MSRB6 Antibody
Description
Biological Context of Methionine Sulfoxide Reductases
Methionine residues in proteins are highly susceptible to oxidation, forming methionine sulfoxide (Met-SO). Msr enzymes reverse this damage, maintaining protein function under oxidative stress .
Key Msr Subfamilies and Localization
| Msr Type | Substrate Specificity | Localization |
|---|---|---|
| MsrA | Met-S-SO | Cytosol, nucleus, mitochondria |
| MsrB1 | Met-R-SO | Cytosol, nucleus |
| MsrB2 | Met-R-SO | Mitochondria |
| MsrB3 | Met-R-SO | ER, mitochondria |
MsrB enzymes require selenium (as selenocysteine in MsrB1) for activity, linking them to antioxidant defense systems .
Antibodies Targeting Msr Proteins
Antibodies against Msr enzymes are primarily used to study their expression, localization, and functional roles in diseases such as neurodegeneration, cancer, and bacterial infections.
Applications of Msr Antibodies:
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Mechanistic Studies:
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Therapeutic Potential:
Research Findings on MsrB Antibodies
While no studies directly address "MSRB6," insights from related MsrB antibodies include:
Table: Functional Insights from MsrB Antibody Studies
Challenges and Opportunities
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Nomenclature Gaps: The term "MSRB6" may reflect an uncharacterized or non-standard isoform; current databases (e.g., UniProt, PubMed) lack entries for this designation.
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Antibody Development: Advances in antibody engineering (e.g., Fc glycosylation, affinity maturation) could enable precise targeting of Msr isoforms .
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Multiplex Assays: Hydrogel microparticle-based platforms allow high-throughput screening of antibodies, potentially accelerating MsrB antibody optimization .
Product Specs
Target Background
Q&A
The following FAQs for MSRB6 antibody research are synthesized using Google's "People Also Ask" framework, optimized for academic rigor and methodological depth. These questions address experimental design, data interpretation, and advanced research challenges while excluding commercial considerations.
What experimental strategies resolve contradictions between MSRB6 mRNA expression levels and protein detection data?
Advanced resolution workflow:
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Post-translational analysis:
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Perform cycloheximide chase assays (0-24hr) with proteasome inhibitor MG132 (10µM)2
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Quantify protein half-life using densitometry (ImageLab vs. ImageJ comparison)
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Antibody-epitope mapping:
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Use PepSpot™ membrane with 15-mer overlapping peptides (5aa shift)
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Identify conformational vs linear epitopes through reducing/non-reducing PAGE
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Common Data Discrepancy Patterns:
| Scenario | Likely Cause | Validation Experiment |
|---|---|---|
| High mRNA, low protein | Rapid turnover | Pulse-chase with 35S-methionine |
| Low mRNA, high protein | Antibody cross-reactivity | siRNA rescue + MS/MS verification |
Which cellular stress models best characterize MSRB6's methionine sulfoxide reductase activity?
Prioritized experimental systems:
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Oxidative challenge: H₂O₂ gradient (0-500µM, 0-6hr) with parallel thioredoxin reductase inhibition2
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Hypoxia-reoxygenation: 1% O₂ ×24hr → 21% O₂ ×2hr
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Pharmacological modulation:
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Methionine sulfoxide (MetO) supplementation (0.1-5mM)
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MSRB6 inhibitor aurin tricarboxylic acid (ATA) dose-response (IC₅₀ determination)
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Key readouts:
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Redox-sensitive GFP (roGFP) targeted to mitochondrial matrix
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Site-specific MetO quantification via LC-MS/MS (Cys-72 vs Cys-218 oxidation states)
How to design a CRISPR interference (CRISPRi) screen for MSRB6 interaction partners?
Advanced workflow:
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Library design:
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Focused sub-library: 5,000 genes from mitochondrial proteome + redox regulators
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sgRNA design using MIT CRISPR Design Tool (≥3 guides/gene, 20bp length)
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Screening conditions:
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Dual selection: 50µM MetO + 100µM paraquat ×7 days
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FACS-based enrichment: Top/bottom 10% by MitoSOX Red intensity
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Hit validation:
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Reciprocal co-IP with MSRB6-FLAG/HA tags
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Structural modeling using AlphaFold2 multimer (v2.3.2)
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What statistical approaches are robust for MSRB6 knockout phenotyping in multi-omics datasets?
Analysis framework:
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Bulk RNA-seq: DESeq2 with independent hypothesis weighting (IHW) for mitochondrial pathways
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Metabolomics: Mixed-effects models (MEM) for TCA cycle intermediates
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Integration method: MOFA+ (v1.8) for cross-omic factor analysis
Critical parameters:
| Data Type | Batch Effect Control | Multiple Testing Correction |
|---|---|---|
| Proteomics | ComBat Harmonization | Benjamini-Yekutieli (FDR <0.1) |
| Lipidomics | SERRF Normalization | Storey's π₁ Estimation |
