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Protein Subunits

Protein subunits are the individual components that make up larger protein structures.
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.
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Most cited protocols related to «Protein Subunits»

In comparative modelling, a 3D protein model of a target sequence is generated by extrapolating experimental information from an evolutionary related protein structure that serves as a template. In SWISS-MODEL, the default modelling workflow consists of the following main steps:

Input data: The target protein can be provided as amino acid sequence, either in FASTA, Clustal format or as a plain text. Alternatively, a UniProtKB accession code (34 (link)) can be specified. If the target protein is heteromeric, i.e. it consists of different protein chains as subunits, amino acid sequences or UniProtKB accession codes must be specified for each subunit.

Template search: Data provided in step 1 serve as a query to search for evolutionary related protein structures against the SWISS-MODEL template library SMTL (30 (link)). SWISS-MODEL performs this task by using two database search methods: BLAST (35 (link),36 (link)), which is fast and sufficiently accurate for closely related templates, and HHblits (37 (link)), which adds sensitivity in case of remote homology.

Template selection: When the template search is complete, templates are ranked according to expected quality of the resulting models, as estimated by Global Model Quality Estimate (GMQE) (30 (link)) and Quaternary Structure Quality Estimate (QSQE) (23 ). Top-ranked templates and alignments are compared to verify whether they represent alternative conformational states or cover different regions of the target protein. In this case, multiple templates are selected automatically and different models are built accordingly. To provide the user with the option to use alternative templates than those selected automatically, all templates are shown in a tabular form with a descriptive set of features. In addition, interactive graphical views facilitate the analysis and comparison of available templates in terms of their three-dimensional structures, sequence similarity and quaternary structure features.

Model building: For each selected template, a 3D protein model is automatically generated by first transferring conserved atom coordinates as defined by the target-template alignment. Residue coordinates corresponding to insertions/deletions in the alignment are generated by loop modelling and a full-atom protein model is obtained by constructing the non-conserved amino acid side chains. SWISS-MODEL relies on the OpenStructure computational structural biology framework (38 (link)) and the ProMod3 modelling engine to perform this step. For more detailed information on model building we refer to a dedicated section in Results.

Model quality estimation: To quantify modelling errors and give estimates on expected model accuracy, SWISS-MODEL relies on the QMEAN scoring function (31 (link)). QMEAN uses statistical potentials of mean force to generate global and per residue quality estimates. The local quality estimates are enhanced by pairwise distance constraints that represent ensemble information from all template structures found. For more information on quality estimation we refer to a dedicated section in Results.

