Mass spectrometric data were collected on an Orbitrap Fusion Lumos mass spectrometer in-line with a Proxeon NanoLC-1200 UHPLC. The 100 μm capillary column was packed with 35 cm of Accucore 150 resin (2.6 μm, 150Å; ThermoFisher Scientific). Spectra were converted to mzXML using a modified version of ReAdW.exe. Database searching included all entries from the Saccharomyces Genome Database (SGD; August 21, 2017). This database was concatenated with one composed of all protein sequences in the reversed order. Searches were performed using a 50-ppm precursor ion tolerance for total protein level profiling. The product ion tolerance was set to 0.9 Da. These wide mass tolerance windows were chosen to maximize sensitivity in conjunction with SEQUEST searches and linear discriminant analysis (16 (link), 17 (link)). TMT tags on lysine residues and peptide N termini (+229.163 Da) and carbamidomethylation of cysteine residues (+57.021 Da) were set as static modifications, while oxidation of methionine residues (+15.995 Da) was set as a variable modification. For phosphorylation analysis, deamidation (+0.984) on asparagine and glutamine and phosphorylation (+79.966) on serine, threonine, and tyrosine were set as variable modifications. Peptide-spectrum matches (PSMs) were adjusted to a 1% false discovery rate (FDR) (18 (link), 19 (link)). PSM filtering was performed using a linear discriminant analysis, as described previously (17 (link)) and then assembled further to a final protein-level FDR of 1% (19 (link)). Phosphorylation site localization was determined using the AScore algorithm (13 (link)). AScore is a probability-based approach for high-throughput protein phosphorylation site localization. Specifically, a threshold of 13 corresponded to 95% confidence in site localization. Proteins were quantified by summing reporter ion counts across all matching PSMs, as described previously (20 (link)). Reporter ion intensities were adjusted to correct for the isotopic impurities of the different TMT reagents according to manufacturer specifications. The signal-to-noise (S/N) measurements of peptides assigned to each protein were summed and these values were normalized so that the sum of the signal for all proteins in each channel was equivalent, to account for equal protein loading. Lastly, each protein was scaled such that the summed signal-to-noise for that protein across all channels was greater than 100, thereby generating a relative abundance (RA) measurement. A detailed description of the methods in a step-by-step outline is available in the Supplementary Materials .
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Asparagine
Asparagine
Asparagine is a nonessential amino acid that plays a crucial role in various physiological processes.
It is involved in protein synthesis, metabolism, and cellular signaling.
Asparagine is synthesized from glutamine and aspartic acid, and can be found in a variety of food sources, including meat, dairy products, and certain vegetables.
Understanding the functions and regulation of asparagine is important for researchers studying topics such as nutrition, biochemistry, and molecular biology.
PubCompare.ai offers an AI-driven platform to help optimize your asparagine research by identifying the best protocols from literature, preprints, and patents, while comparing them to enhahnce reproducibility and accuracy in your studies.
It is involved in protein synthesis, metabolism, and cellular signaling.
Asparagine is synthesized from glutamine and aspartic acid, and can be found in a variety of food sources, including meat, dairy products, and certain vegetables.
Understanding the functions and regulation of asparagine is important for researchers studying topics such as nutrition, biochemistry, and molecular biology.
PubCompare.ai offers an AI-driven platform to help optimize your asparagine research by identifying the best protocols from literature, preprints, and patents, while comparing them to enhahnce reproducibility and accuracy in your studies.
