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Ribosomal Proteins

Ribosomal Proteins are a diverse group of proteins that are integral components of ribosomes, the cellular organelles responsible for protein synthesis.
These proteins play crucial roles in ribosome structure, assembly, and function, contributing to the efficient translation of genetic information into functional proteins.
Ribosomal Proteins exhibit a wide range of molecular weights and structures, and are found in both prokaryotic and eukaryotic organisms.
Understanding the properties and functions of Ribosomal Proteins is crucial for advancing research in areas such as cell biology, genetics, and molecular biology.
Leveraging the power of AI-driven platforms like PubCompare.ai can help researchers easily locate the best protocols, products, and methodologies for their Ribosomal Protein studies, taking their research to new heights.

Most cited protocols related to «Ribosomal Proteins»

Phylogenetic Genome Tree Construction

Cited 532 times

A genome tree incorporating 5656 trusted reference genomes (see Supplemental Methods) was inferred from a set of 43 genes with largely congruent phylogenetic histories. An initial set of 66 universal marker genes was established by taking the intersection between bacterial and archaeal genes determined to be single copy in >90% of genomes. From this initial gene set, 18 multicopy genes with divergent phylogenetic histories in >1% of the reference genomes were removed. A multicopy gene within a genome was only deemed to have a congruent phylogenetic history if all copies of the gene were situated within a single conspecific clade (i.e., all copies were contained in a clade from a single named species) within its gene tree. Genes were aligned with HMMER v3.1b1 (http://hmmer.janelia.org), and gene trees inferred with FastTree v2.1.3 (Price et al. 2009 (link)) under the WAG (Whelan and Goldman 2001 (link)) and GAMMA (Yang 1994 (link)) models. Trees were then modified with DendroPy v3.12.0 (Sukumaran and Holder 2010 (link)) in order to root the trees between archaea and bacteria unless these groups were not monophyletic, in which case midpoint rooting was used. A further five genes found to be incongruent with the IMG taxonomy were also removed as these genes may be subject to lateral transfer. Testing of taxonomic congruency was performed as described in Soo et al. (2014) (link). The final set of 43 phylogenetically informative marker genes (Supplemental Table S6) consists primarily of ribosomal proteins and RNA polymerase domains and is similar to the universal marker set used by PhyloSift (Supplemental Table S7; Darling et al. 2014 (link)). A reference genome tree was inferred from the concatenated alignment of 6988 columns with FastTree v2.1.3 under the WAG+GAMMA model and rooted between bacteria and archaea. Internal nodes were assigned taxonomic labels using tax2tree (McDonald et al. 2012 (link)).

Parks D.H., Imelfort M., Skennerton C.T., Hugenholtz P, & Tyson G.W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25(7), 1043-1055.

Publication 2015

Archaea Bacteria DNA-Directed RNA Polymerase Gamma Rays Genes Genes, Archaeal Genes, vif Genetic Markers Genome Ribosomal Proteins Trees

