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Triglycine sulfate

Triglycine sulfate is a chemical compound consisting of three glycine molecules linked together with a sulfate group.
It is commonly used in biochemical research and applications, particularly in the study of protein structure and function.
This compound can be utilized in various experimental protocols, such as protein crystallization, spectroscopic analyses, and biophysical characterization.
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Most cited protocols related to «Triglycine sulfate»

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Publication 2010
Amides Esocidae Eye Nitrogen Proteins Radionuclide Imaging Reflex Silk Spectroscopy, Fourier Transform Infrared triglycine sulfate Tropoelastin Vertebral Column Water Vapor
Study design. The aim of this study was to identify features of CD8+ T cell responses to SARS-CoV-2 associated with disease state and HLA genetics, including immunodominant T cell epitopes, evidence of immune recall, and shared TCR sequence motifs. We used libraries of peptide-HLA tetramers with epitopes derived from across the SARS-CoV-2 proteome presented in four HLAs with high prevalence in North America. Samples from acute and convalescent patients, with HLAs matching the tetramer libraries, were acquired as they became available and screened in several batches alongside samples from unexposed subjects. A total of 27 acute, 28 convalescent, and 23 unexposed subjects were screened providing HLA-matched analysis for 43 A*02:01, 18 A*24:02, 17 B*07:02, and 9 A*01:01 samples.
Antigen library design. Antigenic peptide libraries were designed by scoring all possible 9mer peptides derived from the entire SARS-CoV-2 proteome (NC_045512.2) using netMHC-4.0 (32 (link)) in the HLA-A*02:01, HLA-A*01:01, HLA-A*24:02 or HLA-B*07:02 alleles. SARS-CoV-1 peptides that had evidence of T cell positive assays, obtained from the Immune Epitope Database (www.iedb.org; (51 (link))), and that were highly homologous to their SARS-CoV2 counterparts within hamming-distance of 2 were converted to 9-mers. Additionally, SARS-CoV-2 peptides predicted to raise immunogenic responses by others were also included (52 , 53 (link)). Finally, libraries included a set of well-defined viral epitopes from Cytomegalovirus, Epstein-Barr virus, and Influenza viruses (CEF peptide pool) that elicit T cell responses in the population at large. Antigenic peptides with 500 nM affinity or lower were then selected for inclusion (Data file S8).
Production of tetramer library pools. HLA-A*01:01, -A*02:01, -A*24:02 and HLA-B*07:02 extracellular domains were expressed in E. coli and refolded along with beta-2-microglobulin and ultraviolet (UV)-labile place-holder peptides STAPGJLEY, KILGFVFJV, VYGJVRACL and AARGJTLAM, respectively (54 (link)). A C-terminal sortase recognition sequence on the HLA was modified by sortase transpeptidation (55 (link), 56 ) with a synthetic alkynylated linker peptide, featuring an N-terminal triglycine connected to propargylglycine via a PEG linker (Genscript, Piscataway, NJ). The modified HLA monomer was then purified by size exclusion chromatography (SEC). Full-length streptavidin with an N-terminal Flag tag and a C-terminal sortase recognition sequence and 6xHisTag was prepared by expression and purification from E. coli using immobilized metal affinity chromatography and SEC. Streptavidin was modified by sortase transpeptidation with a synthetic azidylated linker peptide, featuring an N-terminal triglycine connected to picolyl azide via a PEG linker (Click Chemistry Tools, Scottsdale, AZ). HLA tetramers were produced by mixing alkynylated HLA monomers and azidylated streptavidin in 0.5 mM copper sulfate, 2.5 mM BTTAA (2-(4-((Bis((1-(tert-butyl)-1H-1,2,3-triazol-4-yl)methyl)amino)methyl)-1H-1,2,3-triazol-1-yl)acetic acid) and 5 mM ascorbic acid for up to 4 hours on ice, followed by purification of highly multimeric fractions by SEC. Individual peptide exchange reactions containing 500 nM HLA tetramer and 60 uM peptide were exposed to long-wave UV (366 nm) at a distance of 2-5 cm for 30 min at 4°C, followed by 30 min incubation at 30°C. A biotinylated oligonucleotide barcode (Integrated DNA Technologies) was added to each individual reaction followed by 30 min incubation at 4°C. Individual tetramer reactions were then pooled and concentrated using 30 kDa molecular weight cut-off centrifugal filter units (Amicon). Tetramer production was quality controlled using SEC (Fig. S1a), sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) (Fig. S1b), and UV-mediated peptide exchange by assessing binding to peptide-expanded cell lines (Fig. S2).
