We used the Ensembl Variant Effect Predictor (VEP, Ensembl Gene annotation v68)16 (link) to obtain gene model annotation for single nucleotide and indel variants. For single nucleotide variants within coding sequence, we also obtained SIFT7 (link) and PolyPhen-26 (link) scores from VEP. We combined output lines describing MotifFeatures with the other annotation lines, reformatted it to a pure tabular format and reduced the different Consequence output values to 17 levels and implemented a four-level hierarchy in case of overlapping annotations (see Supplementary Note ). To the 6 VEP input derived columns (chromosome, start, reference allele, alternative allele, variant type: SNV/INS/DEL, length) and 26 actual VEP output derived columns, we added 56 columns providing diverse annotations (e.g. mapability scores and segmental duplication annotation as distributed by UCSC51 (link),52 (link); PhastCons and phyloP conservation scores53 (link) for three multi-species alignments9 (link) excluding the human reference sequence in score calculation; GERP++ single-nucleotides scores, element scores and p-values54 (link), also defined from alignments with the human reference excluded; background selection score40 (link),55 (link); expression value, H3K27 acetylation, H3K4 methylation, H3K4 trimethylation, nucleosome occupancy and open chromatin tracks provided for ENCODE cell lines in the UCSC super tracks52 (link); genomic segment type assignment from Segway56 (link); predicted transcription factor binding sites and motifs11 (link); overlapping ENCODE ChIP-seq transcription factors11 (link), 1000 Genome variant14 (link) and Exome Sequencing Project57 (link) variant status and frequencies, Grantham scores20 (link) associated with a reported amino acid substitution). The Supplementary Note provides a full description and Supplementary Table 1 lists all columns of the obtained annotation matrix.
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Acetylation
Acetylation
Acetylation is a post-translational modification in which an acetyl group (-COCH3) is added to a molecule, typically a protein.
This process plays a critical role in regulating various cellular processes, such as gene expression, protein function, and signal transduction.
Acetylation can alter the structure, stability, and localization of proteins, influencing their interactions and activity.
Understanding the mechanisms and consequences of acetylation is crucial for researchers studying a wide range of biological and medical phenomena, including epigenetics, metabolism, and disease pathogenesis.
PubCompare.ai, an AI-driven platform, can enhance acetylation research by helping researchers locate the best protocols from literature, pre-prints, and patents, identifiying the most reproducible and accurate methods and optimizing the research process.
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This process plays a critical role in regulating various cellular processes, such as gene expression, protein function, and signal transduction.
Acetylation can alter the structure, stability, and localization of proteins, influencing their interactions and activity.
Understanding the mechanisms and consequences of acetylation is crucial for researchers studying a wide range of biological and medical phenomena, including epigenetics, metabolism, and disease pathogenesis.
PubCompare.ai, an AI-driven platform, can enhance acetylation research by helping researchers locate the best protocols from literature, pre-prints, and patents, identifiying the most reproducible and accurate methods and optimizing the research process.
Expereince the power of PubCompare.ai today to advance your acetylation studies.
Most cited protocols related to «Acetylation»
Acetylation
Alleles
Amino Acid Substitution
Binding Sites
Cell Lines
Chromatin
Chromatin Immunoprecipitation Sequencing
Chromosomes
Gene Annotation
Genome
Homo sapiens
INDEL Mutation
Methylation
Nucleosomes
Nucleotides
Open Reading Frames
Segmental Duplications, Genomic
Transcription, Genetic
Transcription Factor
The sequences of all the PPRs were identified with reference to the 11,938 sequences of Orthohepevirus A (including 338 complete HEV genomes) available in the Virus Pathogen Resource (VIPR) database.5 Selected sequences were systematically searched to identify insertions so that they could be used, together with those identified by PacBio sequencing, for further analysis. The compositions of HEV PPR insertions/duplications were determined and their post-translational modifications predicted by analyzing a range of parameters. Potential ubiquitination sites were identified using the BDM-PUB server6 with a threshold of >0.3 average potential score. Potential phosphorylation sites were identified using the NetPhos 3.1 server7 with a threshold of >0.5 average potential score. Potential acetylation sites were identified using the Prediction of Acetylation on Internal Lysines (PAIL) server8 with a threshold of >0.2 average potential score. Potential N-linked glycosylation sites were identified using the NetNGlyc 1.0 server9 with a threshold of >0.5 average potential score. Potential methylation sites were identified using the BPB-PPMS server10 with a threshold of >0.5 average potential score. Nuclear export signal (NES) sites were identified using the Wregex server11 with parameters NES/CRM1 and Relaxed. Nuclear localization signal (NLS) sites were identified using SeqNLS12 with a 0.86 cut-off. The amino acid composition (proportions of amino acids), physico-chemical composition, and net load were analyzed with R. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in a data set. PCA allows to identify new variables, the principal components, which are linear combinations of the original variables (Ringner, 2008 (link)). PCA was done (excluding the amino acid composition due to redundancy with physico-chemical properties) to summarize and visualize the information on the variables in our data set (Abdi and Williams, 2010 (link)); each variable was then studied independently. An in-house R-pipeline based on the amino acid sequences and the results of each analysis was used to generate bar plots for amino acid composition. The amino acid compositions were assigned to one of two categories: sequences with insertions/duplications (including insertions of human genome and HEV genome duplications) and sequences without insertions/duplications. The other parameters were assigned to one of three categories: sequences with insertions, those with duplications, and sequences without insertion/duplication.
