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Protein Domain

Protein Domains are the functional and structural units that make up proteins.
These discrete segments of protein sequence and structure often act independently and can be recombined to create new proteins with different functions.
Protein Domains play a critical role in the diversity and evolution of the proteome, enabling proteins to carry out a wide range of biological activities.
Understanding the composition and organization of Protein Domains is essential for predicting protein function, structure, and interactions - key information for drug design, disease research, and other biotechnology applications.
PubComapre.ai's AI-driven platform helps streamlien Protein Domain analysis by locating the best research protocols from literature, pre-prints, and patents to identify the optimal methods and products, enhancing the reproducibility and reliability of your findings.

Most cited protocols related to «Protein Domain»

1
Estimating Tumor Purity from Gene Expression
ESTIMATE outputs stromal, immune and ESTIMATE scores by performing ssGSEA13 (link)23 (link)37 (link). For a given sample, gene expression values were rank-normalized and rank-ordered. The empirical cumulative distribution functions of the genes in the signature and the remaining genes were calculated. A statistic was calculated by an integration of the difference between the empirical cumulative distribution function, which is similar to the one used in gene set-enrichment analysis but based on absolute expression rather than differential expression.
We defined ssGSEA based on the signatures related to stromal tissue and immune cell infiltration as stromal and immune scores and combined the stromal and immune scores as the ‘ESTIMATE score’. The formula for calculating ESTIMATE-based tumour purity was developed in TCGA Affymetrix data (n=1,001) including both the ESTIMATE score and ABSOLUTE-based tumour purity. To develop a precise prediction model for tumour purity, we excluded six outliers from all Affymetrix data by computing a multivariate outlier criterion based on the generalized extreme studentized deviate test57 58 using the Bioconductor Parametric and Resistant Outlier Detection (PARODY) package (Supplementary Fig. S8a). Next, we entered both the ESTIMATE score and tumour purity to Eureqa Formuliza 0.97 Beta using the default setting59 . Eureqa attempts to design a mathematical formula that fits observed data employing an evolutionary algorithm60 (link). We obtained a fitted formula to predict tumour purity based on the ESTIMATE score. Finally, we applied this formula to the nonlinear least squares method (nls function for stats package) to determine the final formula for predicting tumour purity, as follows:
Tumour purity=cos (0.6049872018+0.0001467884 × ESTIMATE score). (1)
Yoshihara K., Shahmoradgoli M., Martínez E., Vegesna R., Kim H., Torres-Garcia W., Treviño V., Shen H., Laird P.W., Levine D.A., Carter S.L., Getz G., Stemke-Hale K., Mills G.B, & Verhaak R.G. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications, 4, 2612.
Publication 2013
Biological Evolution Gene Expression Genes Neoplasms Operator, Genetic Stromal Cells Tissues
2
Simulating Partial and Contaminated Genomes
Simulated genomes were generated from an initial set of 3604 draft genomes within IMG identified as being of high quality (see Supplemental Methods). To help alleviate bias toward well-sampled lineages, 280 of the 3604 high-quality draft genomes with identical phylogenetic marker genes were not used during the generation of simulated genomes. Simulated genomes were generated at varying degrees of completeness and contamination using three distinct random sampling models. Under the random fragment model, each contig comprising a genome was fragmented into nonoverlapping windows of a fixed size between 5 and 50 kbp. This size range was selected because it approximates the contig lengths of genomes recovered from metagenomic data or single-cell genomics: The mean N50 of the GEBA-MDM single-cell genomes, Wrighton acetate-amended aquifer population genomes, and Sharon infant gut population genomes is ∼28 kbp, ∼17 kbp, and ∼ 12 kbp, respectively. In order to generate genomes at a desired level of completeness and contamination, fragments were sampled without or with replacement, respectively. Windows were sampled until a simulated genome had completeness and contamination equal to or just greater than the target values. Generation of simulated genomes was limited to draft genomes as finished genomes were used to determine appropriate lineage-specific marker sets suitable for evaluating genomes (Fig. 3).
