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Amino Acids, Acidic

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Amino Acids, Acidic are a class of organic compounds containing an amino group (-NH2) and a carboxyl group (-COOH) in which the carboxyl group is attaced to an acidic carbon atom, such as in glutamic acid and aspartic acid.

Most cited protocols related to «Amino Acids, Acidic»

1
Comprehensive NMR Metabolomics in Diverse Cohorts
Cited 33 times
This work is an extension of our previous GWA-metabolomics study, in which the
quantitative high-throughput NMR metabolomics platform, used to quantify human
blood metabolites, was applied4 (link). In this study, we have utilized
the same platform to quantify 123 metabolite measures that represent a broad
molecular signature of systemic metabolism. The metabolite set covers multiple
metabolic pathways, including lipoprotein lipids and subclasses, fatty acids as
well as amino acids and glycolysis precursors. Most of the NMR-based
metabolomics analyses were performed with the comprehensive quantitative
serum/plasma platform described originally by Soininen et al.24 (link) and reviewed recently25 (link). This same platform was
used here to analyse samples in Estonian Genome Center of University of Tartu
Cohort (EGCUT), Finnish Twin Cohort, a subsample of FINRISK 1997 (FR97), Genetic
Predisposition of Coronary Heart Disease in Patients Verified with Coronary
Angiogram (COROGENE), Genetics of METabolic Syndrome, Helsinki Birth Cohort
Study (HBCS), Cooperative Health Research in the Region of Augsburg (KORA),
Northern Finland Birth Cohort 1966 (NFBC 1966), FINRISK subsample of incident
cardiovascular cases and controls (PredictCVD), EGCUT sub-cohort (PROTE) and
YFS. Metabolite-specific untransformed distributions and descriptive summary
statistics from the largest cohort, NFBC 1966, are presented in Supplementary Fig. 3. Chemical shifts and
the coefficients of variation for inter-assay variability are presented in Supplementary Data 3 for each
metabolite. Here, the study was extended with Erasmus Rucphen Family Study
(ERF), Leiden Longevity Study (LLS) and Netherlands Twin Register (NTR) cohorts
for which the small-molecule information was available from another NMR-based
method (Supplementary Table 2 for
details)26 (link). Metabolite-specific untransformed distributions
and descriptive summary statistics for these measures from the ERF cohort are
given in Supplementary Fig. 4.
Chemical shifts and the coefficients of variation for inter-assay variability
are presented in Supplementary Table
7. The sample material was mostly serum, except for EGCUT, PROTE, NTR
and LLS in which the sample material was EDTA-plasma. The ERF cohort had
additional lipoprotein measures available through the method developed by Bruker
Ltd. (https://www.bruker.com/fileadmin/user_upload/8-PDF-Docs/MagneticResonance/NMR/brochures/lipo-analysis_apps.pdf).
The terminology of this method utilized for lipoprotein analyses in ERF was
matched based on the lipoprotein particle size with the comprehensive
quantitative serum/plasma platform to enable meta-analyses. The vast majority of
blood samples were fasting, however, if a study did not have overnight fasting
samples, we corrected the fasting time effect by using R package gam and fitting
a smoothed spline to adjust for fasting. All metabolites were first adjusted for
age, sex, time from last meal, if applicable, and ten first principal components
from genomic data and the resulting residuals were transformed to normal
distribution by inverse rank-based normal transformation.
Kettunen J., Demirkan A., Würtz P., Draisma H.H., Haller T., Rawal R., Vaarhorst A., Kangas A.J., Lyytikäinen L.P., Pirinen M., Pool R., Sarin A.P., Soininen P., Tukiainen T., Wang Q., Tiainen M., Tynkkynen T., Amin N., Zeller T., Beekman M., Deelen J., van Dijk K.W., Esko T., Hottenga J.J., van Leeuwen E.M., Lehtimäki T., Mihailov E., Rose R.J., de Craen A.J., Gieger C., Kähönen M., Perola M., Blankenberg S., Savolainen M.J., Verhoeven A., Viikari J., Willemsen G., Boomsma D.I., van Duijn C.M., Eriksson J., Jula A., Järvelin M.R., Kaprio J., Metspalu A., Raitakari O., Salomaa V., Slagboom P.E., Waldenberger M., Ripatti S, & Ala-Korpela M. (2016). Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nature Communications, 7, 11122.
