Protein Subunits
These subunits can include peptides, polypeptides, or other macromolecules that assemble into complex protein complexes.
Understanding the composition and interactions of protein subunits is crucial for studying protein structure, function, and regulation.
Researchers can leverage AI-driven tools like PubCompare.ai to optimize their protein subunit research by easily finding and comparing protocols from literature, preprints, and patents.
This can help identify the most accurate and effective approaches, enhancing research accuracy and efficieny.
Experince the future of protein subunit research today with PubCompare.ai's powerful AI capabilities.
Most cited protocols related to «Protein Subunits»
The source database providing the seed alignment, required for the incremental alignment process, included a representative set of 51 601 aligned rRNA sequences from Bacteria, Archaea and Eukarya with 46 000 alignment positions. The SSU alignment positions are currently kept identical with the ssu_jan04.arb database which has officially been released by the ARB project (
Both CellChat and CellPhoneDB, but not SingleCellSignalR, and iTALK, consider multi-subunit structure of ligands and receptors to represent heteromeric complexes accurately. To evaluate the effect of neglecting multi-subunit structure of ligands and receptors, we compute false positive rates for the tools that use only one ligand and one receptor gene pairs. The false positive interactions are defined by the interactions with multi-subunits that are partially identified by iTALK and SingleCellSignalR. The ground truth of the interactions with multi-subunits is based on our curated CellChatDB database. For example, for Tgfb1 ligand and its heteromeric receptor Tgfbr1/Tgfbr2 curated in CellChatDB, if the method only identifies one of the two pairs (Tgfb1–Tgfbr1 and Tgfb1–Tgfbr2), then we consider this prediction as one false positive interaction.
We performed subsampling of scRNA-seq datasets using a ‘geometric sketching’ approach, which maintains the transcriptomic heterogeneity within a dataset with a smaller subset of cells96 (link). We evaluated the robustness of inferred interactions from subsampled datasets using three measures, including TPR, FPR, and ACC, which were defined in Supplementary Note
The rRNA Parc databases are a collection of all quality checked and automatically aligned rRNA sequences longer than 300 bases of the aligned rRNA gene (field ‘nuc_gene_slv’,
Most recents protocols related to «Protein Subunits»
Example 20
The instant study is designed to test the immunogenicity in rabbits of candidate betacoronavirus (e.g., MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1 or a combination thereof) vaccines comprising a mRNA polynucleotide encoding the spike (S) protein, the S1 subunit (S1) of the spike protein, or the S2 subunit (S2) of the spike protein obtained from a betacoronavirus (e.g., MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1).
Rabbits are vaccinated on week 0 and 3 via intravenous (IV), intramuscular (IM), or intradermal (ID) routes. One group remains unvaccinated and one is administered inactivated betacoronavirus. Serum is collected from each rabbit on weeks 1, 3 (pre-dose) and 5. Individual bleeds are tested for anti-S, anti-S1 or anti-S2 activity via a virus neutralization assay from all three time points, and pooled samples from week 5 only are tested by Western blot using inactivated betacoronavirus (e.g., inactivated MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1).
In experiments where a lipid nanoparticle (LNP) formulation is used, the formulation may include a cationic lipid, non-cationic lipid, PEG lipid and structural lipid in the ratios 50:10:1.5:38.5. The cationic lipid is DLin-KC2-DMA (50 mol %) or DLin-MC3-DMA (50 mol %), the non-cationic lipid is DSPC (10 mol %), the PEG lipid is PEG-DOMG (1.5 mol %) and the structural lipid is cholesterol (38.5 mol %), for example.
Example 2
The next experiments asked whether inhibition of the same set of FXN-RFs would also upregulate transcription of the TRE-FXN gene in post-mitotic neurons, which is the cell type most relevant to FA. To derive post-mitotic FA neurons, FA(GM23404) iPSCs were stably transduced with lentiviral vectors over-expressing Neurogenin-1 and Neurogenin-2 to drive neuronal differentiation, according to published methods (Busskamp et al. 2014, Mol Syst Biol 10:760); for convenience, these cells are referred to herein as FA neurons. Neuronal differentiation was assessed and confirmed by staining with the neuronal marker TUJ1 (
It was next determined whether shRNA-mediated inhibition of FXN-RFs could ameliorate two of the characteristic mitochondrial defects of FA neurons: (1) increased levels of reactive oxygen species (ROS), and (2) decreased oxygen consumption. To assay for mitochondrial dysfunction, FA neurons an FXN-RF shRNA or treated with a small molecule FXN-RF inhibitor were stained with MitoSOX, (an indicator of mitochondrial superoxide levels, or ROS-generating mitochondria) followed by FACS analysis.
Mitochondrial dysfunction results in reduced levels of several mitochondrial Fe-S proteins, such as aconitase 2 (ACO2), iron-sulfur cluster assembly enzyme (ISCU) and NADH:ubiquinone oxidoreductase core subunit S3 (NDUFS3), and lipoic acid-containing proteins, such as pyruvate dehydrogenase (PDH) and 2-oxoglutarate dehydrogenase (OGDH), as well as elevated levels of mitochondria superoxide dismutase (SOD2) (Urrutia et al., (2014) Front Pharmacol 5:38). Immunoblot analysis is performed using methods known in the art to determine whether treatment with an FXN-RF shRNA or a small molecule FXN-RF inhibitor restores the normal levels of these mitochondrial proteins in FA neurons.