The 2430 draft reference genomes comprised of 20 or more contigs were used to simulate partial and contaminated genomes reflecting the characteristics of assembled contigs. Under this random contig model, genomes were generated by randomly removing contigs until the simulated genome reached or fell below a target completeness level. Contamination was introduced by randomly adding contigs with replacement from a single randomly selected genome until the desired level of contamination was reached or exceeded. These 2430 draft genomes were also used to generate genomes reflecting the limitations of metagenomic binning methods that rely on the statistical properties of contigs (e.g., tetranucleotide signature, coverage) to establish putative population genomes. To simulate this, partial genomes were generated by randomly removing contigs with a probability inversely proportional to their length until the simulated genome reached or fell below a target completeness level. Contamination was introduced by randomly selecting another draft reference genome and adding contigs from this genome with a probability inversely proportional to length until the desired level of contamination was reached or exceeded.
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
Acetate Aquifers Genes Genome Infant Metagenome
3
Comparative Protein Modeling with SWISS-MODEL
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.
Waterhouse A., Bertoni M., Bienert S., Studer G., Tauriello G., Gumienny R., Heer F.T., de Beer T.A., Rempfer C., Bordoli L., Lepore R, & Schwede T. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Research, 46(Web Server issue), W296-W303.
Publication 2018
Amino Acids Amino Acid Sequence Biological Evolution cDNA Library Hypersensitivity INDEL Mutation Protein Domain Proteins Protein Subunits Sequence Alignment
4
Benchmarking Cell Deconvolution Algorithms
We performed two in silico experiments to assess the detection limits of different deconvolution algorithms. In the first experiment (Supplementary Fig. 6), we used the same cell line GEPs described above to compare CIBERSORT and RLR with five other GEP deconvolution methods4 (link)–8 (link). We evaluated detection limit using Jurkat cells (spike-in concentrations of 0.5%, 1%, 2.5%, 5%, 7.5%, and 10%), whose reference GEP (median of three replicates in GSE11103) was added into randomly created background mixtures of the other three blood cell lines. Five mixtures were created for each spike-in concentration. Predicted Jurkat fractions were assessed in the presence of differential tumor content, which we simulated by adding HCT116 (described above) in ten even increments, from 0% to 90%. Of note, we also used the same marker or signature genes described for simulated tumors (above). In a second experiment (Supplementary Fig. 7a), we compared CIBERSORT with QP5 (link), LLSR4 (link), PERT6 (link), and RLR. We spiked naïve B cell GEPs from the leukocyte signature matrix into four random background mixtures of the remaining 21 leukocyte subsets in the signature matrix. The same background mixtures were used for each spike-in. We also tested the addition of unknown content by adding defined proportions (0 to 90%) of randomly permuted expression values from a naïve B cell reference transcriptome (median expression profile from samples used to build LM22, Supplementary Table 1). We then repeated this analysis for each of the remaining leukocyte subsets in LM22 (Supplementary Fig. 7b).
Newman A.M., Liu C.L., Green M.R., Gentles A.J., Feng W., Xu Y., Hoang C.D., Diehn M, & Alizadeh A.A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature methods, 12(5), 453-457.
Publication 2015
B-Lymphocytes BLOOD Cell Lines Cytosol Genes Jurkat Cells Leukocyte Count Leukocytes Neoplasms Transcriptome
5
Genome-wide RNAi Screens Identify KRAS-NF-κB Signaling
Large-scale, arrayed format RNAi screens to identify genes essential for proliferation/viability were performed as described3 (link),14 (link). The effect of introducing each of the 5002 shRNAs (targeting 957 genes) was determined in 19 cell lines, and normalized using the B-score metric4 (link). Feature selection of shRNA B-score data was performed using the Comparative Marker Application Suite in GenePattern5 (link) and was independently analyzed using RIGER analysis6 (link) to compute NES for each gene. Secondary screen viability data was normalized using a percent of control statistic, given the biased nature of the candidate shRNA plate. Expression profiling was used to generate a signature that correlates with KRAS activation and implicated NF-κB signaling in cell lines and tumors dependent on KRAS. Regulation of NF-κB by TBK1 was shown using biochemical and cell biological approaches. Details of the analytical methods are provided in the Full Methods.
Barbie D.A., Tamayo P., Boehm J.S., Kim S.Y., Moody S.E., Dunn I.F., Schinzel A.C., Sandy P., Meylan E., Scholl C., Fröhling S., Chan E.M., Sos M.L., Michel K., Mermel C., Silver S.J., Weir B.A., Reiling J.H., Sheng Q., Gupta P.B., Wadlow R.C., Le H., Hoersch S., Wittner B.S., Ramaswamy S., Livingston D.M., Sabatini D.M., Meyerson M., Thomas R.K., Lander E.S., Mesirov J.P., Root D.E., Gilliland D.G., Jacks T, & Hahn W.C. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(7269), 108-112.
Publication 2009
Biopharmaceuticals Cell Lines Cells Genes K-ras Genes Neoplasms RELA protein, human RNA Interference Short Hairpin RNA TBK1 protein, human