Most cited protocols related to «Asparagine»
Amino Acid Sequence
Asparagine
Capillaries
Cysteine
Genome
Glutamine
Hypersensitivity
Immune Tolerance
Isotopes
Lysine
Mass Spectrometry
Methionine
Peptides
Phosphorylation
Protein Precursors
Proteins
Resins, Plant
Saccharomyces
Serine
Signal Peptides
Staphylococcal Protein A
Threonine
Tyrosine
Acetylation
Amino Acid Sequence
Asparagine
Brain
Cysteine
Cytokeratin
Glutamine
Homo sapiens
Immune Tolerance
Isotopes
Methionine
NR4A2 protein, human
nucleoprotein, Measles virus
Peptides
Proteins
Proteome
Radionuclide Imaging
Retention (Psychology)
Spectrin
Tandem Mass Spectrometry
Trypsin
Agar
Albumins
Antibiotics, Antitubercular
Asparagine
Bacteria
Bacteria, Anaerobic
Catalase
DNA Replication
ferric ammonium citrate
Glucose
Glycerin
Hypoxia
Kanamycin
Mycobacterium
Oleic Acid
Oxygen
Plasmids
Sterility, Reproductive
Sulfate, Magnesium
Technique, Dilution
Tween 80
Tweens
Vision
S. pyogenes was routinely grown in Todd-Hewitt (TH; BD Biosciences) supplemented with 0.2% yeast extract (Y; Amresco) or in an improved chemically-defined medium (CDM) based on a previously described recipe [21] (link), [51] (link). Briefly, we found that the preparation of this medium could be streamlined by combining components into stock solutions that could be stored at −20°C or room temperature (as described in
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Agar
Ampicillin
Antibiotics, Antitubercular
Asparagine
Bacteria
Chloramphenicol
Erythromycin
Escherichia coli
Glycerin
Gold
Spectinomycin
Strains
Streptococcus pyogenes
Yeast, Dried
Missing loops not defined in the HIV-1-Env trimer crystal structure were modeled using Loopy63 (link). Missing side chains were modeled with Scap64 (link).
To model the N-linked glycan shield, we first determined all possible N-linked sequons in the HIV-1 Env trimer structure. A single asparagine residue in each sequon was targeted for computational N-linked glycan addition using a series of oligomannose 9 rotamer libraries at different resolutions. In constructing the rotamer libraries, the asparagine side chain rotamers were also considered. To avoid a combinatorial explosion in the search space, select torsion angles in the oligomannose 9 rotamer libraries were allowed to vary in increments between 30-60 degrees. We used an overlap factor (ofac) to screen for clashes between the sugar moieties and the trimer structure. The ofac between two nonbonded atoms is defined as the distance between two atoms divided by the sum of their van der Waal's radii. For the modeling carried out here, we set the ofac to a value of 0.60. For sterically occluded positions, the ofac was set to 0.55. To remove steric bumps between sugar moieties, all models were subjected to 100 cycles of conjugate gradient energy minimization using the GLYCAM65 (link) force field in Amber1266 with a distance-dependent dielectric.
To model the N-linked glycan shield, we first determined all possible N-linked sequons in the HIV-1 Env trimer structure. A single asparagine residue in each sequon was targeted for computational N-linked glycan addition using a series of oligomannose 9 rotamer libraries at different resolutions. In constructing the rotamer libraries, the asparagine side chain rotamers were also considered. To avoid a combinatorial explosion in the search space, select torsion angles in the oligomannose 9 rotamer libraries were allowed to vary in increments between 30-60 degrees. We used an overlap factor (ofac) to screen for clashes between the sugar moieties and the trimer structure. The ofac between two nonbonded atoms is defined as the distance between two atoms divided by the sum of their van der Waal's radii. For the modeling carried out here, we set the ofac to a value of 0.60. For sterically occluded positions, the ofac was set to 0.55. To remove steric bumps between sugar moieties, all models were subjected to 100 cycles of conjugate gradient energy minimization using the GLYCAM65 (link) force field in Amber1266 with a distance-dependent dielectric.
Asparagine
Carbohydrates
Explosion
factor A
HIV-1
Polysaccharides
Radius
Most recents protocols related to «Asparagine»
Example 2
A. Seed Treatment with Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
B. Seed Treatment with Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
C. Treatment with Agricultural Composition Comprising Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
D. Treatment with Agricultural Composition Comprising Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
A. Seed Treatment with Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
B. Seed Treatment with Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
C. Treatment with Agricultural Composition Comprising Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
D. Treatment with Agricultural Composition Comprising Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
A. Seed Treatment with Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
B. Seed Treatment with Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
C. Treatment with Agricultural Composition Comprising Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
D. Treatment with Agricultural Composition Comprising Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
The inoculants were prepared from isolates grown as spread plates on R2A incubated at 25° C. for 48 to 72 hours. Colonies were harvested by blending with sterile distilled water (SDW) which was then transferred into sterile containers. Serial dilutions of the harvested cells were plated and incubated at 25° C. for 24 hours to estimate the number of colony forming units (CFU) in each suspension. Dilutions were prepared using individual isolates or blends of isolates (consortia) to deliver 1×105 cfu/microbe/seed and seeds inoculated by either imbibition in the liquid suspension or by overtreatment with 5% vegetable gum and oil.