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Publication 2016
Amino Acids, Acidic Biological Assay Birth Cohort Childbirth CTSB protein, human Edetic Acid Extended Family Genome Genome-Wide Association Study Glycolysis Heart Disease, Coronary Hereditary Diseases HMGA2 protein, human Lipids Lipoproteins Metabolism Patients Plasma Serum Twins
2
Construction of SARS-CoV-2 D614G Mutant
Cited 18 times
One single-nucleotide substitution was introduced into a subclone puc57-CoV-2-F5–7 containing the spike gene of the SARS-CoV-2 wild type (WT) infectious clone10 (link) to convert the 614th amino acid from aspartic acid (D) to glycine (G) by overlap fusion PCR. The full-length infectious cDNA clone of SARS-CoV-2 D614G was assembled by in vitro ligation of seven contiguous cDNA fragments following the protocol previously described10 (link). For construction of D614G mNeonGreen SARS-CoV-2, seven SARS-CoV-2 genome fragments (F1 to F5, F6 containing D614G mutation, and F7-mNG containing the mNeonGreen reporter gene) were prepared and in vitro ligated as described previously10 (link). In vitro transcription was then preformed to synthesize full-length genomic RNA. For recovering the mutant viruses, the RNA transcripts were electroporated into Vero E6 cells. The viruses from electroporated cells were harvested at 40 h post electroporation and served as seed stocks for subsequent experiments. The D614G mutation from the recovered viruses was confirmed by sequence analysis. Viral titers were determined by plaque assay on Vero E6 cells. All virus preparation and experiments were performed in a biosafety level 3 (BSL-3) facilities. Viruses and plasmids are available from the World Reference Center for Emerging Viruses and Arboviruses at the University of Texas Medical Branch.
Plante J.A., Liu Y., Liu J., Xia H., Johnson B.A., Lokugamage K.G., Zhang X., Muruato A.E., Zou J., Fontes-Garfias C.R., Mirchandani D., Scharton D., Bilello J.P., Ku Z., An Z., Kalveram B., Freiberg A.N., Menachery V.D., Xie X., Plante K.S., Weaver S.C, & Shi P.Y. (2020). Spike mutation D614G alters SARS-CoV-2 fitness. Nature, 592(7852), 116-121.
Publication 2020
Amino Acids, Acidic Arboviruses Biological Assay Clone Cells COVID 19 Dental Plaque DNA, Complementary Electroporation Genes Genes, Reporter Genome Glycine Ligation Mutation Nucleotides Plasmids SARS-CoV-2 SARS-CoV-2 D614G variant Sequence Analysis Transcription, Genetic Vero Cells Virus
3
Synthetic Generation of Realistic HIV Sequence Data
Cited 18 times
These methods were applied to population-based HIV sequences from chronically infected, antiretroviral naïve and HLA-typed individuals from two cohorts: the HOMER cohort from British Columbia, Canada, consisting of 567 predominantly clade B gag sequences [9] (link), and the Durban cohort, consisting of 522 predominantly clade C p17/p24 gag sequences from Durban, South Africa [10] (link),[44] (link). Individuals in the HOMER and Durban cohorts were HLA-typed to two- and four-digit resolution, respectively. Here, we truncate the Durban data to two-digits for comparison with the HOMER cohort. Viral sequences were determined by nested reverse-transcriptase polymerase chain reaction (RT-PCR) amplification of extracted plasma HIV RNA followed by bulk sequencing, as previously described [8] (link)–[10] (link). Phylogenies were constructed from these sequences using PHYML [50] (link), run using the general time reversible model over the HIV sequences and estimating all parameters via maximum likelihood.