Most recents protocols related to «Protein Domain»

1
Interferon Signaling in ADAR1-Dependent Cancer Cells
Not available on PMC !

Example 9

An analysis of gene ontology (GO) categories associated with ADAR1 dependent cells revealed that NCI-H1650 and HCC366 (“HCC-366”), two ADAR1 dependent cell lines, both have elevated basal expression of interferon inducible genes (FIG. 35). The expression levels of interferon-inducible genes were also elevated in NCI-H196 cells (FIG. 36).

In light of the correlation between ADAR1 dependency and the expression of interferon-inducible genes, additional cancer cell lines from the Molecular Signatures Database (MSigDB) (Liberzon et al. (2015) Cell Systems 1:417-425) was examined. Cancer Cell Line Encyclopedia (CCLE) clustering was performed based on the Type I/Interferon-a gene set, which contained 97 genes including PKR. The resulting cluster included HCC366, NCI-H1650 and 9 additional lung cell lines. Among these cell lines, HCC1438 and NCI-H596 were sensitive to knockout of ADAR1 by lentiviral CRISPR-Cas9 (FIG. 37).

All the above-identified ADAR1 dependent cancer cell lines showed elevated interferon signaling markers, e.g., phosphorylation of STAT1 and expression of interferon-stimulated gene (ISGs) (FIG. 38). Elevated interferon signaling in the ADAR1 dependent cancer cell lines did not necessarily lead to PD-L1 overexpression (FIG. 38). Cell lines in the high interferon signaling cluster (LN215_CENTRAL_NERVOUS_SYSTEM, NCIH596_LUNG, HCC1438_LUNG, T3M10_LUNG, NCIH1869_LUNG, SW900_LUNG, HCC366_LUNG, SKLU1_LUNG, NCIH1650_LUNG, HCC4006_LUNG, and NCIH1648_LUNG) displayed high IFN-β, but not IFN-α (FIG. 39). As such, cancer cell lines sensitive to ADAR1 or ISG15 knockdown displayed elevated interferon secretion and downstream signaling. To further investigate the relationship between ADAR1 and IFN-β secretion, it was found that ADAR1 knockout led to amplified IFN-β secretion in cell lines primed with high basal interferon activation (FIG. 40). It was also found that ADAR1 dependent cell lines do not show enhanced sensitivity to IFN-α or IFN-β alone (FIG. 41).

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
Patent 2024
CD274 protein, human Cell Lines Cells Central Nervous System Clustered Regularly Interspaced Short Palindromic Repeats Gene Expression Genes Hypersensitivity Interferon-alpha Interferons Interferon Type I Light Lung Malignant Neoplasms Phosphorylation secretion STAT1 protein, human
2
Micropeptides Identified from lncRNAs
Not available on PMC !

Example 1

The authors of the invention have identified 3 micropeptides corresponding to sequences SEQ ID NO: 1, 2 and 3.

The micropeptide of SEQ ID NO 1 is a highly conserved 87 aa micropeptide whose sequence is:

(FIG. 1A)
MEGLRRGLSRWKRYHIKVHLADEALLLPLTVRPRDTLSDLRAQLVGQGVSS
WKRAFYYNARRLDDHQTVRDARLQDGSVLLLVSDPR.

In silico analysis of the amino acid sequence predicts a 3D structure resembling the protein UBIQUITIN (FIG. 1B). SEQ ID NO 1 micropeptide is coded by the lncRNA TINCR (LINC00036 in humans and Gm20219 in mice).