Comparative Genomics and Pan-Genome Analysis

Cited 209 times

The genome sequencing revolution has radically altered the field of microbiology. Whole-genome sequencing for prokaryotes became a standard method of study ever since the first complete genome of free-living organism, Haemophilus influenza, was sequenced in 1995 (14 (link)). Due to the widespread use of the next generation sequencing (NGS) techniques, thousands of genomes of prokaryotic species are now available, including genomes of multiple isolates of the same species, typically human pathogens. Thus, the mere density of comparative genomic information for high interest organisms provides an opportunity to introduce a pan-genome based approach to prediction of the protein complement of a species.
The collection of prokaryotic genomes available at NCBI is growing exponentially and shows no signs of abating: as of January 2016 NCBI's assembly resource contains 57 890 genome assemblies representing 8047 species (see genome browser https://www.ncbi.nlm.nih.gov/genome/browse/, for the up-to-date information). Notably, genomes of different strains of the same species can vary considerably in size, gene content and nucleotide composition. In 2005, Tettelin et al. (15 (link)) introduced the concept of pan-genome, aiming to provide a compact description of the full complement of genes of all the strains of a species. Genes common to all pan-genome members (or to the vast majority of them) are called core genes; those present in just a few clade members are termed accessory or dispensable genes; genes specific to a particular genome (strain) are termed unique genes (16 (link)).
In PGAP we define the pan-genome of a clade at a species or higher level (17 ). To be included as a core gene for a species-level pan-genome, we require the gene to be present in the vast majority—at least 80%—of all genomes in the clade. A set of core genes gives rise to a set of core proteins. We show in Figure 1 how the number of protein clusters, for each of four well studied large clades, depends on the fraction of the clade members that contribute proteins to the cluster. There are three critical regions in this analysis: (i) unique genes, present in less than 1% of all clade members; (ii) dispensable genes, present in 1–20% of genomes; and (iii) core genes, found in at least 80% of the represented genomes. Based on our analysis, there are very few clusters appearing in at least 20% of the members of a clade but no more than 80% of the members. The use of a cutoff of 80% was chosen to capture a wide set of genes conserved within the whole clade while eliminating genes having less abundant representation. We further subject the core proteins to clustering using USearch to reduce the total number of proteins required to represent the full protein complement of the pan-genome (18 (link)). We use the representative core proteins to infer genes for homologous core proteins in a newly sequenced genome (19 ).
The notion of the pan-genome can be generalized beyond a species level and applies, in fact, to any taxonomy level (from genus to phylum to kingdom). Notably, in the pan-genomes of Archaea and Bacteria, the universally conserved ribosomal genes make a group of core genes. The main practical value of the pan-genome approach is in formulating an efficient framework for comparative analysis of large groups of closely related organisms separated by small evolutionary distances as defined by ribosomal protein markers (20 (link),21 (link)).

Tatusova T., DiCuccio M., Badretdin A., Chetvernin V., Nawrocki E.P., Zaslavsky L., Lomsadze A., Pruitt K.D., Borodovsky M, & Ostell J. (2016). NCBI prokaryotic genome annotation pipeline. Nucleic Acids Research, 44(14), 6614-6624.

Publication 2016

Bacteria Biological Evolution Complement System Proteins Gene Products, Protein Genes Genes, vif Genome Genome, Archaeal Haemophilus influenzae Homo sapiens Nucleotides Pathogenicity Prokaryotic Cells Proteins Ribosomal Proteins Ribosomes SET protein, human Strains

Phylogenetic Analysis of Microbial Genomes

Cited 138 times

All 2,887 sequenced microbial genomes were retrieved from the Integrated Microbial Genomes^{31 (link)} (IMG) version 3.4 with corresponding coding sequence (CDS) calls, translated protein sequences, and taxonomic assignments. Genomes were screened for length >50,000 nt, at least 50 CDSs, and at least 75% of the genome coding sequences. 51 of the remaining genomes lacked a taxonomic label below the family level and were considered to be taxonomically uncharacterized. 1,221 16S gene sequences representing IMG species were retrieved from Greengenes^{11 (link)}. CDS to COGs assignments and 16S rRNA gene annotations were downloaded from IMG and used only for identifying the 31 ribosomal proteins for re-performing the corresponding method^{15 (link),16 (link)} with these genomes as described in the Supplementary Methods (“Building phylogenetic trees using 16S and ribosomal proteins”). The PhyloPhlAn pipeline was further tested on the 3,171 genomes from IMG 3.5 as of February 2012; 566 additional genomes not contained in IMG 3.5 were downloaded from IMG-GEBA^{31 (link)} as of March 2012, and the genomes of candidate division OP1^{34 (link)} and Caldiserica^{35 (link)} were retrieved from the GOLD database^{59 (link)} (GOLD ids Gc02183 and Gi17125 respectively).

Segata N., Börnigen D., Morgan X.C, & Huttenhower C. (2013). PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nature communications, 4, 2304.