Seeds corresponding to the plants of table 15 were planted within 24 to 48 hours of treatment in agricultural soil, potting media or inert growing media. Plants were grown in small pots (28 mL to 200 mL) in either a controlled environment or in a greenhouse. Chamber photoperiod was set to 16 hours for all experiments on all species. Air temperature was typically maintained between 22-24° C.
Unless otherwise stated, all plants were watered with tap water 2 to 3 times weekly. Growth conditions were varied according to the trait of interest and included manipulation of applied fertilizer, watering regime and salt stress as follows:
-
- Low N—seeds planted in soil potting media or inert growing media with no applied N fertilizer
- Moderate N—seeds planted in soil or growing media supplemented with commercial N fertilizer to equivalent of 135 kg/ha applied N
- Insol P—seeds planted in potting media or inert growth substrate and watered with quarter strength Pikovskaya's liquid medium containing tri-calcium phosphate as the only form phosphate fertilizer.
- Cold Stress—seeds planted in soil, potting media or inert growing media and incubated at 10° C. for one week before being transferred to the plant growth room.
- Salt stress—seeds planted in soil, potting media or inert growing media and watered with a solution containing between 100 to 200 mg/L NaCl.
Untreated (no applied microbe) controls were prepared for each experiment. Plants were randomized on trays throughout the growth environment. Between 10 and 30 replicate plants were prepared for each treatment in each experiment. Phenotypes were measured during early vegetative growth, typically before the V3 developmental stage and between 3 and 6 weeks after sowing. Foliage was cut and weighed. Roots were washed, blotted dry and weighed. Results indicate performance of treatments against the untreated control.
The data presented in table 15 describes the efficacy with which a microbial species or strain can change a phenotype of interest relative to a control run in the same experiment. Phenotypes measured were shoot fresh weight and root fresh weight for plants growing either in the absence of presence of a stress (assay). For each microbe species, an overall efficacy score indicates the percentage of times a strain of that species increased a both shoot and root fresh weight in independent evaluations. For each species, the specifics of each independent assay is given, providing a strain ID (strain) and the crop species the assay was performed on (crop). For each independent assay the percentage increase in shoot and root fresh weight over the controls is given.
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Ammonia
Asparagine
Aspartic Acid
Biological Assay
Bosea thiooxidans
Calcium Phosphates
Capsicum
Cells
Chlorophyll
Cold Shock Stress
Cold Temperature
Crop, Avian
Dietary Fiber
DNA Replication
Droughts
Drought Tolerance
Embryophyta
Environment, Controlled
Farmers
Fertilization
Glutamic Acid
Glutamine
Glycine
Growth Disorders
Herbaspirillum
Herbaspirillum huttiense
Leucine
Lolium
Lycopersicon esculentum
Lysine
Maize
Massilia niastensis
Methionine
Microbial Consortia
Nitrates
Nitrites
Nitrogen
Novosphingobium rosa
Paenibacillus
Paenibacillus amylolyticus
Pantoea agglomerans
Pantoea vagans
Phenotype
Phosphates
Photosynthesis
Plant Development
Plant Embryos
Plant Leaves
Plant Proteins
Plant Roots
Plants
Polaromonas ginsengisoli
Pseudoduganella violaceinigra
Pseudomonas
Pseudomonas fluorescens
Rahnella
Rahnella aquatilis
Retention (Psychology)
Rhodococcus erythropolis
Rosa
Salt Stress
Sodium Chloride
Sodium Chloride, Dietary
Stenotrophomonas chelatiphaga
Stenotrophomonas maltophilia
Stenotrophomonas rhizophila
Stenotrophomonas terrae
Sterility, Reproductive
Strains
Technique, Dilution
Threonine
Triticum aestivum
Tryptophan
Tyrosine
Vegetables
Zea mays
An Xevo G2 QTof coupled to a nanoAcquity UPLC system (Waters, Milford, MA) was used for phosphorylation site identification. Briefly, samples were loaded onto a C18 Waters Trizaic nanotile of 85 µm × 100 mm; 1.7 μm (Waters, Milford, MA). The column temperature was set to 45°C with a flow rate of 0.45 ml/min. The mobile phase consisted of A (water containing 0.1% formic acid) and B (acetonitrile containing 0.1% formic acid). A linear gradient elution program was used: 0–40 min, 3–40% (B); 40–42 min, 40–85% (B); 42–46 min, 85% (B); 46–48 min, 85–3% (B); 48–60 min, 3% (B).
