobilized metal affinity chromatography using HisPur Cobalt resin (Thermo Scientific) under native conditions. For 3 mL cultures from 24 DWP, IMAC was performed using 0.2 mL resin in small gravity feed columns. The resin was washed with 2 × 2 mL of water and equilibrated with 2 × 2 mL of 50 mM phosphate buffer (pH 7.4). Cell lysates on 24 DWP were cleared by centrifugation (3220g, 20 min, 4°C) and loaded onto the columns. The columns were rinsed with 2 mL of 50 mM phosphate buffer (pH 7.4), washed with 4 × 2 mL of wash buffer (50 mM sodium phosphate, 10 mM imidazole, 300 mM sodium chloride; pH 7.4), and then rinsed with 2 mL of 50 mM sodium phosphate (pH 7.4) before elution with 3 × 0.2 mL of 50 mM sodium phosphate, 50 mM EDTA (pH 7.4). For 10 mL cultures, the same protocol was used with the following changes: medium samples were 1:2 diluted (total volume 10 mL), periplasmic and cytoplasmic fractions were diluted in 2.5 mL of 200 mM sodium phosphate buffer and made up to 10 ml with water to reduce the salt concentration. Samples were prepared for SDS‐PAGE analysis and 10 μL were loaded in 4–20% Criterion™ TGX™ Precast Midi Protein Gel, 26 well (BioRad).
For the detection of proteins by WB analysis the method as detailed in Guerrero‐Montero, Dolata, et al. (2019 (link)) was performed, with the exception that was transferred to the polyvinylidene fluoride‐membrane (GE Healthcare) by rapid semi‐dry transfer using the Invitrogen Power Blotter XL System according to the manufacturer's instructions.
For the detection of proteins by WB analysis the method as detailed in Guerrero‐Montero, Dolata, et al. (2019 (link)) was performed, with the exception that was transferred to the polyvinylidene fluoride‐membrane (GE Healthcare) by rapid semi‐dry transfer using the Invitrogen Power Blotter XL System according to the manufacturer's instructions.
Buffers
Cells
Centrifugation
Chromatography, Affinity
Cobalt
Cytoplasm
Edetic Acid
Gravity
His-His-His-His-His-His
imidazole
imidazole-4-acetic acid
Metals
Periplasm
Phosphates
polyvinylidene fluoride
Proteins
Resins, Plant
SDS-PAGE
Sodium Chloride
sodium phosphate
Tissue, Membrane
For the fractionation of the cells, the PureFrac fractionation protocol was used (Malherbe et al., 2019 (link)). For purification, ethylenediamine tetraacetic acid (EDTA) was not added to any of the buffers. Apart from the periplasm and cytoplasm, medium samples were also recovered (same volume in all cultures). In ΔTat experiments, the 1× phosphate‐buffered saline wash of the cells and the separation of the cytoplasm and insoluble fraction was not carried out to avoid extra manipulation of this cell line due to its fragility. Samples were prepared for sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS‐PAGE) analysis in reducing conditions.
Cell Fractionation
Cell Lines
Cytoplasm
Edetic Acid
Periplasm
Phosphates
Radiotherapy Dose Fractionations
Saline Solution
SDS-PAGE
The AcrAtag acceptor protein is a modified and truncated version of the C. jejuni lipoprotein, AcrA (amino acids 61 to 211). Amino acids 98–113 and 151–165, which flank the native AcrA PglB glycosylation sequon at Asn123, were deleted as these have previously been shown to not be required for glycosylation of Asn123. Two additional internal sequons at Asn117 and Asn147 were introduced through mutation at Phe115Asp and Thr145Asp, respectively, and 4 glycosylation sequons were added as C-terminal glycotags, which were separated by glycine-arginine-glycine linkers. Mutations at amino acids Lys97Gln and Lys191Gln were made to reduce proteolytic cleavage. An N-terminal DsbA signal sequence was included for trafficking the protein to the periplasm and a C-terminal hexahistidine tag was included for protein purification. The “acrAtag” construct was ordered for synthesis as a g-block (IDT) and was inserted into pEC415 using Gibson assembly using primers acrAtag_pEC415_f and r and the pEC415 backbone amplified using pEC415_acrAtag f and r. Amplification using Phusion polymerase and Gibson assembly using the NEB HiFi kit were both performed according to the manufacturer’s instruction.