Patient Samples. Peripheral blood mononuclear cells (PBMCs) from COVID-19 positive donors or unexposed donors were obtained from Precision 4 Medicine (USA), the Massachusetts Consortium on Pathogen Readiness (MassCPR, Boston, USA), or CTL (USA), all under appropriate informed consent. Patients were defined COVID-19 positive based on positive SARS-CoV-2 real-time reverse transcriptase–polymerase-chain-reaction (RT-PCR) using nasopharyngeal swabs. Patient samples were characterized as “acute” if collected while the patient was hospitalized and as “convalescent” if collected after recovery or when presenting mild disease. Samples from unexposed subjects were collected prior to December, 2019. A summary of patient samples used in this study are presented in Data file S2.
Cell Staining. PBMCs were thawed, and CD8+ T cells were enriched by magnetic-activated cell sorting (MACS) using a CD8+ T Cell Isolation Kit (Miltenyi) following the manufacturers protocol. The CD8+ T cells were then stained with tetramer libraries (Data file S8), matched to subject HLAs (Data file S2) and at 1nM final concentration for each member, in the presence of 2 mg/mL salmon sperm DNA in PBS with 0.5% BSA solution for 20 min. Cells were then labeled with anti-TCR antibody-derived tag (ADT, clone IP26, Biolegend, CA, USA) for 15 min followed by washing. Tetramer bound cells were then labeled with phycoerythrin (PE) conjugated anti-DKDDDDK-Flag antibody (BioLegend, CA, USA) followed by dead cell discrimination using 7-amino-actinomycin D (7-AAD). The live, tetramer positive cells were sorted (Fig. S3) using a Sony MA900 Sorter (Sony). When necessary, sorting gates were set liberally to enable sufficient cell recovery for single-cell sequencing.
Sample multiplexing. To ensure sufficient cell loading and subsequent cDNA production in single-cell sequencing, we used sample multiplexing for several experiments. When applied, samples were independently stained with tetramer libraries, labeled using custom anti-TCR ADTs with unique 15 base pair DNA barcodes (clone IP26, BioLegend, CA, USA), and sorted. ADT-labeled, sorted samples were combined prior to encapsulation and single-cell sequencing. In several cases, an expanded T cell line (Cellero Anti-MART-1, MA, USA) was labeled with a BV785 anti-CD8 antibody (BioLegend, CA, USA), stained using a tetramer for ELAGIGILTV in A*02:01, and subsequently mixed and co-sorted alongside samples interrogated for this study. This provided confirmation of tetramer staining, guidance for gating, and verification of the multiplexing strategy (Fig. S3). The anti-MART-1 T cells (TCR sequences provided in Data file S9) were excluded from any subsequent analyses.
Single-cell Sequencing. Tetramer positive cells were counted by Nexcelom Cellometer (Lawrence, MA, USA) using AOPI stain following manufacturer’s recommended conditions. When possible, 15,000 cells were targeted for encapsulation. Single-cell encapsulations were generated utilizing 5′ v1 Gem beads from 10x Genomics (Pleasanton, CA, USA) on a 10x Chromium controller and downstream TCR, Gene Expression, and Surface marker libraries were made following manufacturer recommended conditions. All libraries were quantified on a BioRad CFX 384 (Hercules, CA, USA) using Kapa Biosystems (Wilmington, MA, USA) library quantified kits and pooled at an equimolar ratio. TCRs, Gene Expression, surface markers, and tetramer generated libraries were sequenced on Illumina (San Diego, CA, USA) NextSeq550 instruments. Sequencing data were processed using the Cell Ranger Software Suite (Version 3). Samples were demultiplexed and unique molecular identifier (UMI) counts were quantified for TCRs, tetramers, and gene expression.