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Acetylation
Amino Acids
Amino Acid Sequence
chemical composition
chemical properties
DNA Insertion Elements
Genome
Genome, Human
Insertion Mutation
Lysine
Methylation
Nuclear Export Signals
Nuclear Localization Signals
Pathogenicity
Phosphorylation
Protein Glycosylation
Sequence Insertion
Ubiquitination
Virus
Acetylation
Cells
Chromatin Immunoprecipitation Sequencing
CTCF protein, human
Deoxyribonuclease I
Enhancer Elements, Genetic
Gene Expression
Genes
Nucleosomes
Plasmids
Acetylation
Amino Acids
Cytokinesis
Electrons
Enzymes
Homo sapiens
Immune Tolerance
Ions
Lysine
Peptides
Trypsin
Establishing a key word library is critically important to retrieve the related literatures. We randomly choose individual compound and checked the full-text review papers to establish this library. Fifty nine keywords are listed in Table 2 , which are frequently used to describe the interaction between compound and proteins. The keywords are divided into two types. One is the nouns describing the interaction (Type A), while the other (Type B) is the phrases describing the specific effect such as inhibit the activity of some proteins.
Keyword library to describe the interaction between herbal ingredients and proteins
| Interaction | Effect | |||
|---|---|---|---|---|
| Positive | Negative | General | ||
| Type A | Agonist; activator | Antagonist; inhibitor | Bind; target; bound | |
| Type B | Activate; Augment; Ameliorate; Derepress; Elevate; Enhance; Hasten; Increase; Induce; Incitate; Initiate Potentiate; Promote; Raise; Stimulate; Up-regulate | Abrogate; Abolish; Against; Attenuate; Antagonize; Block; Blunt; Down regulate; Decrease; Degrade; Diminish; Impair; Inhibit; Reduce; Repress; Suppress | Affect; Interact; Disturb; Regulate; Impact; Influence; Interfere; Modify; Modulate | Activity; Activation; Expression; Level; Pathway; Cleavage; Methylation; Phosphorylation; Severance; Glycosylation; Acetylation |
Acetylation
Cardiac Arrest
cDNA Library
Cytokinesis
Glycosylation
Lanugo
Methylation
Phosphorylation
Proteins
Most recents protocols related to «Acetylation»
Example 8
Characterization of Absorption, Distribution, Metabolism, and Excretion of Oral [14C]Vorasidenib with Concomitant Intravenous Microdose Administration of [13C315N3]Vorasidenib in Humans
Metabolite profiling and identification of vorasidenib (AG-881) was performed in plasma, urine, and fecal samples collected from five healthy subjects after a single 50-mg (100 μCi) oral dose of [14C]AG-881 and concomitant intravenous microdose of [13C3 15N3]AG-881.
Plasma samples collected at selected time points from 0 through 336 hour postdose were pooled across subjects to generate 0—to 72 and 96-336-hour area under the concentration-time curve (AUC)-representative samples. Urine and feces samples were pooled by subject to generate individual urine and fecal pools. Plasma, urine, and feces samples were extracted, as appropriate, the extracts were profiled using high performance liquid chromatography (HPLC), and metabolites were identified by liquid chromatography-mass spectrometry (LC-MS and/or LC-MS/MS) analysis and by comparison of retention time with reference standards, when available.
Due to low radioactivity in samples, plasma metabolite profiling was performed by using accelerator mass spectrometry (AMS). In plasma, AG-881 was accounted for 66.24 and 29.47% of the total radioactivity in the pooled AUC0-72 h and AUC96-336 h plasma, respectively. The most abundant radioactive peak (P7; M458) represented 0.10 and 43.92% of total radioactivity for pooled AUC0-72 and AUC96-336 h plasma, respectively. All other radioactive peaks accounted for less than 6% of the total plasma radioactivity and were not identified.