Publication 2013

Amino Acid Sequence Exons Gene Annotation Genes Genome Genome, Microbial Gold Open Reading Frames Ribosomal Proteins RNA, Ribosomal, 16S Triglyceride Storage Disease with Ichthyosis

Factors Limiting Bacterial Division Rate

Cited 88 times

To show the power and usefulness of BioNumbers we address a specific thought experiment: What limits the maximal rate at which a bacterium can divide? That is, why does E. coli under ideal conditions of LB medium and 37°C divide every ∼20 min (BNID 100260) and not every ∼2 min? Clearly the ability to divide at faster rates would provide an overwhelming selective advantage, at least in laboratory conditions. There are many cellular processes that could potentially limit E. coli to a ∼20 min doubling time. But for most such processes, it seems possible for the bacterium to overcome the limitation by increasing the amount of the limiting factor, for instance by increasing the number of nutrient transporters, the number of DNA replication circles, or the number of RNA polymerase complexes. But ribosomes are an interesting partial exception to this rule. Ribosomes translate all the proteins in the cell including those that are assembled into new ribosomes. Doubling ribosome content would necessitate translating twice the number of ribosomal proteins. Here then is a potentially limiting rate: the time that it takes a ribosome to translate enough amino acids to copy itself (4 ). We demonstrate the use of the BioNumbers database with a brief analysis of these considerations. An E. coli ribosome contains in total ∼7500 amino acids (7459, Search term: ‘ribosome’, BNID101175) and the translation rate is as high as ∼21 aa/sec (Search term: ‘translation ribosome’, BNID100059). Translating a single copy of all of the ribosomal proteins thus minimally requires ∼7500/21 ≈ 400 sec ≈ 7 min. In order to make a new cell of the same size, each ribosome must make a copy of itself. Taking into account essential translational cofactors like the elongation factors EF-Tu and EF-G would increase the required time to ∼9 min. It therefore seems impossible to obtain a cellular doubling time faster than ∼9 min. Perhaps when further requirements for ribosome duplication are taken into account, it will be evident why E. coli double in ∼20 min. We thus see that with simple calculations and with several useful biological numbers on hand, we can generate an intriguing hypothesis for what sets a lower bound on the proliferation rate of E. coli.

Milo R., Jorgensen P., Moran U., Weber G, & Springer M. (2009). BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Research, 38(Database issue), D750-D753.

Publication 2009

Amino Acids Bacteria Biopharmaceuticals Cells Culture Media, Conditioned DNA-Directed RNA Polymerase DNA Replication EEF1A1 protein, human Escherichia coli Membrane Transport Proteins Nutrients Physiology, Cell Protein Biosynthesis Proteins ribosomal A-protein Ribosomal Proteins Ribosomes

Spatial Gene Expression Profiling and Clustering

Cited 87 times

Seurat’s spatial inferences enable us to not only re-infer spatial patterns for the ‘landmark’ genes, but also to create computational in situ patterns for any gene detected in our RNA-seq data. We therefore inferred spatial patterns for all genes that were likely to exhibit spatially restricted expression patterns across our dataset. Specifically, we took all genes that displayed weak evidence of being variable across our single cells (see Identification of Highly Variable Genes, though here we applied a z-score cutoff of 1 instead of 2), and added all genes exhibiting ‘significant’ loadings of the first five principal components using a Bonferroni-corrected p-value of 0.01. Finally, we removed genes which were detected in less than 20 cells in the overall dataset, as they may present strong spatial patterns that are simply the result of aberrant expression in a very small number of cells, leaving us with 2,190 remaining genes. We then inferred the spatial localization patterns of all these genes. Since our goal at this stage was to identify broad clusters of spatial gene patterning, we inferred spatial patterns using imputed measurements for each of these genes in order to ameliorate technical noise.
We further examined these 2,190 patterns to search for genes whose expression patterns exhibited significant spatial variability across the embryo. To accomplish this, we calculated a ‘spatial CV’ for each gene, by calculating the coefficient of variation of its expression levels across all 64 bins. We identified 290 genes with a CV greater than 0.25, implying spatial heterogeneity. We chose this cutoff because it excluded known housekeeping genes (e.g ribosomal proteins) from further analysis, as these genes are unlikely to be heavily spatially patterned.
We next performed k-means clustering on the remaining 290 patterns. Specifically, the input for the k-means clustering was a 290×64 matrix, containing the expression level of all 290 genes in each of the 64 spatial bins. We used a k = 9, as this was the largest value of k for which we observed distinct and non-overlapping clusters^{9 (link)}. The nine clusters represent ‘archetypes’ of gene expression, namely, broad spatial patterns representing clusters of similarly localized genes.