SWISS-MODEL allows for further customization of steps 1 and 3. Expert users can directly upload custom target-template sequence alignments, template structures or DeepView project files (26 (link)) in separate input forms.
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Publication 2018
Amino Acids Amino Acid Sequence Biological Evolution cDNA Library Hypersensitivity INDEL Mutation Protein Domain Proteins Protein Subunits Sequence Alignment
The SILVA release cycle and numbering corresponds to that of the EMBL database, a member of the International Nucleotide Sequence Database Collaboration (http://www.insdc.org). Thus, the ribosomal RNA sequences used to build version 91 of the SILVA databases, which is referred to in this paper, were retrieved from release 91 (June 2007) of EMBL. A complex combination of keywords including all permutations of 16S/18S, 23S/28S, SSU, LSU, ribosomal and RNA was used to retrieve a comprehensive subset of all available small and large subunit ribosomal RNA sequences. All candidate rRNA sequences extracted from the EMBL database were stored locally in a relational database system (MySQL). The specificity of the SILVA databases for rRNA is assured by the subsequent processing of the primary sequence information.
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 (http://www.arb-home.de) in 2004. For the large subunit RNA databases, an in-house, aligned database was used as the seed. It encompasses a representative set of 2868 sequences from all three domains (150 000 alignment positions). Since the quality of the final datasets critically depends on the quality of the seed alignments both datasets were iteratively cross-checked by expert curators during database build-up. Within this process, all sequences that could not be unambiguously aligned were removed from the seed.
Publication 2007
A 601 Archaea Bacteria Base Sequence Eukaryota Nucleotides Protein Subunits Ribosomal RNA Ribosomes
We compare the performance of CellChat with three other tools, including SingleCellSignalR9 , iTALK10 , and CellPhoneDB16 (link) . We compare our database CellChatDB with other existing analogous databases, including CellTalkDB71 , CellPhoneDB16 (link), iTALK10 , SingleCellSignalR9 , Ramilowski201572 (link), NicheNet13 (link) and ICELLNET73 . SingleCellSignalR scores a given ligand-receptor interaction between two cell populations using a regularized product score approach based on average expression levels of a ligand and its receptor and an ad hoc approach for estimating an appropriate score threshold. iTALK identifies differentially expressed ligands and receptors among different cell populations and accounts for the matched ligand-receptor pairs as significant interactions. CellPhoneDB v2.0 predicts enriched signaling interactions between two cell populations by considering the minimum average expression of the members of the heteromeric complex and performing empirical shuffling to calculate which ligand–receptor pairs display significant cell-state specificity. The detailed description of how these methods were performed is available in Supplementary Note 3.
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 3. Note that such subsampling analysis was used to evaluate the consistency rather than accuracy.
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Publication 2021
Cell Communication Cells Gene Expression Profiling Genes Genetic Heterogeneity Ligands Population Group Protein Subunits Receptor, Transforming Growth Factor-beta Type I Receptors, Cell Surface Single-Cell RNA-Seq TGFB1 protein, human TGFBR2 protein, human
We describe here application of the UniDec approach to problems of increasing complexity: membrane protein AqpZ; small heat shock proteins HSP17.7, HSP16.5, and αB-crystallin; and lipoprotein Nanodiscs.
MS and IM-MS data of aquaporin Z (AqpZ) with bound 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) obtained at 100 V accelerating potential into a dedicated collision cell was analyzed using UniDec by limiting the mass range to between 95 and 105 kDa.27 (link) An example of how the algorithm performs without mass limitations is shown in Figure S-2. Data was smoothed in MassLynx 4.1 software (Waters Corp.) before analysis with Transform and MaxEnt, which used the same mass limitation.
Deconvolution of subunit exchange data from HSP17.7 was performed by limiting the allowed mass range to between 211 kDa and 222 kDa. Tandem MS spectra of the isolated +47 charge state of HSP16.5 24-mers were summed across multiple collision voltages to compile an aggregate spectrum.28 (link) Deconvolution was performed by limiting the charge state between 10 and 49 and manually defining the +47 charge state, which was necessary because only one charge state was isolated in the MS/MS experiment. Collision induced dissociation (CID) spectra of αB-crystallin were obtained similarly. Masses were limited to within 3000 Da of a wide range of potential oligomer complexes ranging from 1 to 74 subunits of a 20085 Da monomer. Charge was limited to between 5 and 84. In addition to the charge-smooth filter, a mass-smooth filter was applied to smooth the distribution of dimer units.
Nanodiscs with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and POPC were analyzed with a linear drift cell Waters Synapt G1 ion mobility-mass spectrometer.29 (link) Data was deconvolved without a charge filter but by using a mass filter to smooth the distribution of lipids. Masses were limited to between 100 kDa and 175 kDa. Conversion from arrival time to collision cross section (CCS) was performed using the Mason-Schamp equation as described previously,27 (link),29 (link) using t0 values calibrated from alcohol dehydrogenase analyzed under the same instrumental conditions.
Publication 2015
Cells Crystallins Dehydrogenase, Alcohol Dimyristoylphosphatidylcholine Glycerylphosphorylcholine GPER protein, human Heat-Shock Proteins, Small Lipids Lipoproteins Phosphorylcholine plasma protein Z Protein Subunits Range of Motion, Articular Tandem Mass Spectrometry Tissue, Membrane Water Channel
Two types of precompiled databases for both small and large subunit ribosomal RNA sequences are available in the ARB format: the high-quality Ref databases and the comprehensive Parc databases. The Ref databases are subsets of Parc, which are exclusively comprised of nearly full length 16S/18S and 23S/28S rRNA sequences. A sequence is accepted if it is at least 1200 bases long. Additionally, sequences as short as 900 bases are included if they belong to the domain Archaea. Applying a strict cut-off at 1200 bases would result in the loss of the majority of sequences of this domain. Sequences in the LSU Ref database have to be at least 1900 bases long. For quality control, all sequences that could not be unambiguously aligned (alignment quality score <50 and <30 for SSU and LSU, respectively) were removed from the Ref databases. Both Ref databases are supplemented with a guide tree based on the full length sequence tree of the ARB Jan 04 SSU and the Ludwig LSU databases, respectively. The trees were built using the ARB parsimony tool with filters to remove highly variable positions. Common filters like the positional variability filters were recalculated for the Ref databases. Sequences with long branches in combination with low alignment qualities (<80) were removed from the Ref databases.
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’, Tables 1–3). The name Parc has been chosen according to the UniProt concept (23 (link)), where Parc stands for the comprehensive protein sequence archive. All sequences in the SILVA databases are associated with a rich set of sequence and process parameters. Included is information from the initial quality checks to the alignment process, as well as information taken directly from the EMBL entry (Tables 1–3). Together with the search and query functionalities on the web site and in ARB, one can quickly search for problematic sequences or generate individual high or low quality sequence subsets for further processing or curation. The ARB package can be used to export sequences in various formats like EMBL, GenBank, or aligned and unaligned FASTA.
Publication 2007
Archaea Genes parC Protein Protein Subunits Ribosomal RNA Ribosomal RNA Genes RNA, Ribosomal, 23S RNA, Ribosomal, 28S Trees

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.