Synthetic datasets were designed to mimic the real datasets as closely as possible. We first fit a specified model to the real data to identify parameters and q-values for each predictor-target pair. We then planted predictor-target pairs for each significant (q≤0.2) predictor-target pair identified from the real data. Specifically, we generated a synthetic target amino acid for each consensus amino acid in the sequence, such that (1) if the amino acid had no significant (q≤0.2) associations, then the amino acid was generated according to the parameters of the independent evolution model (the null model from the univariate case), and (2) if the amino acid had M>0 associations, then the amino acid was generated according to the given multivariate model with the predictor parameters s1,…, sM, taken from the real data. When an observation was missing in the real data, the corresponding observation in the synthetic data was also made to be missing. We treated amino acid insertions/deletions and mixtures as missing data.
Our goal was to generate data that is as realistic as possible, both in the values of the parameters used and the number of predictors deemed correlated with the target. Because our recall rate is less than 100% (see section on synthetic results), planting only those associations that are found in the real data would result in a smaller proportion of synthetic predictor-target pairs called significant than real predictor-target pairs called significant. We therefore planted two associations for every observed significant association in the real data and reduced the number of independently evolving codons accordingly. For the Noisy Add model, this procedure planted 72 HLA-codon and 612 codon-codon associations in the HOMER cohort and 114 HLA-codon and 952 codon-codon associations in the combined HOMER-Durban cohort. In hindsight, doubling the number of planted associations was an overcompensation, as experiments on this synthetic data yielded a 75% recall rate. Nonetheless, the doubling produced a reasonable result, as Noisy Add declared 0.56% of all synthetic predictor-target pairs significant at q≤0.2 compared to 0.65% of all predictor-target pairs in the real data for the combined HOMER-Durban cohort.
Carlson J.M., Brumme Z.L., Rousseau C.M., Brumme C.J., Matthews P., Kadie C., Mullins J.I., Walker B.D., Harrigan P.R., Goulder P.J, & Heckerman D. (2008). Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 Gag. PLoS Computational Biology, 4(11), e1000225.
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Publication 2008
Amino Acids Amino Acids, Acidic Biological Evolution Codon Dietary Fiber Fingers INDEL Mutation Mental Recall Plasma Reverse Transcriptase Polymerase Chain Reaction
4
Efficient HMM-HMM Alignment with Local Sequence Scoring
Cited 14 times
The Viterbi algorithm, when applied to profile HMMs, is formally equivalent to global sequence alignment with position-specific gap penalties [27 (link)]. We had previously introduced a modification of the Viterbi algorithm that is formally equivalent to Smith-Waterman local sequence alignment [15 (link)]. In HH-suite we use it to compute the best-scoring local alignment between two profile HMMs.
HH-suite models MSA columns with <50% gaps (default value) by match states and all other columns as insertion states. By traversing through the states of a profile HMM, the HMM can “emit” sequences. A match state (M) emits amino acids according to the 20 probabilities of amino acids estimated from their fraction in the MSA column, plus some pseudocounts. Insert states (I) emit amino acids according to a standard amino acid background distribution, while delete states (D) do not emit any amino acids.
The alignment score between two HMMs in HH-suite is the sum over all co-emitted sequences of the log odds scores for the probability for the two aligned HMMs to co-emit this sequence divided by the probability of the sequence under the background model. Since M and I states emit amino acids and D states do not, M and I in one HMM can only be aligned with M or I states in the other HMM. Conversely, a D state can only be aligned with a D state or with a Gap G (Fig. 1). The co-emission score can be written as the sum of the similarity scores of the aligned profile columns, in other words the match-match (MM) pair states, minus the position-specific penalties for indels: delete-open, delete-extend, insert-open and insert-extend.