Satija R., Farrell J.A., Gennert D., Schier A.F, & Regev A. (2015). Spatial reconstruction of single-cell gene expression. Nature biotechnology, 33(5), 495-502.

Publication 2015

Cells Debility Embryo Gene Clusters Gene Expression Genes Genes, Housekeeping Genes, vif Genetic Heterogeneity Ribosomal Proteins RNA-Seq

Most recents protocols related to «Ribosomal Proteins»

In Vitro Evaluation of S6K Binding Activity

Not available on PMC !

Example 49

The functional activity of compounds was determined in a cell line where p70S6K is constitutively activated. Test article was dissolved in DMSO to make a 10 μM stock. PathScan® Phospho-S6 Ribosomal Protein (Ser235/236) Sandwich ELISA Kit was purchased from Cell Signaling Technology. A549 lung cancer cell line, was purchased from American Type Culture Collection. A549 cells were grown in F-12K Medium supplemented with 10% FBS. 100 μg/mL penicillin and 100 μg/mL streptomycin were added to the culture media. Cultures were maintained at 37° C. in a humidified atmosphere of 5% CO₂and 95% air. 2.0×10⁵cells were seeded in each well of 12-well tissue culture plates for overnight. Cells were treated with DMSO or test article (starting at 100 μM, 10-dose with 3 fold dilution) for 3 hours. The cells were washed once with ice cold PBS and lysed with 1× cell lysis buffer. Cell lysates were collected and samples were added to the appropriate wells of the ELISA plate. Plate was incubated for overnight at 4° C. 100 μL of reconstituted Phospho-S6 Ribosomal Protein (Ser235/236) Detection Antibody was added to each well and the plate was incubated at 37° C. for 1 hour. Wells were washed and 100 μl of reconstituted HRP-Linked secondary antibody was added to each well. The plate was incubated for 30 minutes at 37° C. Wash procedure was repeated and 100 μL of TMB Substrate was added to each well. The plate was incubated for 10 minutes at 37° C. 100 μL of STOP Solution was added to each well and the absorbance was read at 460 nm using Envision 2104 Multilabel Reader (PerkinElmer, Santa Clara, CA). IC₅₀curves were plotted and IC₅₀values were calculated using the GraphPad Prism 4 program based on a sigmoidal dose-response equation.

TABLE 2

In vitro biological data for representative compounds of Formula

I-IX Unless otherwise noted, compounds that were tested had an IC₅₀

of less than 50 μM in the S6K binding assay.

Example NumberS6K Binding Activity

10B

11B

12C

13C

14C

15A

16A

17B

18A

19A

20A

21A

22C

23B

24A

25A

26C

27A

28C

29C

30C

31A

32A

33C

34C

35C

36C

37C

38A

39A

40A

41A

Unless otherwise noted, compounds that were tested had an IC₅₀of less than 50 μM in the S6K Binding assay. A=less than 0.05 μM; B=greater than 0.05 μM and less than 0.5 μM; C=greater than 0.5 μM and less than 10 μM;

US11866421B2. Pyrimidine and pyridine amine compounds and usage thereof in disease treatment (2024-01-09). Epigen Biosciences, Inc. [US]. Inventors: Graham Beaton [US], Fabio Tucci [US], Satheesh Ravula [US], Suk Joong Lee [US], Chandravadan Shah [US].