Amino Acids
Anabolism
Arginine
Cytokinesis
Glycine
His-His-His-His-His-His
Lipoproteins
Mutation
Oligonucleotide Primers
Periplasm
Protein Glycosylation
Proteins
Proteolysis
Signal Peptides
Vertebral Column
The remaining DNA samples used for sequencing in Section 2.3 were used for quantitative real-time PCR (q-PCR) analysis. The key genes, including narG (encoding the membrane-bound nitrate reductase), napA (encoding the periplasmic nitrate reductase), nirK (encoding the copper-containing nitrite reductase), nirS (encoding the haem-containing nitrite reductases), norB (encoding nitric oxide reductase), and nosZ (encoding nitrous oxide reductase), were further quantified by q-PCR using a ChamQ SYBR Color qPCR Master Mix (2X) with an ABI PRISM 7300 Sequence Detection System (Applied Biosystems, USA), and were conducted in triplicate in different experimental groups. Each PCR tube (20 μl) contained 10 μl 2X ChamQ SYBR Color qPCR Master Mix (Nanjing Novizan Biotechnology Co., LTD, China), 2 μl DNA, 0.8 μl each of forward and reverse primer, 0.4 μl ROX Reference Dye II (50×), and sterile ddH2O to a total volume of 20 μl. The primers used for the PCR amplification of each gene are listed in Table 2 .
Gene Amplification
Genes
Heme
Nitrate Reductase
Nitrates
nitric oxide reductase
Nitrite Reductase
nitrite reductase, copper-containing
nitrous oxide reductase
Oligonucleotide Primers
Periplasm
periplasmic oxidoreductase
prisma
Real-Time Polymerase Chain Reaction
Spectroscopy, Near-Infrared
Sterility, Reproductive
Tissue, Membrane
Top products related to «Periplasm»
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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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Ni-NTA beads are a type of agarose-based affinity resin used for the purification of recombinant proteins that contain a polyhistidine (His) tag. The Ni-NTA (Nickel-Nitrilotriacetic Acid) moiety on the beads binds to the His-tagged proteins, allowing them to be separated from other cellular components during the purification process.
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Ni-NTA agarose is a solid-phase affinity chromatography resin designed for the purification of recombinant proteins containing a histidine-tag. It consists of nickel-nitrilotriacetic acid (Ni-NTA) coupled to agarose beads, which selectively bind to the histidine-tagged proteins.
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The HiLoad 16/600 Superdex 75 column is a size exclusion chromatography column designed for the separation and purification of proteins and macromolecules. It has a bed volume of 120 ml and a fractionation range of 3,000 to 70,000 daltons. The column is made of borosilicate glass and is compatible with a variety of aqueous and organic solvents.
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ELISA (Enzyme-Linked Immunosorbent Assay) plates are a type of laboratory equipment used for performing enzyme-linked immunosorbent assays. These plates typically consist of a flat surface with multiple wells, allowing for the simultaneous analysis of multiple samples or conditions. The wells are designed to facilitate the binding of specific proteins or other molecules to the plate surface, enabling the detection and quantification of target analytes in the samples.
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The HisTrap HP column is a pre-packed chromatography column designed for the purification of recombinant proteins containing a histidine tag. The column is filled with a matrix that selectively binds to the histidine tag, allowing the target protein to be separated from other components in the sample.
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The Vmax Microplate Reader is a versatile laboratory instrument designed for high-performance absorbance measurements. It is capable of accurately and precisely measuring the optical density of samples in a microplate format.