The majority of the radioactivity recovered in feces was associated with unchanged AG-881 (55.5% of the dose), while no AG-881 was detected in urine. In comparison, metabolites in excreta accounted for approximately 18% of dose in feces and for approximately 4% of dose in urine. M515, M460-1, M499, M516/M460-2, and M472/M476 were the most abundant metabolites in feces, and each accounted for approximately 2 to 5% of the radioactive dose, while M266 was the most abundant metabolite identified in urine and accounted for a mean of 2.54% of the dose. The remaining radioactive components in urine and feces each accounted for <1% of the dose.
Overall, the data presented indicate [14C]AG-881 underwent moderate metabolism after a single oral dose of 50-mg (100 μCi) and was eliminated in humans via a combination of metabolism and excretion of unchanged parent. AG-881 metabolism involved the oxidation and conjugation with glutathione (GSH) by displacement of the chlorine at the chloropyridine moiety. Subsequent biotransformation of GSH intermediates resulted in elimination of both glutamic acid and glycine to form the cysteinyl conjugates (M515 and M499). The cysteinyl conjugates were further converted by a series of biotransformation reactions such as oxidation, S-dealkylation, S-methylation, S-oxidation, S-acetylation and N-dealkylation resulting in the formation multiple metabolites.
A summary of the metabolites observed is included in Table 2
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Acetylation
AG 30
Biotransformation
Chlorine
Dealkylation
Deamination
Elements, Radioactive
Feces
Glutamic Acid
Glutathione
Glycine
Healthy Volunteers
High-Performance Liquid Chromatographies
Homo sapiens
Hydrolysis
Intravenous Infusion
Ions
Liquid Chromatography
Mass Spectrometry
Metabolism
Methylation
Parent
Plasma
Radioactivity
Retention (Psychology)
Tandem Mass Spectrometry
Urinalysis
Urine
vorasidenib
Digoxigenin (DIG)-labeled sense and antisense RNA probes were synthesized from plasmids linearized with restriction enzymes using the DIG RNA Labelling Kit (SP6/T7) (Sigma-Aldrich, St. Louis, MO). After being treated with DNase and EDTA, probes were precipitated with ethanol, dissolved in water, aliquoted, and stored at − 80°C until use. Sections were fixed in 4% (w/v) paraformaldehyde in 0.1 M PB for 10 min at RT, treated with 40 µg/mL proteinase K for 15 min at 37°C, and immersed in 0.1% (v/v) acetic anhydrate in the acetylation buffer for 15 min at RT. Hybridization was performed in the hybridization buffer ISHR7 (Nippon Gene) overnight at 55°C. Post-hybridization washing was performed in formamide/2 × saline-sodium citrate (SSC) for 1 h and 0.1 × SSC for 2 h at 55°C. The sections were incubated with anti-DIG antibody coupled to alkaline phosphatase (Roche Diagnostics, Basel, Switzerland) for 2 h at RT, and color was developed using NBT/BCIP stock solution (Roche) for signal detection.
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Acetylation
Acid Hybridizations, Nucleic
Alkaline Phosphatase
Antibodies, Anti-Idiotypic
Buffers
Deoxyribonucleases
Diagnosis
Digoxigenin
DNA Restriction Enzymes
Edetic Acid
Endopeptidase K
Ethanol
formamide
Genes
paraform
Plasmids
RNA Probes
Saline Solution
Signal Detection (Psychology)
Sodium Citrate
Molecular weight distributions of lyophilized crude EPS were determined by size exclusion chromatography. In brief, crude EPS powder was suspended in 0.1 M NaNO3 (0.5 mg/mL) and then filtered through a 0.45 μm pore diameter polyvinylidene fluoride membrane (Millipore Corporation, USA). The average molecular weight (MW) was determined by high-performance molecular exclusion chromatography (HPLC-SEC, Agilent 1,100 Series System, Hewlett-Packard, Germany) associated with a refractive index (IR) detector (Ibarburu et al., 2015 (link)). 50 μL of the samples were injected and eluted at a flow rate of 0.95 mL/min (pressure: 120:130 psi) at room temperature using 0.1 M NaNO3 as mobile phase. Dextrans (0.5 mg/mL) with a molecular weight between 103 and 2.106 Da (Sigma-Aldrich, USA) were used as standards.