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Patent 2024

A549 Cells Atmosphere Biological Assay Biopharmaceuticals Buffers Cell Lines Cells Cold Temperature Culture Media Enzyme-Linked Immunosorbent Assay Immunoglobulins Lung Cancer Penicillins prisma Psychological Inhibition Ribosomal Proteins Ribosomal Protein S6 Ribosomal Protein S6 Kinases, 70-kDa Streptomycin Sulfoxide, Dimethyl Technique, Dilution Tissues

Longitudinal Gene Expression Analysis in Honey Bees

A total of five genes were investigated in this study, namely: 1- vitellogenin (Vg), 2- major royal jelly protein 1 (mrjp1), 3- acetylcholine esterase 2 (AChE-2), 4- superoxide dismutase-like (Rsod) and 5- thioredoxin 1 (Trx-1). Sequences of the primers and their amplicon sizes are given in Table 1. Two-step reverse transcription quantitative PCR (RT-qPCR) using BioRad iTaq SYBER Green Supermix 2X was conducted on three biological and technical replicates per sample on five time points enabling a greater longitudinal analysis of gene regulation. cDNA was synthesized from RNA extractions using BioRad iScript Kit following the manufacturer’s protocol. Target genes were normalized against two housekeeping genes (GAPDH, RPS18) known for their stability in honey bee tissues^{38 (link),62 (link)}.Table 1

Target genes investigated in this study, housekeeping genes, primer sequences, amplicon size and NCBI accession numbers.

Gene	Description	F/R	bp	NCBI Accession
Target
AChE-2	Acetylcholinesterase-2	GACGCGAAGACCATATCCGT TCTGTGTCCTTGAAGTCCGC	140	NM_001040230.1
Mrjp1	Major royal jelly protein 1	TGACCAATGGCATGATAAG GACCACCATCACCGACCT	98	NM_001011579.1
Vg	Vitellogenin	AACGCTTTTACTGTTCGCGG TATGCACGTCCGACAGATCG	128	NM_001011578.1
Rsod	Superoxide dismutase-like	GGAGCAGTATCTGCAATGGGA CGCTACAAAACGTGGTGGTT	141	XM_006558333.2
Trx-1	Thioredoxin-1	AATGCACCGGCTCAAGAACA CATGCGACAAGGATTGCACC	138	XM_393603.7
Housekeeping
GAPDH	Glyceraldehyde-3-phosphate dehydrogenase 2	TACCGCTTTCTGCCCTTCAA GCACCGAACTCAATGGAAGC	142	XM_393605.7
RPS18	40S ribosomal protein S18	AATTATTTGGTCGCTGGAATTG TAACGTCCAGCAGAATGTGGTA	238	XM_625101.6

Alburaki M., Madella S, & Cook S.C. (2023). Non-optimal ambient temperatures aggravate insecticide toxicity and affect honey bees Apis mellifera L. gene regulation. Scientific Reports, 13, 3931.

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Publication 2023

Acetylcholinesterase Biopharmaceuticals DNA, Complementary GAPDH protein, human Gene Expression Regulation Genes Genes, Housekeeping Genes, vif Honey Oligonucleotide Primers Oxidoreductase Phosphates Proteins Reverse Transcription Ribosomal Proteins royal jelly Superoxide Dismutase Thioredoxin 1 Vitellogenins

Gene Expression Analysis of erbb3b in Anemonefish

In total, ten juveniles (five orange and five black) were assayed to measure gene expression of the three erbb3b genes identified in A. clarkii. Specific primers for each A. clarkii erbb3b gene (two short genes containing 1,911 bp and one long gene of 4,275 bp) were designed manually based on their genomic sequence (Supplementary Table 3). Primers previously used by Roux et al. (2022) (link) with A. ocellaris were used to target the housekeeping genes ribosomal protein L7 (rpl7) and ribosomal protein L32 (rpl32). Extracted RNA from each juvenile (as described in Supplementary Methods 1) was converted to cDNA using PrimeScript RT-PCR Kit (Takara Bio, Shiga, Japan). The efficiency and specificity of the designed primers was tested through PCR using the GoTaq Green Master kit (Promega, Madison, USA) with thermal cycling conditions of 2 min at 95°C, followed by 30 cycles of 45 s at 95°C, 45 s at 60/63/65°C, and 30 s 72°C, a final extension step of 5 min at 72°C, preservation at 4°C, and subsequent agarose gel electrophoresis (Supplementary Fig. 3). The specificity was also tested through direct forward and reverse Sanger sequencing by aligning the forward and reverse outputs and blasting the obtained amplicons against the reference genomic sequences (Supplementary Fig. 4).
The expression of each erbb3b gene and the two housekeeping genes (rpl7 and rpl32) was obtained by RT-qPCR at 65°C (PrimeScript transcriptase, Takara, SYBRgreen) and normalized with the Pfaffle equation (Ståhlberg et al. 2004 (link)):
where RE is the relative expression, E(x) is the efficiency of the amplification for isoform x, and Ct(x) is the quantification cycle of gene x.

