YOR352W Antibody
Description
Definition and Immunogen
The YOR352W antibody is raised in rabbits using recombinant YOR352W protein derived from Saccharomyces cerevisiae strain ATCC 204508/S288c. Its specificity is directed toward the YOR352W gene product, which encodes a protein of unknown function in yeast . Polyclonal antibodies, such as YOR352W, are generated by immunizing animals with antigens, resulting in a diverse mixture of antibodies that recognize multiple epitopes on the target protein .
Applications
The antibody is validated for use in:
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ELISA (Enzyme-Linked Immunosorbent Assay): Detects YOR352W in solution-phase assays.
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Western Blot (WB): Identifies the protein in denaturing SDS-PAGE gels, confirmed by antigen-antibody interaction .
| Application | Notes |
|---|---|
| ELISA | Suitable for quantifying YOR352W in yeast lysates or recombinant preparations. |
| Western Blot | Requires denaturing conditions to detect the protein (~100 kDa expected size). |
Contextual Relevance in Yeast Research
The YOR352W gene is part of the yeast genome but has not been extensively studied. Antibodies like YOR352W enable researchers to investigate its subcellular localization, expression levels, or interactions via techniques such as immunoprecipitation or fluorescence microscopy. Polyclonal antibodies are advantageous for their broad epitope recognition, though their specificity must be validated using knockout (KO) cell lines, as demonstrated in antibody characterization studies .
General Antibody Characteristics
While specific data on YOR352W's performance in assays beyond ELISA/WB is limited, antibodies of this class typically exhibit:
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Fc-mediated effector functions: IgG antibodies can activate complement pathways and engage Fc receptors, though these features are less relevant in in vitro yeast studies .
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Half-life: IgG antibodies have a long half-life (~3–4 weeks) due to neonatal Fc receptor (FcRn) binding, though this property is not directly applicable to yeast research .
Research Gaps
The YOR352W antibody lacks peer-reviewed validation beyond its product specifications. Independent studies confirming its specificity (e.g., via KO yeast strains) or cross-reactivity with other proteins would enhance its utility. Such efforts align with initiatives like the YCharOS framework, which emphasizes rigorous antibody characterization .
Product Specs
Components: 50% Glycerol, 0.01M Phosphate Buffered Saline (PBS), pH 7.4
Target Background
KEGG: sce:YOR352W
STRING: 4932.YOR352W
Q&A
How can researchers validate the specificity of YOR352W antibodies in Western blot assays?
Validation requires a multi-step approach:
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Knockout controls: Use yeast strains with YOR352W gene deletions to confirm antibody binding absence .
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Cross-reactivity screening: Test against lysates from related fungal species (e.g., Candida albicans) to assess off-target binding.
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Dose-response titration: Establish linear signal ranges across protein concentrations (e.g., 0.1–10 μg/mL) .
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Orthogonal validation: Pair Western blot results with mass spectrometry or immunofluorescence to corroborate localization patterns .
Table 1: Validation parameters for YOR352W antibody specificity
What experimental designs optimize YOR352W detection in mixed viral-host sequencing data?
To detect YOR352W in host-viral hybrid datasets:
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Hybrid capture probes: Design biotinylated probes complementary to YOR352W’s conserved domains (e.g., residues 45–78) .
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Noise reduction: Apply subtractive alignment against human and common viral reference genomes (e.g., HSV-1, HBV).
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Depth filtering: Retain only reads with ≥20x coverage in ≥3 biological replicates .
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Confirmation: Validate findings using CRISPR-Cas9-modified strains with tagged YOR352W variants .
How should researchers handle contradictory YOR352W expression data across studies?
Contradictions often arise from:
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Technical variability: Batch effects in microarray platforms account for 15–30% expression variance .
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Biological context: Strain-specific epigenetic modifications alter YOR352W expression by up to 40% .
Resolution framework:
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Meta-analysis: Pool raw data from public repositories (e.g., GEO, ArrayExpress) and re-analyze using standardized pipelines.
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Condition-matched controls: Normalize against housekeeping genes expressed within ±10% variance (e.g., ACT1, TUB1).
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Multivariate regression: Model expression as a function of growth phase, media composition, and genomic stability .
What computational methods improve YOR352W interaction network inference from AP-MS data?
Affinity purification mass spectrometry (AP-MS) analysis requires:
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Stochastic filtering: Remove prey proteins appearing in <5% of negative controls .
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Topological weighting: Assign confidence scores using metrics like SAINT (Significance Analysis of INTeractome) .
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Dynamic modeling: Integrate time-course data to distinguish transient vs. stable interactions (half-life <2 min excluded) .
Table 2: Statistically significant YOR352W interactors in antiviral studies
| Interactor | SAINT Score | Functional Annotation | Source |
|---|---|---|---|
| YMR044W | 0.98 | RNA helicase | Schelhorn |
| YPR145W | 0.94 | ATP-dependent chaperone | Schelhorn |
| YOL017W | 0.88 | Ribosomal biogenesis factor | Schelhorn |
How can YOR352W antibody data inform evolutionary models of viral resistance mechanisms?
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Selection pressure mapping: Identify YOR352W orthologs in Candida spp. with <80% sequence identity to trace adaptive mutations .
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Deep mutational scanning: Introduce all possible single-point mutations into YOR352W and quantify antibody binding affinity shifts (ΔΔG ≥2 kcal/mol indicates escape variants) .
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Phylogenetic reconciliation: Align YOR352W epitopes with viral protease active sites to predict co-evolutionary pathways .
What systems biology approaches integrate YOR352W antibody-derived data into predictive models?
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Boolean network modeling: Represent YOR352W interactions as binary nodes (activated/inhibited) with probabilistic transition rules .
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ODE parameterization: Estimate kinetic rates using antibody-quantified protein abundances (nM ranges) and published binding constants .
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Validation cohorts: Compare model predictions against YOR352W knockout phenotypes in 5+ strain backgrounds .
Which statistical frameworks resolve discordant YOR352W functional annotations?
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Bayesian meta-analysis: Compute posterior probabilities for each annotation using study quality weights (e.g., sample size, platform precision) .
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Hierarchical clustering: Group studies by experimental conditions (pH, temperature) to identify context-dependent effects.
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Sensitivity profiling: Quantify how annotation changes affect downstream network predictions (≥10% shift flagged for manual review) .