The micropeptide of SEQ ID NO: 2 is a 64-amino acid micropeptide whose sequence is:

(FIG. 2A)
MVRRKSMKKPRSVGEKKVEAKKQLPEQTVQKPRQECREAGPLFLQSRRETR
DPETRATYLCGEG.

It is encoded by ZEB2 antisense 1 (ZEB2AS1) long non-coding RNA (lncRNA). ZEB2AS1 is a natural antisense transcript corresponding to the 5′ untranslated region (UTR) of zinc finger E-box binding homeobox 2 (ZEB2). The ORF encoding the micropeptide spams part of the second and third exons of the lncRNA. I-Tasser, a 3D protein structure predictor, has been used in order to build a model of SEQ ID NO: 2 micropeptide 3D structure (FIG. 2B). Further in-silico analysis has revealed high amino acidic sequence conservation across the species and a potential cytoplasmatic localization of the micropeptide of SEQ ID NO: 2.

The micropeptide of SEQ ID NO: 3 is a 78-amino acid micropeptide encoded by the first exon of LINC0086 lncRNA. Its sequence, highly conserved across evolution is:

(FIG. 3A)
MAASAALSAAAAAAALSGLAVRLSRSAAARGSYGAFCKGLTRTLLTFFDLA
WRLRMNFPYFYIVASVMLNVRLQVRIE.

In silico analysis of this sequence predicted a tertiary structure (FIG. 3B) with a transmembrane domain at C-terminal of the protein and a signal peptide in the first 25 amino acids.

US11858972B2. Micropeptides and uses thereof (2024-01-02). FUNDACIÓ PRIVADA INSTITUT D'INVESTIGACIÓ ONCOLÒGICA DE VALL HEBRON [ES]. Inventors: Maria Abad Méndez [ES], Olga Boix Sánchez [ES], Emanuela Greco [ES], Iñaki Merino Valverde [ES].
Patent 2024
Amino Acids Amino Acid Sequence Biological Evolution Cytoplasm Exons Homo sapiens Integral Membrane Proteins Mice, House Protein Domain Proteins RNA, Long Untranslated Sequence Analysis Sequence Analysis, Protein Signal Peptides Ubiquitin Zinc Finger E-box Binding Homeobox 2
3
Identifying SLAM-Dependent Surface Lipoproteins
Not available on PMC !

Example 8

In selecting genomes for a given bacterial species where a SLAM homolog was identified, preference was given to reference genomes that contained fully sequenced genomes. SLAM homologs were identified using iterative Blast searches into closely related species to Neisseria to more distantly related species. For each of the SLAM homologs identified in these species, the corresponding genomic record (NCBI genome) was used to identify genes upstream and downstream along with their corresponding functional annotations (NCBI protein database, Ensembl bacteria). In a few cases, no genes were predicted upstream or downstream of the SLAM gene as they were too close to the beginning or end of the contig, respectively, and thus these sequences were ignored.

Neighbouring genes were analyzed for 1) an N-terminal lipobox motif (predicted using LipoP, SignalP), and 2) a solute binding protein, Tbp-like (InterPro signature: IPR or IPR011250), or pagP-beta barrel (InterPro signature: IPR011250) fold. If they contained these elements, we identified the adjacent genes as potential SLAM-dependent surface lipoproteins.

A putative SLAM (PM1515, SEQ ID NO: 1087) was identified in Pasteurella multocida using the Neisseria SLAM as a search. The putative SLAM (PM1515, SEQ ID NO: 1087) was adjacent to a newly predicted lipoprotein gene with unknown function (PM1514, SEQ ID NO: 1083) (FIG. 11A). The putative SLAM displayed 32% identity to N. meningitidis SLAM1 while the SLP showed no sequence similarity to known SLAM-dependent neisserial SLPs.

The putative SLAM (PM1515, SEQ ID NO: 1087) and its adjacent lipoprotein (PM1514, SEQ ID NO: 1083) were cloned into pET26b and pET52b, respectively, as previously described and transformed into E. coli C43 and grown overnight on LB agar supplemented with kanamycin (50 ug/ml) and ampicillin (100 ug/ml).