HMM-HMM alignment of query and target. The alignment is represented as red path through both HMMs. The corresponding pair state sequence is MM, MM, MI, MM, MM, DG, MM

We denote the alignment pair states as MM, MI, IM, II, DD, DG, and GD. Figure 1 shows an example of two aligned profile HMMs. In the third column HMM q emits a residue from its M state and HMM p emits a residue from the I state. The pair state for this alignment column is MI. In column six of the alignment HMM q does not emit anything since it passes through the D state. HMM p does not emit anything either since it has a gap in the alignment. The corresponding pair state is DG. To speed up the alignment, we exclude pair states II and DD, and we only allow transitions between a pair state and itself and between pair state MM and pair states MI, IM, DG, or GD.
To calculate the local alignment score, we need five dynamic programming matrices SXY, one for each pair state XY ∈{MM, MI, IM, DG, GD }. They contain the score of the best partial alignment which ends in column i of q and column j of p in pair state XY. These five matrices are calculated recursively.
SMMi,j=Saaqip,tjp+Sssqiss,tjss+max0(for local alignment)SMM(i−1,j−1)+logqi−1(M,M)tj−1(M,M)SMI(i−1,j−1)+logqi−1(M,M)tj−1(I,M)SII(i−1,j−1)+logqi−1(I,M)tj−1(M,M)SDG(i−1,j−1)+logqi−1(D,M)tj−1(M,M)SGD(i−1,j−1)+logqi−1M,Mtj−1(D,M)
SMIi,j=maxSMM(i−1,j)+logqi−1(M,M)tj(D,D)SMI(i−1,j)+logqi−1(M,M)tj(I,I)
SDGi,j=maxSMM(i−1,j)+logqi−1(D,M)SDG(i−1,j)+logqi−1(D,D)
Saaqip,tjp=log∑a=120qip(a)tjp(a)fa
Vector qip contains the 20 amino acid probabilities of q at position i, tjp are the amino acid probabilities t at j, and fa denotes the background frequency of amino acid a. The score Saa measures the similarity of amino acid distributions in the two columns i and j. Sss can optionally be added to Saa. It measures the similarity of the secondary structure states of query and target HMM at i and j [15 (link)].
Steinegger M., Meier M., Mirdita M., Vöhringer H., Haunsberger S.J, & Söding J. (2019). HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics, 20, 473.
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Publication 2019
Amino Acids Amino Acids, Acidic Cloning Vectors Enzyme Multiplied Immunoassay Technique Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility INDEL Mutation Sequence Alignment
5
Modeling Alkaline Phosphatase Catalysis
Cited 14 times
The enzyme model is constructed based on the X-ray structures
for the E. coli Alkaline Phosphatase (AP) mutant
R166S with bound inorganic phosphate at 2.05 Å resolution (PDB
code 3CMR(122 (link))). The substrate methyl p-nitrophenyl
phosphate (MpNPP–) is first “mutated”
to the orientation with the −OMe group oriented toward the
magnesium ion (denoted as α orientation) starting from the PDB
structure. All basic and acidic amino acids are kept in their physiological
protonation states except for Ser102 in AP, which is accepted to be
the nucleophile and deprotonated in the reactive complex. Water molecules
are added following the standard protocol of superimposing the system
with a water droplet of 25 Å radius centered at Zn12+ (see Figure 6 for atomic labels) and removing
water molecules within 2.8 Å from any heavy atoms resolved in
the crystal structure. GSBP is used to treat long-range electrostatic
interactions in MD simulations. In the QM/MM simulations, as described
in details in our previous work,48 (link),49 (link) the QM region
includes the two zinc ions and their six ligands (Asp51, Asp369, His370,
Asp327, His412, His331), Ser102, and the substrate MpNPP–. Only side chains of protein residues are included in the QM region,
and link atoms are added between Cα and Cβ atoms. A NOE potential is added to the C–O bonds in Asp51
in AP to prevent artificial polarization.48 (link) The integration time step is 1 fs, and all bonds involving hydrogens
are constrained using SHAKE. The DFTB3/MM results are also compared
to MM simulations using a conventional nonbonded zinc model14 (link) (referred to as a Coulomb scheme) or short–long
effective functions (SLEF1)25 (link) model developed
by Zhang and co-workers. Protein atoms in the MM region are described
by the all-atom CHARMM22 force field, and water molecules are described
with the TIP3P model.