Moore B., Herrera M., Gairin E., Li C., Miura S., Jolly J., Mercader M., Izumiyama M., Kawai E., Ravasi T., Laudet V, & Ryu T. (2023). The chromosome-scale genome assembly of the yellowtail clownfish Amphiprion clarkii provides insights into the melanic pigmentation of anemonefish. G3: Genes|Genomes|Genetics, 13(3), jkad002.

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Publication 2023

Base Sequence Biologic Preservation DNA, Complementary Electrophoresis, Agar Gel Gene Expression Genes Genes, Housekeeping Genome Oligonucleotide Primers Promega Protein Isoforms Reverse Transcriptase Polymerase Chain Reaction ribosomal protein L32 Ribosomal Proteins Transcriptase

Comparative Virome Analysis of Reef Fishes

We employed RNA-seq by Expectation-Maximization (v1.2.28) to quantify the relative abundance of transcripts within each fish species transcriptome (Haas et al. 2013 (link)). These included both viral genes and the stably expressed host reference gene, ribosomal protein S13 (RPS13), which was used to assess sequencing depth across libraries (Geoghegan et al. 2018 (link), 2021 ). Abundance measures were standardized by dividing values against the total reads for each library. We calculated both alpha and beta diversity to compare virome composition between reef fish families, as well as between cryptobenthic reef fishes (n = 39 species) and large reef fishes (n = 22 species). We also analysed the composition of the non-vertebrate virome as a form of internal control, as these viruses are not impacted by aspects of fish biology. Accordingly, we used Rhea scripts to calculate alpha diversity, including viral abundance, observed virome richness, and Shannon diversity (Lagkouvardos et al. 2017 ). Statistical comparisons of alpha diversity were modelled using generalized linear models and tested using a likelihood-ratio test (χ²) and Tukey’s post hoc analysis with the multcomp package (Hothorn, Bretz, and Westfall 2008 (link)). To compare viral communities between reef fish assemblages, we calculated beta diversity using a Bray–Curtis distance matrix with the phyloseq package (McMurdie and Holmes 2013 ). These data were then tested using permutational multivariate analysis of variance (permanova) with the vegan package (adonis) (Dixon 2003 ). All plots were constructed using ggplot2 (Valero-Mora 2010 (link)).

Costa V.A., Bellwood D.R., Mifsud J.C., Van Brussel K., Geoghegan J.L., Holmes E.C, & Harvey E. (2023). Limited cross-species virus transmission in a spatially restricted coral reef fish community. Virus Evolution, 9(1), vead011.

Publication 2023

Adonis DNA Library Fishes Forms Control Genes Genes, Viral Rhea Ribosomal Proteins RNA-Seq Transcriptome Vegan Vertebrates Virome Virus

Western Blotting Analysis of EGFR Signaling

Anti‐PlexinB2 antibody used for Western blotting analysis was purchased from Abcam (ab193355; dilution 1:500). EGFR antibody was provided by Enzo Life Sciences (ALX‐804‐064‐C100; dil. 1:500), while anti‐phospho‐EGFR‐specific antibodies (directed to p‐Tyr1068; ab5644; dil. 1:500) were from Abcam. Anti‐vinculin (V4505; dil. 1:1,000) and anti‐VSV‐G (clone P5D4; dil. 1:1,000) were provided by Sigma‐Aldrich. Anti‐phospho‐p44/42‐MAPK (Erk1/2; Thr202/Tyr204; #4370; dil. 1:1,000), anti‐p44/42‐MAPK (Erk1/2; 137F5; #4695; dil. 1:1,000), and anti‐phospho‐S6 ribosomal protein (Ser240/244; #2215; dil. 1:1,000) antibodies were from Cell Signaling.