Cells were grown in auto-induction media for 18 hours at 37 C and then harvested, washed twice in PBS containing 1 mM MgCl2, and labeled with α-Flag (1:200, Sigma) for 1 hr at 4 C. The cells were then washed twice with PBS containing 1 mM MgCl2 and then labeled with R-PE conjugated α-mouse IgG (25 ug/mL, Thermo Fisher Scientific) for 1 hr at 4 C. following straining, cells were fixed in 2% formaldehyde for 20 minutes and further washed with PBS containing 1 mM MgCl2. Flow Cytometry was performed with a Becton Dickinson FACSCalibur and the results were analyzed using FLOWJO software. Mean fluorescence intensity (MFI) was calculated using at least three replicates was used to compare surface exposure the lipoprotein in strains either containing or lacking the putative SLAM (PM1515) and are shown in FIG. 11C and FIG. 11D. PM1514 could be detected on the surface of E. coli illustrating i) that SLAM can be used to identify SLPs and ii) that SLAM is required to translocate these SLPs to the surface of the cell—thus identifying a class of proteins call “SLAM-dependent surface lipoproteins”. Antibodies were raised against purified PmSLP (PM1514) and the protein was shown to be on the surface of Pasteurella multocida via PK shaving assays.

US11872275B2. SLAM polynucleotides and polypeptides and uses thereof (2024-01-16). ENGINEERED ANTIGENS INC. [CA]. Inventors: Trevor F. Moraes [CA], Christine Chieh-Lin Lai [CA], Yogesh Hooda [CA], Andrew Judd [CA], Anthony B. Schryvers [CA], Scott D. Gray-Owen [CA].
Patent 2024
Agar Ampicillin Antibodies Bacteria Binding Proteins Biological Assay Cells Escherichia coli Flow Cytometry Fluorescence Formaldehyde Genes Genome Kanamycin Lipoprotein (a-) Lipoproteins Magnesium Chloride Mus Neisseria Neisseria meningitidis Pasteurella multocida Proteins Staphylococcal Protein A Strains
4
Chemically Modified miRNAs for Intracellular Targeting
Not available on PMC !

Example 1

miRNAs with naturally occurring sequences were fused covalently to phosphorothioated ssDNA (PS) 20meric oligo to facilitate cellular internalization targeting intracellular molecular targets. A non-phosphorothioated, phosphodiester ssDNA oligo (PO) extension of the miRNAs was employed as a non-internalizing control.

Applicants modified naturally occurring miRNAs, for example, let7a-3p (SEQ ID NO:1) (FIG. 1), let7a-5p (SEQ ID NO:2) (FIG. 3), miR17-3p (SEQ ID NO:3) (FIG. 5), miR17-5p (SEQ ID NO:4) (FIG. 7), and miR218-5p (SEQ ID NO:5) (FIG. 9) by attaching a phosphorothioated ssDNA (PS) 20meric oligo to the 3′ end of the miRNAs via a chemical linker. Examples of a phosphorothioated ssDNA (PS) 20meric oligo include, but are not limited to, SEQ ID NO:6 (TCCATGAGCTTCCTGATGCT) and SEQ ID NO:7 (AGCATCAGGAAGCTCATGGA). Applicants designed that the modification by ssDNA oligo avoids any C/G or G/C motifs, because it is known that CpG oligodeoxynucleotides (CpG-ODN) involve undesired Toll-like receptor (TLR) engagement and subsequent intracellular signaling. Applicants used an alkyl chain harboring a fluorophore as a linker to track the conjugate molecule.

US11857633B2. Phosphorothioate-conjugated miRNAs and methods of using the same (2024-01-02). City of Hope [US]. Inventors: Andreas Herrmann [US], Hua Yu [US].
Patent 2024
Acids Cell Nucleus Cells CPG-ODN DNA, Single-Stranded MicroRNAs Oligonucleotides Protoplasm Toll-Like Receptors
5
CasX CRISPR Plasmid Interference in E. coli
Not available on PMC !

Example 2

FIGS. 4A-4C. Plasmid Interference by CasX expressed in E. coli. Experimental design of CasX plasmid interference. Competent E. coli cells expressing the minimal interference CasX locus (acquisition proteins removed) were prepared. These cells were transformed with a plasmid containing a match to the spacer in the CasX CRISPR locus (target) or not (non-target) and plated on media containing antibiotic selection for the CRISPR and target plasmid. Successful plasmid interference results in reduced number of transformed colonies for the target plasmid. FIG. 4B cfu/ug of transformed plasmid containing spacer from CasX1 (sX1), spacer from CasX2 (sX2) or a non-target plasmid containing a random 30 nt sequence. FIG. 4C serial dilution was performed of transformants from FIG. 4B on media containing antibiotic selection for both the CRISPR and target plasmid.