Mass spectrometry data were recorded for 60 min for each run and controlled by MassLynx 4.2 (Waters, Milford, MA). Acquisition mode was set to positive polarity under resolution mode. Mass range was set from 50 to 2000 Da. Capillary voltage was 3.5 kV, sampling cone at 25 V, and extraction cone at 2.5 V. Source temperature was held at 110°C. Cone gas was set to 25 l/hr, nano flow gas at 0.10 bar, and desolvation gas at 1200 l/hr. Leucine–enkephalin at 720 pmol/µl (Waters, Milford, MA) was used as the lock mass ion at m/z 556.2771 and introduced at 1 µl/min at 45-s intervals with a 3 scan average and mass window of ±0.5 Da. The Mse data were acquired using two scan functions, corresponding to low energy for function 1 and high energy for function 2. Function 1 had collision energy at 6 V and function 2 had a collision energy ramp of 18–42 V.
RAW Mse files were processed using Protein Lynx Global Server (PLGS) version 3.0.3 (Waters, Milford, MA). Processing parameters consisted of a low energy threshold set at 200.0 counts, an elevated energy threshold set at 25.0 counts, and an intensity threshold set at 1500 counts. The databank used corresponded to C. elegans and was downloaded fromuniprot.org and then randomized. Searches were performed with trypsin specificity and allowed for two missed cleavages. Possible structure modifications included for consideration were methionine oxidation, carbamidomethylation of cysteine, deamidiation of asparagine or glutamine, dehydration of serine or threonine, and phosphorylation of serine, threonine, or tyrosine.
Mass spectrometry data were recorded for 60 min for each run and controlled by MassLynx 4.2 (Waters, Milford, MA). Acquisition mode was set to positive polarity under resolution mode. Mass range was set from 50 to 2000 Da. Capillary voltage was 3.5 kV, sampling cone at 25 V, and extraction cone at 2.5 V. Source temperature was held at 110°C. Cone gas was set to 25 l/hr, nano flow gas at 0.10 bar, and desolvation gas at 1200 l/hr. Leucine–enkephalin at 720 pmol/µl (Waters, Milford, MA) was used as the lock mass ion at m/z 556.2771 and introduced at 1 µl/min at 45-s intervals with a 3 scan average and mass window of ±0.5 Da. The Mse data were acquired using two scan functions, corresponding to low energy for function 1 and high energy for function 2. Function 1 had collision energy at 6 V and function 2 had a collision energy ramp of 18–42 V.