The Peroxidase-conjugated anti-FLAG antibody is a laboratory reagent used for the detection and identification of proteins tagged with the FLAG peptide epitope. The antibody is conjugated to the enzyme horseradish peroxidase, which allows for colorimetric or chemiluminescent detection of the target protein in various applications, such as Western blotting, immunoprecipitation, and enzyme-linked immunosorbent assays (ELISA).
The Envision multiwell reader is a high-performance microplate reader designed for a wide range of applications in life science research and drug discovery. It offers sensitive detection across multiple detection modes, including absorbance, fluorescence, and luminescence. The Envision provides reliable and reproducible results, enabling researchers to conduct a variety of assays with confidence.
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Zeba Spin Desalting Columns are a size-exclusion chromatography product designed to quickly remove salts, buffers, and other small molecules from protein samples. The columns are pre-packed with a proprietary resin that efficiently separates proteins from small molecules based on their size difference. This allows for the effective desalting and buffer exchange of protein samples in a simple, rapid, and reproducible manner.
More about "Periplasm"
The periplasm is a critical compartment in Gram-negative bacteria, situated between the inner and outer membranes.
This space contains the peptidoglycan layer and various proteins involved in crucial processes like nutrient transport, cell signaling, and stress response.
Understanding the periplasm is essential for optimizing research on bacterial physiology and developing effective antimicrobial therapies.
Researchers can explore a wealth of protocols and methods for studying the periplasm using tools like PubCompare.ai.
This AI-driven platform helps scientists find the most reproducible and accurate approaches, drawing from a vast database of literature, pre-prints, and patents.
By comparing different techniques, PubCompare.ai guides researchers to the optimal procedures for periplasm analysis and optimization.
Techniques like the Pierce BCA Protein Assay Kit, Ni-NTA beads and agarose, HiLoad 16/600 Superdex 75 column, ELISA plates, HisTrap HP column, Vmax Microplate Reader, Peroxidase-conjugated anti-FLAG antibody, Envision multiwell reader, and Zeba Spin Desalting Columns can be invaluable for studying the periplasm and its components.
These tools, combined with the insights from PubCompare.ai, empower researchers to unlock the full potential of their periplasm research and drive progress in bacterial physiology and antimicrobial development.
Whether you're investigating nutrient transport, cell signaling, or stress response mechanisms in the periplasm, PubCompare.ai and a suite of specialized equipment can help you achieve your research goals with greater efficiency and accuracy.
Explore the power of this AI-driven platform and unleash your full potential in the world of periplasm optimization.
This space contains the peptidoglycan layer and various proteins involved in crucial processes like nutrient transport, cell signaling, and stress response.
Understanding the periplasm is essential for optimizing research on bacterial physiology and developing effective antimicrobial therapies.
Researchers can explore a wealth of protocols and methods for studying the periplasm using tools like PubCompare.ai.
This AI-driven platform helps scientists find the most reproducible and accurate approaches, drawing from a vast database of literature, pre-prints, and patents.
By comparing different techniques, PubCompare.ai guides researchers to the optimal procedures for periplasm analysis and optimization.
Techniques like the Pierce BCA Protein Assay Kit, Ni-NTA beads and agarose, HiLoad 16/600 Superdex 75 column, ELISA plates, HisTrap HP column, Vmax Microplate Reader, Peroxidase-conjugated anti-FLAG antibody, Envision multiwell reader, and Zeba Spin Desalting Columns can be invaluable for studying the periplasm and its components.
These tools, combined with the insights from PubCompare.ai, empower researchers to unlock the full potential of their periplasm research and drive progress in bacterial physiology and antimicrobial development.
Whether you're investigating nutrient transport, cell signaling, or stress response mechanisms in the periplasm, PubCompare.ai and a suite of specialized equipment can help you achieve your research goals with greater efficiency and accuracy.
Explore the power of this AI-driven platform and unleash your full potential in the world of periplasm optimization.