Single-cell Transcriptomic Analysis. Hydrogel-based RNA-seq data were analyzed using the Cell Ranger package from 10X Genomics (v3.1.0) with the GRCh38 human expression reference (v3.0.0). Except where noted, Scanpy (v1.6.0 (57 (link))) was used to perform the subsequent single cell analyses. Any exogenous control cells identified by TCR clonotype were removed before further gene expression processing. Hydrogels that contain UMIs for less than 300 genes were excluded. Genes that were detected in less than 3 cells were also excluded from further analysis. Several additional quality control thresholds were also enforced. To remove data generated from cells likely to be damaged, upper thresholds were set for percent UMIs arising from mitochondrial genes (13%). To exclude data likely arising from multiple cells captured in a single drop, upper thresholds were set for total UMI counts based on individual distributions from each encapsulation (from 1500 to 3000 UMIs). A lower threshold of 10% was set for UMIs arising from ribosomal protein genes. Finally, an upper threshold of 5% of UMIs was set for the MALAT1 gene. Any hydrogel outside of any of the thresholds was omitted from further analysis. A total of 15,683 hydrogels were carried forward. Gene expression data were normalized to counts per 10,000 UMIs per cell (CP10K) followed by log1p transformation: ln(CP10K + 1).
Highly variable genes were identified (1,567) and scaled to have a mean of zero and unit variance. They were then provided to scanorama (v1.7, (58 (link))) to perform batch integration and dimension reduction. The data were used to generate the nearest neighbor graph which was in turn used to generate a UMAP representation that was used for Leiden clustering. The hydrogel data (not scaled to mean zero, unit variance, and before extraction of highly variable genes) were labeled with cluster membership and provided to SingleR (v1.4.0, (59 (link))) using the following references from Celldex (v1.0.0, (59 (link))): Monaco Immune Data, Database Immune Cell Expression Data, and Blueprint Encode Data. SingleR was used to annotate the clusters with their best-fit match from the cell types in the references. Clusters that yielded cell types other than types of the T Cell lineage were removed from consideration and the process was repeated starting from the batch integration step. The best-fit annotations from SingleR after the second round of clustering and the annotation was assigned as putative labels for each Leiden cluster. Further clustering of transcriptomic data was performed across the genes shown in Fig. 5 using KMeans in sklearn (v0.24) with n_clusters set to 8. As the method has a preference to assign like-sized clusters, further consolidation of two central memory clusters was performed.
In order to provide corroboration for the SingleR best-fit annotations and further evidence as to the phenotype of the clusters, gene panels representing functional categories (Naïve, Effector, Memory, Exhaustion, Proliferation) were used to score each hydrogel’s expression profiles using scanpy’s “score_genes” function (57 (link)) which compares the mean expression values of the target gene set against a larger set of randomly chosen genes that represent background expression levels. The gene panels for each class were: Naïve - TCF7, LEF1, CCR7; Effector - GZMB, PRF1, GNLY; Memory - AQP3, CD69, GZMK; Exhaustion - PDCD1, TIGIT, LAG3; Proliferation - MKI67, TYMS. The gene expression matrix for all hydrogels were first imputed using the MAGIC algorithm (v2.0.4, (60 (link)). These functional scores were the only data generated from imputed expression values.
Scoring peptide-HLA-TCR interactions. Tetramer data analysis was performed using built-in methods of pandas (v1.2.5) and numpy (v1.20.3) in Python (v3.7.3). For each single-cell encapsulation, tetramer UMI counts (columns) were matrixed by cell (rows) and log-transformed. Duplicates of this matrix were independently Z-score transformed by row or column, and subsequently median-centered by the opposite axis (column or row), respectively (Fig. S7). For each peptide-HLA-cell interaction, this provided two scores - inter-tetramer ( Ztet ) and inter-cell ( Zcell ), which were used to calculate a classifier for unique CDR3 a/b clonotypes across N cells as N×Z¯tet×Z¯cell . Classifier thresholds for positive interactions were set at 40, 36, 50, and 65 for A*02:01, B*07:02, A*24:02, and A*01:01, respectively.