RAW Mse files were processed using Protein Lynx Global Server (PLGS) version 3.0.3 (Waters, Milford, MA). Processing parameters consisted of a low energy threshold set at 200.0 counts, an elevated energy threshold set at 25.0 counts, and an intensity threshold set at 1500 counts. The databank used corresponded to C. elegans and was downloaded from
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acetonitrile
ARID1A protein, human
Asparagine
Capillaries
Cysteine
Cytokinesis
Dehydration
Enkephalin, Leucine
formic acid
Glutamine
Lynx
Mass Spectrometry
Methionine
Phosphorylation
Proteins
Radionuclide Imaging
Retinal Cone
Serine
Threonine
Trypsin
Tyrosine
The data were analyzed using Proteome Discoverer (version 2.5.0.400; Thermo Scientific). Spectra files were searched using Sequest HT and Mascot against a Sus scrofa UniProt database. Search parameters of peptide datasets included a 10 ppm precursor mass tolerance and a 0.02 Da fragment mass tolerance for b and y ions produced by HCD fragmentation. Data were searched with the static carbamidomethyl modification of cysteine residues, static TMT-6plex modifications of peptide N-termini and lysine residues, dynamic methionine oxidation, and dynamic deamination of asparagines, and glutamine residues. Identified peptides were filtered to a 1% false discovery rate (FDR). The peptides were then matched to proteins and were filtered to a 1% FDR on the protein level. Proteins were grouped using the maximum parsimony principle from all retained PSM using the default settings of Proteome Discoverer. Protein groups with no unique peptides were removed, and the ambiguity of spectra with more than one PSM was resolved by matching the PSM with the best-fitting protein group and rejecting the other PSM. The best-fitting protein group was the protein group with the highest number of unambiguous PSM and the most unique peptides (Thermo Fisher Scientific, 2020 ).
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Asparagine
Cysteine
Deamination
Glutamine
Immune Tolerance
Ions
Lysine
Methionine
Peptides
Proteins
Proteome
Strains
Sus scrofa
vRNA-synthetic plasmids (pPol-D/OK-PB2, -PB1, -P3, -HEF, -NP, -M, and -NS) comprising cDNAs of D/OK viral genes between human RNA polymerase I promoter and mouse RNA polymerase I terminator and eukaryotic protein expression plasmids (pCAGGS-D/OK-PB2, -PB1, -P3, and -NP) under the control of the chicken β-actin promoter were used for reverse genetics as described previously38 (link). To generate mutant recombinant viruses, vRNA synthesis plasmids (pPol-D/OK-PB2 and -PB1) with PB2 and PB1 segments were modified. We constructed pol-D/OK-PB2-AL by replacing the 494th codon in pPol-D/OK-PB2 from GTG (encoding valine) to CTC (leucine). Similarly, pPol-D/OK-PB1-AL was constructed by replacing the 267th codon of pPol-D/OK-PB1 from AGA (arginine) to AAT (asparagine).
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Actins
Anabolism
Arginine
Asparagine
Chickens
Codon
DNA, Complementary
Eukaryotic Cells
Genes, Viral
Homo sapiens
Leucine
Mice, Laboratory
Plasmids
Proteins
RNA Polymerase I
Valine
Virus
All Thermo
RAW files were saved in our local interaction proteomics LIMS, ProHits.20 (link) mzXML files were generated from ThermoFinnigan
RAW files using the ProteoWizard21 converter,
implemented within ProHits (−filter “peakPicking true2”–filter
“msLevel2”). The searched database contained the human
and adenovirus complements of the RefSeq protein database (version
57) supplemented with “common contaminants” from the
Max Planck Institute (http://141.61.102.106:8080/share.cgi?ssid=0f2gfuB ) and the Global Proteome Machine (GPM; http://www.thegpm.org/crap/index.html ) as well as sequences from common fusion proteins and epitope tags.
The sequence database consisted of forward and reversed sequences;
in total, 72,226 sequences were searched. The search engines were
Mascot and Comet, with trypsin specificity and two missed cleavage
sites allowed. Methionine oxidation and asparagine/glutamine deamidation
were set as variable modifications. The fragment mass tolerance was
0.6 Da, and the mass window for the precursor was ±12 ppm. The
resulting Comet and Mascot search results were individually processed
by PeptideProphet,22 (link) and peptides were
assembled into proteins using parsimony rules first described in ProteinProphet23 (link) into a final iProphet24 (link) protein output using the Trans-Proteomic Pipeline (TPP; Linux version,
v0.0 Development trunk rev 0, Build 201303061711). TPP options were
as follows: general options were -p0.05 -x20 -PPM - d’DECOY’;
iProphet options were pPRIME and PeptideProphet options were pPAEd.
All proteins with a minimal iProphet protein probability of 0.05 were
parsed to the relational module of ProHits. Note that for analysis
with significance analysis of interactome (SAINT), only proteins with
iProphet protein probability R ≥ 0.95 and
two unique peptides are considered.