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Patent 2024
Antigens Betacoronavirus Biological Assay Cations Cholesterol Coronavirus 229E, Human Coronavirus OC43, Human Hemorrhage Human coronavirus HKU1 Lipid Nanoparticles Lipids Middle East Respiratory Syndrome Coronavirus M protein, multiple myeloma NL63, Human Coronavirus Oryctolagus cuniculus Polynucleotides Protein Subunits Rabbits RNA, Messenger Serum Severe acute respiratory syndrome-related coronavirus spike protein, SARS-CoV-2 Vaccines Virus Physiological Phenomena

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 (FIG. 2A). As expected, the FA neurons were post-mitotic as evidenced by the lack of the mitotic marker phosphorylated histone H3 (FIG. 2B). Treatment of FA neurons with an shRNA targeting any one of the 10 FXN-RFs upregulated TRE-FXN transcription (FIG. 2C) and increased frataxin (FIG. 2D) to levels comparable to that of normal neurons. Likewise, treatment of FA neurons with small molecule FXN-RF inhibitors also upregulated TRE-FXN transcription (FIG. 2E) and increased frataxin (FIG. 2F) to levels comparable to that of normal neurons.

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. FIG. 3A shows that FA neurons expressing an NS shRNA accumulated increased mitochondrial ROS production compared to EZH2- or HDAC5-knockdown FA neurons. FIG. 3B shows that FA neurons had increased levels of mitochondrial ROS production compared to normal neurons (Codazzi et al., (2016) Hum Mol Genet 25(22): 4847-485). Notably, inhibition of FXN-RFs in FA neurons restored mitochondrial ROS production to levels comparable to that observed in normal neurons. In the second set of experiments, mitochondrial oxygen consumption, which is related to ATP production, was measured using an Agilent Seahorse XF Analyzer (Divakaruni et al., (2014) Methods Enzymol 547:309-54). FIG. 3C shows that oxygen consumption in FA neurons was ˜60% of the level observed in normal neurons. Notably, inhibition of FXN-RFs in FA neurons restored oxygen consumption to levels comparable to that observed in normal neurons. Collectively, these preliminary results provide important proof-of-concept that inhibition of FXN-RFs can ameliorate the mitochondrial defects of FA post-mitotic neurons.

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.

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Patent 2024
Aconitate Hydratase Biological Assay Cells Cloning Vectors Enzymes EZH2 protein, human frataxin Genets HDAC5 protein, human Histone H3 Immunoblotting Induced Pluripotent Stem Cells inhibitors Iron Ketoglutarate Dehydrogenase Complex Mitochondria Mitochondrial Inheritance Mitochondrial Proteins MitoSOX NADH NADH Dehydrogenase Complex 1 NEUROG1 protein, human Neurons Oxidoreductase Oxygen Consumption Proteins Protein Subunits Psychological Inhibition Pyruvates Reactive Oxygen Species Repression, Psychology Seahorses Short Hairpin RNA Sulfur sulofenur Superoxide Dismutase Superoxides Thioctic Acid Transcription, Genetic

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 FIG. 17, the MERS-CoV vaccine encoding the full-length S protein induced strong immune response after the boost dose on day 21. Further, full-length S protein vaccine generated much higher neutralizing antibody titers as compared to S2 alone (FIG. 18).

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Patent 2024
Antibodies, Neutralizing Biological Assay Hemorrhage Immunogenicity, Vaccine Middle East Respiratory Syndrome Coronavirus M protein, multiple myeloma mRNA Vaccine Mus Polynucleotides Protein Subunits Response, Immune RNA, Messenger Serum spike protein, SARS-CoV-2 Vaccines Virus Physiological Phenomena
The instructor provided to the students a brief introduction
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 (Figure 1).7 (link)It is highlighted that spike protein (approximately 180–200
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 (Figure 2). Each protomer
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 7DWY(9 (link)) has been selected and must be loaded in a PyMOL session.
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 (Figure 3) is given to the
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)
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Publication 2023
Carbohydrates Cells COVID 19 Cytokinesis Cytoplasm Membrane Fusion Membrane Glycoproteins M protein, multiple myeloma Nucleocapsid Peptides Proteins Protein Subunits Protomers Protoplasm SARS-CoV-2 Student Tail Tissue, Membrane Virus
To acquire small subunit (SSU) 16S rRNA datasets for this meta-analysis, an email was sent on July 14, 2020, and July 23, 2020, to the hosts of the coral-list listserv and the SCTLD Disease Advisory Committee (DAC) email list, respectively, requesting scientists to share unpublished SCTLD-associated microbiome datasets. In addition, to allow for comparisons of microbiomes between a past Caribbean coral disease to the novel SCTLD outbreak, a rapid tissue loss (RTL) disease study in Acropora palmata (APAL) and Acropora cervicornis (ACER) comprising apparently healthy (AH) samples, inoculated AH samples, and inoculated diseased samples [61 ], also was included in some analyses. This particular study was selected because Acropora spp. reportedly are not susceptible to SCTLD and the study used V4 primers [3 ]. In total, 17 studies were analyzed, 16 from SCTLD and one from an Acropora spp. RTL study (Supplementary Table 1).
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 (https://dpnr.vi.gov/czm/sctld/).
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Publication 2023
Caribbean People Coral Coral Reefs Epidemics Microbiome Oligonucleotide Primers Protein Subunits RNA, Ribosomal, 16S Specimen Collection Tissues

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More about "Protein Subunits"

Protein subunits, also known as protein components or protein moieties, are the individual building blocks that make up larger, more complex protein structures.
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.