Frequency Calculation. The frequency of reactive T cells in parent CD8+ T cell populations was estimated using a calculation of compounded frequency by taking the product of the fraction of reactive cells in the sorted population and the fraction of cells sorted (Fig. S8). When sample multiplexing was applied, care was taken to include only de-multiplexed cells from the corresponding sample to determine reactive cell fraction.
TCR Network Analysis. TCR motif analysis was performed using scirpy (v0.6.1) with receptor_arms = “any,” metric = “alignment,” and default cutoff of 10. Once clusters were identified, sequence alignment was performed using the pairwise2 module in Biopython (v1.78) and visualized using logomaker (v0.8).
Recombinant TCR validation. Recombinant TCRs identified from patient samples were ordered from TWIST Biosciences in the pLVX-EF1a lentiviral backbone (Takara) as a bicistronic TCRb-T2A-TCRa vector. Viral supernatants from transfected HEK 293T cells were collected 48 and 72 hours after transfection and added to the parental TCRab−/− Jurkat J76 cell like (34 (link)) expressing CD8 and a nuclear factor of activated T cells (NFAT)-green fluorescent protein (GFP) reporter, referred to as J76-CD8-NFAT-GFP. Recombinant TCR surface expression was confirmed through flow cytometry by staining transduced J76-CD8-NFAT-GFP cells with anti-CD3-PE (Clone UCHT1) and anti-TCRab-allophycocyanin (APC) antibodies (Clone IP26).
To assess functional activity of recombinant TCRs, J76-CD8-NFAT-GFP expressing recombinant TCRs were incubated at a 1:1 ratio with the HLA-A*02:01+and HLA-B*07:02+ HCC 1428 BL (ATCC CRL-2327) lymphoblastic cell line, with a final concentration of 0.5% dimethylsulfoxide (DMSO, vehicle) or 50 uM of cognate peptide (New England Peptide, >95% pure). Cell mixtures were incubated in the Sartorius IncuCyte at 37°C, 5% CO2 overnight and analyzed for NFAT-GFP expression measured as total integrated intensity (GCU x mm2/image) at 12 hours after assay setup. At 16 hours, cells were removed from the IncuCyte and subsequently washed and blocked with staining buffer (BD 554656), stained with anti-CD3-PE-Cy7 (Clone UCHT1) and anti-CD69-APC (Clone FN50) antibodies, and analyzed using the Intellicyt iQue Screener Plus and FlowJo v10. CD69 activity was measured as percent positive of CD3+ cells.
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Publication 2021
NB cultured at 37°C in cell culture conditions for 2 weeks were scraped from the flasks, centrifuged using a L8-80M ultracentrifuge (Beckman, Fullerton, CA, USA) at 20,000×g for 1 hour. The samples were washed twice with double-distilled water using the same centrifugation step. NLP prepared as mentioned above were centrifuged at 16,000×g for 10 minutes and washed twice with double-distilled water. The washed pelleted particles were dried in a vacuum centrifuge for 15 minutes at room temperature. The dried powder was mixed 1∶100 (w/w) with KBr and compressed into a 1.3 cm diameter pellet using a hand press. FTIR spectra were obtained using a Nicolet 5700 FTIR spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a deuterated triglycine sulfate (DTGS) detector. Spectra were obtained at a resolution of 4 cm−1 at wavelengths spanning from 4000 to 400 cm−1, each time averaging 32 scans. The following commercial reagents were used for FTIR as well as the other analyses: calcium carbonate (A.C.S. grade reagent, purity 99.6%, Mallinckrodt Baker, Inc., Phillipsburg, NJ), calcium phosphate tribasic (Kanto Chemical Co., Tokyo, Japan) and HAP (buffered aqueous suspension, 25% solid, Sigma).
