At3g20030 Antibody
Description
Overview of At3g20030 Antibody
The At3g20030 Antibody is a polyclonal antibody raised against the recombinant Arabidopsis thaliana (mouse-ear cress) protein At3g20030. Marketed under the product code CSB-PA881744XA01DOA, it is designed for research applications, including ELISA (Enzyme-Linked Immunosorbent Assay) and Western Blot (WB), to detect and analyze the At3g20030 protein in plant biology studies .
Functional Characteristics
-
Target Identification: Recognizes the At3g20030 protein in Arabidopsis thaliana, enabling its detection in cellular extracts or purified samples .
-
Applications: Primarily used for ELISA (antigen quantification) and WB (protein localization/abundance analysis) in plant molecular biology research .
-
Specificity: No cross-reactivity is reported for non-Arabidopsis species based on product documentation .
Target Protein (At3g20030)
The At3g20030 gene encodes a protein of unknown function in Arabidopsis thaliana. While its exact role remains uncharacterized in published literature, antibodies like CSB-PA881744XA01DOA are critical tools for studying its expression patterns, subcellular localization, and interactions in plant development or stress responses.
Potential Applications
-
Protein Expression Analysis:
-
Quantifying At3g20030 levels in wild-type vs. mutant Arabidopsis lines.
-
Investigating tissue-specific expression (e.g., roots, leaves, flowers).
-
-
Interaction Studies:
-
Identifying binding partners via co-immunoprecipitation (Co-IP) or affinity chromatography.
-
-
Subcellular Localization:
-
Using immunofluorescence microscopy to determine the protein’s cellular compartment (e.g., nucleus, cytoplasm, membranes).
-
Experimental Constraints
-
Lack of Published Studies: No peer-reviewed studies explicitly using the At3g20030 Antibody are cited in the provided sources. Data are primarily derived from product specifications .
-
Validation Requirements:
-
Positive Controls: Recombinant At3g20030 protein or overexpressing Arabidopsis lines.
-
Negative Controls: Non-transfected Arabidopsis or unrelated proteins.
-
-
Cross-Species Reactivity: Not tested in other plant species (e.g., Nicotiana benthamiana, Zea mays).
Comparative Analysis with Other Arabidopsis Antibodies
| Antibody | Target Gene | Product Code | Applications |
|---|---|---|---|
| At3g20030 Antibody | At3g20030 | CSB-PA881744XA01DOA | ELISA, WB |
| At5g06550 Antibody | At5g06550 | CSB-PA727841XA01DOA | ELISA, WB |
| At4g29970 Antibody | At4g29970 | CSB-PA879894XA01DOA | ELISA, WB |
| FAD2 Antibody | FAD2 | CSB-PA342791XA01DOA | ELISA, WB |
Note: These antibodies share similar technical specifications (e.g., rabbit polyclonal origin, IgG isotype) but target distinct proteins in Arabidopsis .
Product Specs
Composition: 50% Glycerol, 0.01M PBS, pH 7.4
Q&A
Here’s a structured collection of FAQs tailored for researchers working with the At3g20030 antibody, informed by recent advances in specificity-driven antibody design methodologies :
How do I design phage display experiments to isolate high-specificity At3g20030 antibodies?
Methodological Answer:
-
Use a germline library with systematic variation in the CDR3 region (e.g., 4 consecutive positions randomized across 20 amino acids) to balance diversity and sequencing coverage .
-
Perform pre-selection steps against naked magnetic beads to deplete nonspecific binders.
-
Include multiple ligand complexes (e.g., DNA hairpins immobilized on beads) during selection to disentangle epitope-specific binding modes.
-
Validate using high-throughput sequencing to track antibody population dynamics across selection rounds (Fig A in S1 Text ).
What computational models are effective for predicting antibody specificity?
Methodological Answer:
-
Implement a biophysics-informed model that assigns binding energies () to distinct modes (e.g., bead-bound, DNA-bound).
-
Train the model on multi-experiment data (e.g., selections against Black, Blue, and Mix complexes) to infer hidden binding modes.
-
Use shallow neural networks to parameterize energy functions and simulate selection outcomes (Equation 1 ).
How can I validate antibody specificity for structurally similar ligands?
Methodological Answer:
-
Conduct counter-selection experiments against off-target ligands (e.g., competing DNA hairpins).
-
Compare model-predicted enrichment profiles (e.g., for Black vs. Blue ligands) with empirical sequencing data (Fig 2B–D ).
-
Use energy minimization to generate variants with customized specificity and test them experimentally.
How do I resolve contradictions between predicted and observed binding affinities?
Methodological Answer:
-
Analyze discrepancies through mode disentanglement: Assign conflicting data to pseudo-modes (e.g., phage amplification biases) not directly tied to ligand binding.
-
Validate model assumptions using independent test sets (e.g., predicting Black ligand selection from Mix/Beads data) (Fig 2D ).
-
Refine energy function parameterizations if codon-level biases or amplification artifacts are detected (Fig H in S1 Text ).
What strategies optimize cross-specificity without sacrificing affinity?
Methodological Answer:
| Approach | Description | Application Example |
|---|---|---|
| Joint energy minimization | Minimize for multiple target ligands | Designing antibodies binding both Black and Blue DNA hairpins |
| Mode exclusion | Maximize for undesired ligands while minimizing for targets | Eliminating bead-binding modes while retaining hairpin specificity |
How can biophysical models improve interpretability in antibody engineering?
Methodological Answer:
-
Decompose selection outcomes into thermodynamic modes (e.g., bead-bound vs. unbound) to quantify epitope contributions.
-
Use the model to simulate hypothetical experiments (e.g., novel ligand combinations) and prioritize high-potential variants.
-
Validate model interpretability by correlating inferred modes with structural epitope mapping (Fig C in S1 Text ).
Methodological Considerations Table
| Challenge | Solution | Validation Metric |
|---|---|---|
| Low-abundance variants in libraries | Deep sequencing with >48% coverage | Variant recovery rate post-selection |
| Disentangling similar epitopes | Multi-ligand selection + mode-specific modeling | Prediction accuracy for unseen ligand combinations |
| Cross-reactivity optimization | Energy function-based sequence generation | ELISA binding ratios (target/off-target) |