To further benchmark the applicability
of DFTB3/3OB to the reaction of interest, we also study an active
site model that includes all QM atoms in the QM/MM enzyme model. The
Cβ carbons are fixed at their positions in the crystal
structure during geometry optimization; the positions of the link
atoms used to saturate the valence of the Cβ atoms
in the active site model are also fixed. The reactant (Michaelis)
complex and transition state are located for MpNPP– (methyl p-nitrophenyl phosphate), MmNPP– (methyl m-nitrophenyl phosphate), and MPP– (methyl phenyl phosphate) using DFTB3 and B3LYP with the 6-31+G(d,p)
basis set. The minimum energy path (MEP) calculations are carried
out by one-dimensional adiabatic mapping at the DFTB3 level; the reaction
coordinate is the antisymmetric stretch involving the breaking and
forming P–O bonds. Subsequently, the saddle point is further
refined by conjugated peak refinement (CPR123 ) to obtain precise transition state structure. Single-point energy
calculations at DFTB3 and B3LYP geometries are then carried out using
B3LYP, M06,124 PBE, and MP2 methods using
a larger basis set of 6-311++G(d,p). The D3125 (link) dispersion corrections are added for B3LYP, M06, and PBE methods.
Lu X., Gaus M., Elstner M, & Cui Q. (2014). Parametrization of DFTB3/3OB for Magnesium and Zinc for Chemical and Biological Applications. The Journal of Physical Chemistry. B, 119(3), 1062-1082.
Publication 2014
Alkaline Phosphatase Amino Acids, Acidic Carbon Enzymes Escherichia coli Ions Ligands nitrophenylphosphate Phosphates Protein Domain Proteins Radiography Radius Tremor Workers Zinc

Most recents protocols related to «Amino Acids, Acidic»

1
Eukaryotic Protein Domain Analysis
All protein domain information was extracted from the Pfam database (Finn et al., 2016 (link)). We used the Pfam 33.1 version of May 2020, containing 18,259 entries. Among these domains, we used the Pfam-A subset of 18,101 curated domains for further analysis (Sonnhammer et al., 1997 (link)). Each alignment was filtered to remove information from Archaea, bacteria, viruses, and other sequences to retain only data from eukaryotes. Overall, they contain information from 27,077,043 domains from 1,161 species. The human protein domains were extracted from the canonical Uniprot transcripts used in Pfam and represent 5,168,776 amino acids out of the 12,871,017 amino acids in human proteins (40.2%).
For each residue, we then calculated an amino acid value using the following steps: creation of a count matrix, a corrected frequency matrix, a corrected relative frequency matrix, and the position score matrix.
Corcuff M., Garibal M., Desvignes J.P., Guien C., Grattepanche C., Collod-Béroud G., Ménoret E., Salgado D, & Béroud C. (2023). Protein domains provide a new layer of information for classifying human variations in rare diseases. Frontiers in Bioinformatics, 3, 1127341.
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Publication 2023
A-101 Amino Acids Amino Acids, Acidic Archaea Bacteria Eukaryota Homo sapiens NR4A2 protein, human Protein Domain Virus
2
Quantification of D-amino Acids in Serum

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Amino Acids Amino Acids, Acidic BLOOD Buffers derivatives Fluorescence High-Performance Liquid Chromatographies Maritally Unattached Methanol Pressure Reproduction Serum sodium borate Trifluoroacetic Acid
3
Improved Rabies Vaccine Formulation
The SPBN GASGAS vaccine suspension (107.5 FFU/mL, 3.0 mL) was filled in flexible sachets made of a proprietary foil (“soft blister”) and embedded in egg-flavoured gelatine-based baits. The SPBN GASGAS vaccine construct is derived from a cDNA clone (SAD L16) of the vaccine strain SAD B19. Through site-directed mutagenesis, the residual pathogenicity observed with the parental strain in mice after i.c. inoculation has been abolished (amino acid exchange at amino acid position 333 of the glycoprotein gene). Additionally, all 3 nucleotides at amino acid position 194 of the glycoprotein gene were exchanged to circumvent, potentially, a partial reversion to virulence due to a compensatory mutation at this position. Finally, a second identically modified glycoprotein gene was inserted between the G- and L-gene [31 (link),32 (link),33 (link)].