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Publication 2009
Carbonate, Calcium Cell Culture Techniques Centrifugation Powder Radionuclide Imaging Spectroscopy, Fourier Transform Infrared tricalcium phosphate triglycine sulfate Vacuum
Synthesis of mesoporous zinc oxide (ZnO) structures. Mesoporous ZnO samples (3 mg each) were prepared by following a two-step synthetic approach, as previously described [7 (link)]. In the first step, a metallic Zn layer was deposited at room temperature on silicon (Si) substrates (~1 cm2 area) by radio-frequency magnetron sputtering for an overall deposition time of 4 h. Then, thermal oxidation of the Zn-coated Si samples was performed in a muffle furnace at 380 °C (ramp rate 150 °C/h) in air for 2 h. Before Zn deposition, the Si substrates were cleaned in an ultrasonic bath of acetone and ethanol (10 min for each washing cycle) and dried under nitrogen flow.
Gentamicin sulfate (GS) adsorption and release experiments. GS powders were dissolved either in bidistilled water or simulated body fluid (SBF) at room temperature, under continuous stirring conditions (360 rpm) for 30 min. SBF salt solution was prepared according to Kokubo’s protocol [58 ]. Both GS/H2O and GS/SBF uptake solutions had a final concentration of 250 μg/mL. Adsorption of GS was performed at room temperature by soaking the mesoporous ZnO/Si samples for different times (1 h, 2 h, 5 h and 24 h) in a plastic tube filled with the uptake solution (5 mL), under orbital shaking conditions (160 rpm). After GS uptake, all the samples were washed with bidistilled water and air-dried overnight.
GS release experiments were carried out by soaking the GS-ZnO/Si samples in a plastic tube filled with SBF (10 mL), in orbital conditions (160 rpm) at 37 °C for up to seven days. At specific points of time (5 min, 15 min, 30 min, 1 h, 2 h, 4 h, 6 h, 24 h, 48 h, 72 h, 7 days), 350 μL aliquot was collected from the release solution, centrifuged at 20,000× g for 5 min and analyzed by UV-Vis spectroscopy. The drug release profile was then obtained by considering the characteristic GS absorbance peak at 251 nm. This was compared with a calibration curve obtained from the UV absorbance values at 251 nm of a series of GS dilutions in SBF (from 5 to 1000 μg/mL). The amount of drug at each release time wt was obtained according to the following equation:
where Ct is the concentration of the solution collected at each release time ti (being i an integer = 1, 2, …, n), while Vt is the residual volume of the release solution at that time, i.e., the starting release volume (10 mL) depleted at each time point ti of a fixed volume (ΔVi = 350 μL). The cumulative GS release profile was then obtained according to this equation:
where w0 is the starting amount of GS loaded on the mesoporous matrix.
Sample characterization. The morphology of the samples was evaluated by means of Field-Emission Scanning Electron Microscope (FESEM, Supra®40, Carl Zeiss AG, Oberkochen, Germany). X-ray Diffraction measurements were performed by a Panalytical X’Pert PRO diffractometer in Bragg-Brentano configuration, equipped with a Cu Kα monochromatic radiation (λ = 1.54059 Å) as X-ray source. The chemical composition was investigated by Energy Dispersive Spectroscopy (EDS), using a desktop SEM Phenom XL (Phenom-World B.V. part of Thermo Fisher Scientific, Eindhoven, The Netherlands) equipped with an EDS analyzer. UV-Vis absorbance spectra were collected in the range 200–285 nm, by means of a double-beam Varian Cary 5000 UV-vis-NIR spectrophotometer (Milan, Italy). UV analysis of drug solutions were carried out in a quartz cuvette, with an optical path length of 1 mm, analyzing a volume of 350 μL. All of the UV spectra were background subtracted. IR spectroscopy was carried out with a Nicolet 5700 FTIR Spectrometer (ThermoFisher, Waltham, MA, USA), equipped with a room temperature working L-alanine doped triglycine sulfate (DLaTGS) detector. All of the spectra were background subtracted and acquired with 2 cm−1 resolution and 64 scans accumulation. The release profile for Zn2+ element was determined with an Inductively Coupled Plasma Mass Spectrometer analyzer (ICP-MS, mod. 7500cc, Agilent Technologies, Milan, Italy).