Example 23
The instant study was designed to test the immunogenicity in mice of candidate MERS-CoV vaccines comprising a mRNA polynucleotide encoding the full-length Spike (S) protein, or the S2 subunit (S2) of the Spike protein obtained from MERS-CoV.
Mice were vaccinated with a 10 μg dose of MERS-CoV mRNA vaccine encoding either the full-length MERS-CoV Spike (S) protein, or the S2 subunit (S2) of the Spike protein on days 0 and 21. Sera were collected from each mice on days 0, 21, 42, and 56. Individual bleeds were tested for anti-S, anti-S2 activity via a virus neutralization assay from all four time points.
As shown in
to the most important features of the structure of SARS-CoV-2. The
four major structural proteins are displayed: the envelope (E), membrane
(M), nucleocapsid (N), and spike (S) proteins (
kDa) is the surface glycoprotein anchored to the viral membrane that
plays an essential role when the infection process of SARS-CoV-2 takes
place. This protein is a trimer of three identical protomers (
contains three segments: a short intracellular tail (IC), a transmembrane
anchor (TM), and a large ectodomain that extends outward from the
virus which is coated with sugar chains to hide the virus from the
immune system8 (link) and comprises S1 and S2
subunits.
Next, the students are invited to study the ectodomain by analyzing
the requested structural features that they must observe manipulating
PyMOL.
Although hundreds of structures of this spike protein
are already
available in the Protein Data Bank, the one with the code
They are encouraged to distinguish the four different levels of the
protein structures: primary, secondary, tertiary, and quaternary,
changing the representation of the molecule from lines or wireframe
to cartoon.
They must learn how to select individual residues
or different
chains, how to change their colors, how to generate objects, how to
show and hide different parts of the protein, how to measure distances
and angles for bonds, and how to generate surfaces.
They have
to realize that the spike protein is a complex of three
identical chains. A schematic illustration of the spike protein (
students, and they must recognize every single domain in the ectodomain,
extracting them as different objects and coloring them in the suggested
color.
The S1 subunit has an N-terminal
domain (NTD) and a receptor-binding
domain (RBD) located in the C-terminal domain, which is implied in
recognition and binding to the host cell receptor. S2 consists of
the fusion peptide (FP), two heptad repeats 1 (HR1 and HR2) which
operate the fusion of viral and host membranes, a transmembrane domain
(TM), and a cytoplasmic tail (CT).
When different species of
coronavirus are compared, the S2 subunit
is highly conserved, but the sequence of the S1 subunit varies greatly.
S1 and S2 are connected to the S1/S2 cleavage site in which specific
proteases act. The cleavage transforms the spike protein into a fusion
competent form that suffers several conformational changes and allows
it to anchor to the host membrane leading to the membrane fusion.10 (link)
Study authors were requested to complete a preformatted metadata file to facilitate comparisons of data across studies. Requested metadata included sample handling information (e.g., source laboratory, and sample collector) and ecological information (e.g., source reef name, coordinates, zone, water temperature, and coral colony measurements). SCTLD zones included vulnerable (i.e., locations where the disease had not been observed/reported), endemic (i.e., locations where the initial outbreak of the disease had moved through and no or few active lesions were observed on colonies), and epidemic (i.e., locations where the outbreak was active and prevalent across colonies of multiple species). Invasion zone sites, where the disease was newly arrived but not yet prevalent, were grouped within the epidemic zone for consistency across studies and simplicity of analysis. Some metadata required standardization of units and not all metadata were available across all studies. However, in all field-collected samples, all sampling dates and site information were available, enabling the completion of SCTLD disease zone metadata for Florida studies by referencing the Coral Reef Evaluation and Monitoring Project, Disturbance Response Monitoring, and SCTLD boundary reconnaissance databases. For USVI, zones were assigned based on the USVI Department of Planning and Natural Resources SCTLD database (
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More about "Protein Subunits"
These subunits can include peptides, polypeptides, or other macromolecules that assemble into intricate protein complexes.
Understanding the composition and interactions of these protein subunits is crucial for studying protein structure, function, and regulation.
Researchers can leverage AI-driven tools like PubCompare.ai to optimize their protein subunit research by easily finding and comparing protocols from scientific literature, preprints, and patents.
This can help identify the most accurate and effective approaches, enhancing research accuracy and efficiency.
PubCompare.ai's powerful AI capabilities can provide valuable insights and streamline the protein subunit research process.
Techniques like TRIzol reagent, RNeasy Mini Kit, and Lipofectamine 2000 can be used to extract and purify RNA and DNA, which are essential for studying protein expression and structure.
The High-Capacity cDNA Reverse Transcription Kit and IScript cDNA synthesis kit can be used to convert RNA into cDNA, while the StepOnePlus Real-Time PCR System can be employed for quantitative analysis of gene expression and protein levels.
Furthermore, the DNeasy Blood and Tissue Kit, or DNeasy Blood & Tissue Kit, can be utilized for DNA extraction from various biological samples, which is crucial for investigating genomic factors that influence protein subunit composition and function.
Fetal Bovine Serum (FBS) is a commonly used supplement in cell culture media, providing essential nutrients and growth factors that support the growth and maintenance of cells, including those used in protein subunit research.
By leveraging these tools and techniques, researchers can enhance their understanding of protein subunits, leading to breakthroughs in fields such as structural biology, drug design, and disease diagnostics.