Brundu S., Napolitano V., Franzolin G., Lo Cascio E., Mastrantonio R., Sardo G., Cascardi E., Verginelli F., Sarnataro S., Gambardella G., Pisacane A., Arcovito A., Boccaccio C., Comoglio P.M., Giraudo E, & Tamagnone L. (2023). Mutated axon guidance gene PLXNB2 sustains growth and invasiveness of stem cells isolated from cancers of unknown primary. EMBO Molecular Medicine, 15(3), e16104.

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Publication 2023

Antibodies Antibodies, Anti-Idiotypic Antibodies, Phospho-Specific Clone Cells EGFR protein, human Immunoglobulins Mitogen-Activated Protein Kinase 3 Ribosomal Proteins Technique, Dilution VCL protein, human Western Blot

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What are the different types or variations of Ribosomal Proteins?

Ribosomal Proteins are a diverse group with a wide range of molecular weights and structures. They can be classified into different categories based on their location within the ribosome (large or small subunit), their specific functions (e.g. structural, regulatory, or enzymatic), or their evolutionary conservation across prokaryotes and eukaryotes. Understanding the unique properties of these different Ribosomal Protein variations is crucial for studying their roles in ribosome assembly, stability, and function.

How can Ribosomal Proteins be used in research applications?

Ribosomal Proteins have a variety of research applications, including: - Studying ribosome biogenesis and assembly - Investigating the regulation of protein synthesis - Analyzing structural changes in ribosomes under different conditions - Identifying mutations or alterations in Ribosomal Proteins associated with disease states - Developing antibodies or other tools for the detection and quantification of specific Ribosomal Proteins Leveraging the power of PubCompare.ai can help researchers easily identify the most effective protocols, products, and methodologies for their Ribosomal Protein studies, maximizing the impact of their research.

What are some common challenges in working with Ribosomal Proteins?

Some common challenges in working with Ribosomal Proteins include: - Extracting and purifying Ribosomal Proteins without compromising their structure or function - Accurately measuring the abundance and localization of different Ribosomal Proteins within the cell - Differentiating between highly homologous Ribosomal Protein isoforms - Maintaining the stability and activity of Ribosomal Proteins during experimental procedures PubCompare.ai's AI-driven platform can help researchers overcome these challenges by identifying the most effective protocols and products for their specific Ribosomal Protein research needs, ensuring reproducibility and accuracy in their findings.

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More about "Ribosomal Proteins"

Ribosomal proteins, also known as riboproteins, are a diverse group of proteins that are essential components of ribosomes, the cellular organelles responsible for protein synthesis.
These proteins play a crucial role in ribosome structure, assembly, and function, contributing to the efficient translation of genetic information into functional proteins.
Ribosomal proteins exhibit a wide range of molecular weights and structures, and can be found in both prokaryotic and eukaryotic organisms.
Understanding the properties and functions of ribosomal proteins is essential for advancing research in areas such as cell biology, genetics, and molecular biology.
Researchers studying ribosomal proteins can leverage the power of AI-driven platforms like PubCompare.ai to easily locate the best protocols, products, and methodologies for their research.
Some common techniques and products used in ribosomal protein studies include TRIzol reagent and the RNeasy Mini Kit for RNA extraction, the High-Capacity cDNA Reverse Transcription Kit and the IScript cDNA synthesis kit for cDNA synthesis, and the StepOnePlus Real-Time PCR System and the QuantiTect Reverse Transcription Kit for quantitative gene expression analysis.
Researchers may also study the S6 ribosomal protein and its phosphorylated form, Phospho-S6 ribosomal protein, which are important markers of cellular activity and signaling pathways.
By utilizing the insights and tools provided by PubCompare.ai, researchers can take their ribosomal protein studies to new heights, optimizing their experimental designs and accelerating their discoveries.