Bobe K., Ortmann S., Kaiser C., Perez-Bravo D., Gethmann J., Kliemt J., Körner S., Theuß T., Lindner T., Freuling C., Müller T, & Vos A. (2023). Efficacy of Oral Rabies Vaccine Baits Containing SPBN GASGAS in Domestic Dogs According to International Standards. Vaccines, 11(2), 307.
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Publication 2023
Amino Acids Amino Acids, Acidic Clone Cells DNA, Complementary Gelatins Gene Order Genes Glycoproteins Mice, House Mutagenesis, Site-Directed Mutation Nucleotides Parent Pathogenicity Strains Vaccination Vaccines Virulence
4
Spectroscopic Analysis of Coordination Complexes
Reagents of analytical grade were procured from Sigma-Aldrich, Alfa-Aesar, and Spectrochem and were used without further purification. The solvents were purified using the standard literature methods [42 ]. HPLC-grade solvents were used for the UV–Visible and fluorescence spectral studies. A stock solution of L1 and L2 (1 mM) was prepared in Tetrahydrofuran (THF). All stock solutions of metal salts of NaCl, KCl, BeCl2, MgCl2, Al(NO3)3, GaCl3, Pb(NO3)2, CrCl3, MnCl2, FeSO4, FeCl3, LiCl, CoCl2, NiCl2, CuSO4, ZnCl2, AgNO3, Pd(CH3COO)2, Cd(NO3)2, In(OTf)3, and HgCl2 (2.5 mM) were prepared in EtOH. All stock solutions of amino acids of Glutamic acid (Glu), Proline (Pro), Cysteine (Cys), Isoleucine (Ile), Tyrosine (Tyr), Arginine (Arg), Glutamine (Gln), Lysine (Lys), Threonine (Thr), Aspartic acid (Asp), Alanine (Ala), Asparagine (Asn), Glycine (Gly), Histidine (His), Leucine (Leu), Methionine (Met), Phenylalanine (Phe), Serine (Ser), Tryptophan (Trp), and Valine (Val) were prepared in H2O. All UV-Vis and fluorescence spectral experiments were performed with a 1.0 cm path length cuvette at 25 °C in EtOH.
Goyal H., Annan I., Ahluwalia D., Bag A, & Gupta R. (2023). Discriminative ‘Turn-on’ Detection of Al3+ and Ga3+ Ions as Well as Aspartic Acid by Two Fluorescent Chemosensors. Sensors (Basel, Switzerland), 23(4), 1798.
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Publication 2023
Alanine Amino Acids, Acidic Arginine Asparagine Aspartic Acid Cysteine Ethanol Fluorescence Glutamine Glycine High-Performance Liquid Chromatographies Histidine Isoleucine Leucine Lysine Magnesium Chloride manganese chloride Mercuric Chloride Metals Methionine Phenylalanine POU5F1 protein, human Proline Salts Serine Sodium Chloride Solvents tetrahydrofuran Threonine Tryptophan Tyrosine Valine
5
Protein and Amino Acid Analysis Protocol
The Kjeldahl assay (Velp Scientifica UDK 127, Italy) [17 ] was employed to measure the total protein content. The results were calculated according to Equation (1):
where TN is the total nitrogen content (%), and 6.25 is the conversion factor.