FIGS. 5A-5B PAM dependent plasmid interference by CasX. PAM depletion assays were conducted with CasX. E. coli containing the CasX CRISPR locus were transformed with a plasmid library with 7 nucleotides randomized 5′ or 3′ of the target sequence. The target plasmid was selected for and transformants were pooled. The randomized region was amplified and prepared for deep sequencing. Depleted sequences were identified and used to generate a PAM logo. FIG. 5B PAM logo generated for deltaproteobacteria CasX showed a strong preference for sequences containing a 5′-TTCN-3′ flanking sequence 5′ of the target. A 3′ PAM was not detected. c, PAM logo generated for planctomyces CasX showed a strong preference for sequences containing a 5′-TTCN-3′ flanking sequence 5′ of the target with lower stringency at the first T. A 3′ PAM was not detected.

FIGS. 6A-6C. CasX is a dual-guided CRISPR-Cas effector complex. FIG. 6A CRISPR locus for tracrRNA knockout experiments and sgRNA tests. FIG. 6B colony forming units (cfu) per g of transformed plasmid containing a target or non-target sequence. Deletion of the tracrRNA resulted in ablation of plasmid interference. Expression of a synthetic sgRNA in place of the tracrRNA and CRISPR array resulted in robust plasmid interference by CasX. FIG. 6C diagram of sgRNA design (derived from tracrRNA and crRNA sequences for CasX1). The tracrRNA (green) was joined to the crRNA (repeat, black; spacer, red) by a tetraloop (GAAA).

FIG. 7. Schematic of CasX RNA guided DNA interference. CasX binds to a tracrRNA (green) and the crRNA (black, repeat; red, spacer). Base pairing of the guide RNA to the target sequence (blue) containing the correct protospacer adjacent motif (yellow) results in double stranded cleavage of the target DNA. The depicted sequences are derived from tracrRNA and crRNA sequences for CasX1.

US11873504B2. RNA-guided nucleic acid modifying enzymes and methods of use thereof (2024-01-16). The Regents of the University of California [US]. Inventors: Jennifer A Doudna [US], Jillian F. Banfield [US], David Burstein [US], Lucas Benjamin Harrington [US], Steven C. Strutt [US].
Patent 2024
Antibiotics Biological Assay Cells Clustered Regularly Interspaced Short Palindromic Repeats CRISPR Loci crRNA, Transactivating Deletion Mutation Deltaproteobacteria DNA Cleavage DNA Library Enzymes Escherichia coli Nucleic Acids Nucleotides Plasmids Proteins RNA, CRISPR Guide Technique, Dilution

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More about "Protein Domain"

Protein domains are the fundamental building blocks that make up proteins, serving as the functional and structural units responsible for the diverse biological activities carried out by these macromolecules.
These discrete segments of protein sequence and structure can act independently and be recombined to create new proteins with different functions, enabling the evolution and diversity of the proteome.
Understanding the composition and organization of protein domains is crucial for predicting protein function, structure, and interactions - key information for drug design, disease research, and other biotechnology applications.
Techniques like nuclear extraction (using kits like the Nuclear Extract Kit), chromatin immunoprecipitation (ChIP-IT Express and ChIP-IT High Sensitivity), RNA extraction (TRIzol reagent and RNeasy Mini Kit), and reporter assays (Dual-Luciferase Reporter Assay System) are often employed to study protein domains and their interactions.
PubCompare.ai's AI-driven platform helps streamline protein domain analysis by locating the best research protocols from literature, pre-prints, and patents, using advanced comparisons to identify the optimal methods and products.
This enhances the reproducibility and reliability of your findings, whether you're working with next-generation sequencing instruments like the NextSeq 500 and HiSeq 2500, or utilizing transfection reagents like Lipofectamine 2000.
By optimizing your research protocols and enhancing the quality of your data, PubCompare.ai empowers you to make more informed decisions and drive your protein domain research forward with greater confidence and efficiency.