Once the molecular weight distributions were determined, low and high molecular weight fractions that composed the crude EPS obtained at 20°C were separated. For this purpose, EPS solutions (0.2% w/v) were centrifuged through a Vivaspin™ ultrafiltration spin column 100 KDa MWCO, (Sartorious, Goettingen, Germany) for 20 min at 6000 g, eluting only the low MW fraction. Subsequently, high MW fraction retained in the column was eluted using hot distilled water. The eluted fractions were passed through a Vivaspin column (cut-off 30KDa) in order to separate the middle and low MW fraction of EPS.
Monosaccharide composition of crude EPS and their fractions were determined by gas chromatography as previously described (Notararigo et al., 2013 (link)). Briefly, 1–2 mg of EPS were hydrolyzed in 1 mL of 3 M trifluoroacetic acid (1 h at 120°C). The monosaccharides obtained were converted into alditol acetates by reduction with NaBH4 and subsequent acetylation. The samples were analyzed by gas chromatography in an Agilent 7890A coupled to a 5975C mass detector, using an HP5-MS column with helium as carrier gas at a flow rate of 1 mL/min. For each run, 1 μL of sample was injected (with a Split 1:50) and the following temperature program was performed: the oven was heat to 175°C for 1 min; the temperature was increased to 215°C at a rate of 2.5°C/min and then increased to 225°C at 10°C/min, keeping it constant at this temperature for 1.5 min. Monosaccharides were identified by comparison of retention times with standards (arabinose, xylose, rhamnose, galactose, glucose, mannose, glucosamine and galactosamine) analyzed under the same conditions. Calibration curves were also processed for monosaccharide quantification. Myo-inositol was added to each sample as internal standard.
Once the molecular weight distributions were determined, low and high molecular weight fractions that composed the crude EPS obtained at 20°C were separated. For this purpose, EPS solutions (0.2% w/v) were centrifuged through a Vivaspin™ ultrafiltration spin column 100 KDa MWCO, (Sartorious, Goettingen, Germany) for 20 min at 6000 g, eluting only the low MW fraction. Subsequently, high MW fraction retained in the column was eluted using hot distilled water. The eluted fractions were passed through a Vivaspin column (cut-off 30KDa) in order to separate the middle and low MW fraction of EPS.
Monosaccharide composition of crude EPS and their fractions were determined by gas chromatography as previously described (Notararigo et al., 2013 (link)). Briefly, 1–2 mg of EPS were hydrolyzed in 1 mL of 3 M trifluoroacetic acid (1 h at 120°C). The monosaccharides obtained were converted into alditol acetates by reduction with NaBH4 and subsequent acetylation. The samples were analyzed by gas chromatography in an Agilent 7890A coupled to a 5975C mass detector, using an HP5-MS column with helium as carrier gas at a flow rate of 1 mL/min. For each run, 1 μL of sample was injected (with a Split 1:50) and the following temperature program was performed: the oven was heat to 175°C for 1 min; the temperature was increased to 215°C at a rate of 2.5°C/min and then increased to 225°C at 10°C/min, keeping it constant at this temperature for 1.5 min. Monosaccharides were identified by comparison of retention times with standards (arabinose, xylose, rhamnose, galactose, glucose, mannose, glucosamine and galactosamine) analyzed under the same conditions. Calibration curves were also processed for monosaccharide quantification. Myo-inositol was added to each sample as internal standard.
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Acetates
Acetylation
Arabinose
Dextrans
Division Phase, Cell
Galactosamine
Galactose
Gas Chromatography
Gel Chromatography
Glucosamine
Glucose
Helium
High-Performance Liquid Chromatographies
Inositol
Mannose
Monosaccharides
polyvinylidene fluoride
Powder
Pressure
Retention (Psychology)
Rhamnose
Sugar Alcohols
Tissue, Membrane
Trifluoroacetic Acid
Ultrafiltration
Xylose
mTECs were lysed in 0.1% sodium dodecyl sulfate (SDS) in PBS plus 1× HALT protease inhibitor (Thermo Fisher Scientific, 78443), then processed by a multi-protease FASP protocol as described (Wiśniewski and Mann, 2012 (link)). In brief, SDS was removed and proteins were first digested with Lys-C (Wako) and subsequently with Trypsin (Promega) with an enzyme to protein ratio (1:50). 10 μg of Lys-C and Trypsin digests were loaded separately and desalted on C18 Stage tip and eluates were analyzed by high-performance liquid chromatography coupled to a Q-Exactive mass spectrometer as described previously (Farrell et al., 2014 (link)). Peptides and proteins were identified and quantified with the MaxQuant software package, and label-free quantification was performed by MaxLFQ (Cox et al., 2014 (link)). The search included variable modifications for oxidation of methionine, protein N-terminal acetylation, and carbamidomethylation as fixed modification. Peptides with at least seven amino acids were considered for identification. The false discovery rate (FDR), determined by searching a reverse database, was set at 0.01 for both peptides and proteins. All bioinformatic analyses were performed with the Perseus software (Tyanova et al., 2016 (link)). Intensity values were log-normalized, 0-values were imputed by a normal distribution 1.8 π down of the mean and with a width of 0.2 π.