The water-soluble amino acid profile was investigated by hydrolysing and derivatising the samples before injection onto a gas chromatograph with flame ionization detector GC-FID Trace GC1310 (Thermo Scientific, USA). A total of 1 g of the sample was ground, rigorously mixed with 5 mL of distilled water [18 (link)], and centrifuged for 5 min. A volume of 0.5 mL of supernatant was harvested and passed through a Dowex 50W-W8 ion exchange resin and, subsequently, eluted with a 4M NH4OH solution. Derivatisation was performed in two steps: esterification of the extracted amino acids with a mixture consisting of acetyl chloride n-butanol (4:1 v/v) for 1 h at 100 °C, and acetylation with 100 μL of trifloractic anhydride at 60 °C for 30 min. A volume of 1 μL of the solution of the derived amino acids was separated into a chromatograph gas with a flame ionization detector GC-FID Trace GC1310 (Thermo Scientific) using a capillary column Rtx-5MS capillary 30 mm × 0.25 mm at a film thickness of 0.25 μm with a schedule of temperature increase from 50 °C (1 min) by 10 °C · min−1 to 100 °C, 4 °C · min−1 at 200 °C, and 20 °C · min−1 at 290 °C (maintained for 5 min). The injector was kept at 250 °C and the detector at 280 °C. The carrier gas was He, with a flow rate of 1 mL · min−1. The external calibration was carried out using standard solutions obtained from seven serial dilutions of a working solution containing mixtures of pure analytical standards. Accuracy and precision were assessed by replicate analysis of standards at three concentration levels and replicate analysis of spiked samples at three concentration levels (low, medium, and high) on three validation days. Precision was estimated as the percentage of relative standard deviation (RSD) of replicate standards within one validation batch (intra-day) and between validation batches (inter-day). The results are expressed as the amino acid percentage of the total amino acids.
Mihaly Cozmuta A., Uivarasan A., Peter A., Nicula C., Kovacs D.E, & Mihaly Cozmuta L. (2023). Yellow Mealworm (Tenebrio molitor) Powder Promotes a High Bioaccessible Protein Fraction and Low Glycaemic Index in Biscuits. Nutrients, 15(4), 997.
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Publication 2023
Acetylation acetyl chloride Amino Acids Amino Acids, Acidic Anhydrides Biological Assay Butyl Alcohol Capillaries DNA Replication Dowex Esterification Fever Flame Ionization Gas Chromatography Ion Exchange Resins Nitrogen Proteins Technique, Dilution

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Impact hd 2 mass spectrometer by Bruker
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More about "Amino Acids, Acidic"

Amino Acids, Acidic, also known as Acidic Amino Acids, are a class of organic compounds that contain both an amino group (-NH2) and a carboxyl group (-COOH) where the carboxyl group is attached to an acidic carbon atom.
These include important biomolecules such as glutamic acid and aspartic acid, which play crucial roles in various biological processes.
Researchers studying Amino Acids, Acidic can utilize advanced analytical techniques like the L-8900 amino acid analyzer, which can accurately quantify these compounds.
The L-8900 is a powerful instrument that separates and detects amino acids with high precision.
Additionally, mass spectrometry techniques, such as the Impact HD II and the 6520 Q-TOF LC/MS, can provide detailed structural information and identification of Acidic Amino Acids.
For purification and detection of Acidic Amino Acids, scientists may employ tools like the Anti-FLAG M2 monoclonal antibody, which can selectively bind to and isolate tagged proteins containing these amino acids.
Formic acid is also commonly used as a mobile phase additive in liquid chromatography-mass spectrometry (LC-MS) analysis of Amino Acids, Acidic.
To streamline the research process, researchers can leverage AI-driven comparison platforms like PubCompare.ai.
This tool can help locate relevant protocols from literature, pre-prints, and patents, and provide AI-driven comparisons to identify the best protocols and products for their specific needs.
By utilizing PubCompare.ai, researchers can make more informed decisions and optimize their studies on Amino Acids, Acidic.
Other useful tools and techniques in this field include the Focus XC workstation for protein expression and purification, the Maestro software for molecular modeling and simulation, and the SP6 RNA polymerase for in vitro transcription of genes encoding Acidic Amino Acids.
Milli-Q water is also a common reagent used in the handling and analysis of these compounds.