RAW files were saved in our local interaction proteomics LIMS, ProHits.20 (link) mzXML files were generated from ThermoFinnigan
RAW files using the ProteoWizard21 converter,
implemented within ProHits (−filter “peakPicking true2”–filter
“msLevel2”). The searched database contained the human
and adenovirus complements of the RefSeq protein database (version
57) supplemented with “common contaminants” from the
Max Planck Institute (
The sequence database consisted of forward and reversed sequences;
in total, 72,226 sequences were searched. The search engines were
Mascot and Comet, with trypsin specificity and two missed cleavage
sites allowed. Methionine oxidation and asparagine/glutamine deamidation
were set as variable modifications. The fragment mass tolerance was
0.6 Da, and the mass window for the precursor was ±12 ppm. The
resulting Comet and Mascot search results were individually processed
by PeptideProphet,22 (link) and peptides were
assembled into proteins using parsimony rules first described in ProteinProphet23 (link) into a final iProphet24 (link) protein output using the Trans-Proteomic Pipeline (TPP; Linux version,
v0.0 Development trunk rev 0, Build 201303061711). TPP options were
as follows: general options were -p0.05 -x20 -PPM - d’DECOY’;
iProphet options were pPRIME and PeptideProphet options were pPAEd.
All proteins with a minimal iProphet protein probability of 0.05 were
parsed to the relational module of ProHits. Note that for analysis
with significance analysis of interactome (SAINT), only proteins with
iProphet protein probability R ≥ 0.95 and
two unique peptides are considered.
Adenovirus Vaccine
Amino Acid Sequence
Asparagine
Comet Assay
Complement System Proteins
Epitopes
Feces
Gene Products, Protein
Glutamine
Immune Tolerance
link protein
Methionine
Peptides
Proteins
Proteome
R recombinase
Trypsin
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More about "Asparagine"
Asparagine, a nonessential amino acid, plays a crucial role in various physiological processes, including protein synthesis, metabolism, and cellular signaling.
This essential biomolecule is synthesized from L-glutamine and L-aspartic acid, and can be found in a variety of food sources, such as meat, dairy products, and certain vegetables.
Understanding the functions and regulation of asparagine is vital for researchers studying topics like nutrition, biochemistry, and molecular biology.
Asparagine is also closely related to other amino acids like tryptophan and phenylalanine, which are involved in similar metabolic pathways.
Proteome Discoverer and Mascot are powerful bioinformatics tools used to identify and quantify asparagine and other amino acids within complex protein samples.
These platforms, along with Proteome Discoverer 2.2 and 1.4, can help researchers optimize their asparagine research by identifying the best protocols from literature, preprints, and patents, while comparing them to enhance reproducibility and accuracy.
PubCompare.ai offers an AI-driven platform that can assist researchers in navigating this complex landscape, enabling them to locate the most effective methods and protocols for their asparagine studies.
By leveraging the insights gained from the available data, researchers can enhance the reproducibility and accuracy of their work, ultimately advancing our understanding of this crucial amino acid and its role in various physiological processes.
This essential biomolecule is synthesized from L-glutamine and L-aspartic acid, and can be found in a variety of food sources, such as meat, dairy products, and certain vegetables.
Understanding the functions and regulation of asparagine is vital for researchers studying topics like nutrition, biochemistry, and molecular biology.
Asparagine is also closely related to other amino acids like tryptophan and phenylalanine, which are involved in similar metabolic pathways.
Proteome Discoverer and Mascot are powerful bioinformatics tools used to identify and quantify asparagine and other amino acids within complex protein samples.
These platforms, along with Proteome Discoverer 2.2 and 1.4, can help researchers optimize their asparagine research by identifying the best protocols from literature, preprints, and patents, while comparing them to enhance reproducibility and accuracy.
PubCompare.ai offers an AI-driven platform that can assist researchers in navigating this complex landscape, enabling them to locate the most effective methods and protocols for their asparagine studies.
By leveraging the insights gained from the available data, researchers can enhance the reproducibility and accuracy of their work, ultimately advancing our understanding of this crucial amino acid and its role in various physiological processes.