Proteomic expression data were analyzed in R (3.6.0) with the Bioconductor package DEP (1.6.1) (Zhang et al., 2018 (link)). To aid in the imputation of missing values only those proteins that are identified in all replicates of at least one condition were retained for analysis. The filtered proteomic data were normalized by variance stabilizing transformation. Following normalization, data missing at random, such as proteins quantified in some replicates but not in others, were imputed using the k-nearest neighbour approach. For differential expression analysis between the wild-type and mutant groups, protein-wise linear models combined with empirical Bayes statistics were run using the Bioconductor package limma (3.40.6) (Ritchie et al., 2015 (link)). Significantly differentially expressed proteins were defined by an FDR cutoff of 0.05. Total proteomic data are available via ProteomeXchange with identifier PXD031920 and are summarized inSupplementary file 5 .
Proteomic expression data were analyzed in R (3.6.0) with the Bioconductor package DEP (1.6.1) (Zhang et al., 2018 (link)). To aid in the imputation of missing values only those proteins that are identified in all replicates of at least one condition were retained for analysis. The filtered proteomic data were normalized by variance stabilizing transformation. Following normalization, data missing at random, such as proteins quantified in some replicates but not in others, were imputed using the k-nearest neighbour approach. For differential expression analysis between the wild-type and mutant groups, protein-wise linear models combined with empirical Bayes statistics were run using the Bioconductor package limma (3.40.6) (Ritchie et al., 2015 (link)). Significantly differentially expressed proteins were defined by an FDR cutoff of 0.05. Total proteomic data are available via ProteomeXchange with identifier PXD031920 and are summarized in
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Acetylation
Amino Acids
Enzymes
High-Performance Liquid Chromatographies
Methionine
nucleoprotein, Measles virus
Peptide Hydrolases
Peptides
Promega
Protease Inhibitors
Proteins
Sulfate, Sodium Dodecyl
Trypsin
The peptides were synthesized on
an automatic peptide synthesizer (Syro I, Biotage) by using a Rink-amide
resin and Fmoc chemistry. The Fmoc deprotection was carried out with
25% piperidine in DMF/NMP (70:30, v/v) for 3 min and 12.5% piperidine
in DMF/NMP (70:30, v/v) for 12 min. The couplings were accomplished
with the mixture Fmoc-AA-OH/HOBt/HBTU/DIPEA (5:5:4.8:10 equiv) for
2 × 40 min. N-terminal acetylation was performed manually with
acetic anhydride/DIPEA (10:10 equiv) in DMF for 30 min. The peptides
were cleaved from the resin with TFA/H2O/TIA/EDT/TIS (90:1:3:3:3; Vtot = 1 mL) for about 3 h, precipitated by ice-cold
diethyl ether, and recovered by centrifugation at 4 °C for 5
min. The homogeneity and identity of the lyophilized peptides were
assessed by analytical HPLC (Thermo Fisher Scientific) and MALDI-TOF-MS
(Bruker Daltonics) (Figures S20 and S21 and Table S7 ).
an automatic peptide synthesizer (Syro I, Biotage) by using a Rink-amide
resin and Fmoc chemistry. The Fmoc deprotection was carried out with
25% piperidine in DMF/NMP (70:30, v/v) for 3 min and 12.5% piperidine
in DMF/NMP (70:30, v/v) for 12 min. The couplings were accomplished
with the mixture Fmoc-AA-OH/HOBt/HBTU/DIPEA (5:5:4.8:10 equiv) for
2 × 40 min. N-terminal acetylation was performed manually with
acetic anhydride/DIPEA (10:10 equiv) in DMF for 30 min. The peptides
were cleaved from the resin with TFA/H2O/TIA/EDT/TIS (90:1:3:3:3; Vtot = 1 mL) for about 3 h, precipitated by ice-cold
diethyl ether, and recovered by centrifugation at 4 °C for 5
min. The homogeneity and identity of the lyophilized peptides were
assessed by analytical HPLC (Thermo Fisher Scientific) and MALDI-TOF-MS
(Bruker Daltonics) (
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1-hydroxybenzotriazole
Acetylation
Anhydrides
Centrifugation
DIPEA
Ethyl Ether
High-Performance Liquid Chromatographies
Peptides
piperidine
Resins, Plant
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
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