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Publication 2018
Recombinant mouse prion protein fragment (rMoPrP(89-230)) used in this study was purified and stored as described previously (Milto, Michailova & Smirnovas, 2014 (link)). Protein grade guanidine hydrochloride (GuHCl) was purchased from Carl Roth GmbH, guanidine thiocyanate (GuSCN) and other chemicals were purchased from Fisher Scientific UK.
To prepare different fibril strains, monomeric protein from a stock solution was diluted to a concentration of 0.5 mg/mL in 50 mM phosphate buffer (pH 6) containing 2 M or 4 M GuHCl, and incubated for one week at 37 °C with 220 rpm shaking (in shaker incubator IKA KS 4000i). For seeding experiments rPrP-A4M fibrils were treated for 10 min using Bandelin Sonopuls 3100 ultrasonic homogenizer equipped with MS72 tip (using 20% power, cycles of 30 s/30 s sonication/rest, total energy applied to the sample per cycle—0.36 kJ). The sample was kept on ice during the sonication. Right after the treatment, fibrils were mixed with 0.5 mg/ml of mouse prion solution in 2 M GuHCl in 50 mM phosphate buffer, pH 6, containing 50 µM ThT. Elongation kinetics at 60 °C temperature was monitored by ThT fluorescence assay (excitation at 470 nm, emission at 510 nm) using Qiagen Rotor-Gene Q real-time analyzer (Milto, Michailova & Smirnovas, 2014 (link)). ThT fluorescence curves were normalized by dividing each point by the maximum intensity of the curve.
For denaturation assays, amyloid fibrils were resuspended to a concentration of 25 µM in 50 mM phosphate buffer, pH 6, containing 0.5 M GuSCN and homogenized by sonication (same way as in preparation of seeds). These solutions were diluted 1:4 in a buffer containing varying concentrations of GuSCN, and incubated for 60 min at 25 °C in Maxymum Recovery™ microtubes (Axygen Scientific, Inc., Union City, California, USA). 150 µL of samples were mixed with 850 µL of 100 mM phosphate buffer, pH 7, containing ThT (final concentration after dilution was 50 µM), then each mixture was sonicated for 15 s (same conditions as described above). Fluorescence was measured at 480 nm using the excitation wavelength of 440 nm. Denaturation curves were normalized by dividing each point by the average intensity of the points in the plateau region. Fractional loss of signal at increasing denaturant concentrations corresponds to the fraction of rPrP dissociated from amyloid fibrils.
For AFM experiments, 30 µL of the sample were deposited on freshly cleaved mica and left to adsorb for 1 min, the sample was rinsed with several mL of water and dried gently using airflow. AFM images were recorded in the Tapping-in-Air mode at a drive frequency of approximately 300 kHz, using a Dimension Icon (Bruker, Santa Barbara, California, USA) scanning probe microscope system. Aluminium-coated silicon tips (RTESPA-300) from Bruker were used as a probe.
To prepare samples for the FTIR measurements, rMoPrP aggregates were separated from the buffer by centrifugation (30 min, 15,000 g), and resuspended in D2O, sedimentation and resuspension was repeated three times to minimize the amount of GuHCl and H2O. After resuspension samples were homogenized by 1 min sonication (same conditions as described above). The FTIR spectra were recorded using Bruker Alpha spectrometer equipped with deuterium triglycine sulfate (DTGS) detector. For all measurements, CaF2 transmission windows and 0.1 mm Teflon spacers were used. Spectra were recorded at room temperature. For each spectrum, 256 interferograms of 2 cm−1 resolution were co-added. A corresponding buffer spectrum was subtracted from each sample spectrum. All the spectra were normalized to the same area of amide I/I’ band. All data processing was performed using GRAMS software.
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Publication 2015

Most recents protocols related to «Triglycine sulfate»

The Fourier transform infrared (FTIR) spectra were recorded using attenuated total reflection (ATR) in transmission mode with a ThermoFisher Scientific (Waltham, MA, USA) Nicolet iS50 FT-IR Flex Gold spectrometer equipped with a deuterated triglycine sulfate (DTGS) detector. The characteristic IR absorption bands are reported in cm−1.
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Publication 2024
The FTIR spectra of the lipopeptides
were recorded using a Thermo-Scientific Nicolet iS5 instrument with
a deuterated triglycine sulfate (DTGS) detector, with a Specac Pearl
liquid cell containing CaF2 plates, where the sample was
fixed. A total of 128 scans for each sample were recorded over the
range of 900–4000 cm–1.
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Publication 2024

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Publication 2024
TPUs were analyzed by ATR-FTIR (NicoletTM iS50, Thermo Fisher Scientific) before and after HME, as well as hot pressing. Spectra was acquired at 4000–525 cm−1 wavenumber with 4 cm−1 spectral resolution at room temperature. 16 co-added scans were averaged. Potassium bromide (KBr) and deuterated-triglycine sulfate (DTGS) were used as beam splitter and detector, respectively.
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Publication 2024
Attenuated total reflectance Fourier-transform infrared (ATR FT-IR) spectroscopy was used to generate mid-IR (4000–400 cm−1) and far-IR (700–100 cm−1) spectra of the compounds. Mid-IR spectra were collected with a Bruker Alpha FT-IR spectrometer, which was equipped with a single-bounce diamond ATR crystal, a deuterated triglycine sulfate (DTGS) detector, and a SiC globar source. Far-IR spectra were measured with a Nicolet 6700 FT-IR spectrometer. This instrument is equipped with a deuterated lanthanum α alanine-doped triglycine sulfate (DLaTGS) detector covered with a polyethylene window and an EverGlo IR source. Samples for far-IR analysis were deposited onto a Harrick DiaMAX diamond ATR accessory. Mid- and far-IR spectra were collected at 1 cm−1 resolution. FT–Raman spectroscopy was performed with a Thermo NXR 9610 Raman spectrometer. Each sample was transferred to a quartz NMR tube and sealed inside an argon-filled glove box. The samples were then measured in a 180° backscattering geometry with a 976 nm excitation laser set to 1.0 W. The spectral resolution was 4 cm−1. All of the vibrational spectra were measured at room temperature and ambient pressure. Spectral deconvolution was performed with Fityk (version 1.3.1) [79 (link)].
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Publication 2024

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More about "Triglycine sulfate"

Triglycine sulfate, also known as tris-glycine sulfate, is a chemical compound consisting of three glycine molecules linked together with a sulfate group.
This compound is widely used in biochemical research and applications, particularly in the study of protein structure and function.
One of the key applications of triglycine sulfate is in protein crystallization experiments.
Researchers can utilize this compound to optimize the conditions for protein crystal growth, which is essential for determining the 3D structure of proteins using techniques like X-ray crystallography.
Triglycine sulfate can also be employed in spectroscopic analyses, such as Fourier-transform infrared (FTIR) spectroscopy, using instruments like the Vertex 70 FTIR spectrometer, Nicolet iS50 FTIR spectrometer, Nicolet 6700 spectrometer, and the Alpha FTIR spectrometer.
These techniques allow scientists to study the secondary structure and other properties of proteins in the presence of triglycine sulfate.
Additionally, triglycine sulfate is used in biophysical characterization experiments, where it can help researchers understand the stability, folding, and interactions of proteins.
This information is crucial for understanding protein function and developing new therapeutic agents.
To optimize their triglycine sulfate research, scientists can leverage the AI-driven platform PubCompare.ai.
This platform helps identify the most accurate and reproducible protocols from the scientific literature, preprints, and patents, allowing researchers to make data-driven decisions and improve the quality and reproducibility of their studies.
By utilizing PubCompare.ai, researchers can access the most up-to-date and reliable information on triglycine sulfate applications, ultimately enhancing their understanding of protein structure and function.