At1g60570 Antibody
Description
Introduction to At1g60570 Antibody
The At1g60570 Antibody is a research-grade immunoglobulin designed to detect and study the protein encoded by the At1g60570 gene in Arabidopsis thaliana (Mouse-ear cress). This antibody is critical in plant biology for investigating protein localization, function, and molecular interactions. Below is a structured analysis of its characteristics, applications, and research findings.
Gene Background and Antibody Characteristics
The At1g60570 gene encodes a protein of unknown function, though preliminary data suggest involvement in cellular processes specific to Arabidopsis. The antibody is classified as a polyclonal or monoclonal reagent, depending on production methods, and is validated for use in western blotting, immunoprecipitation, and immunohistochemistry.
Protein Localization Studies
The antibody is used to map the subcellular localization of the At1g60570 protein in Arabidopsis tissues. For example, studies may focus on its distribution in chloroplasts, mitochondria, or plasma membranes, aiding in functional annotation.
Interaction Studies
Western blotting and immunoprecipitation experiments employing this antibody can identify protein-protein interactions involving At1g60570. For instance, co-purification with stress-response proteins might indicate roles in abiotic stress adaptation.
Validation and Specificity
Antibody specificity is critical to avoid false positives. Common validation methods include:
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Western blot controls: Testing lysates from At1g60570-knockout plants to confirm absence of signals.
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Immunoprecipitation: Verifying pull-down of a single band corresponding to the predicted molecular weight.
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Cross-reactivity checks: Ensuring no binding to homologous proteins in other species.
Note: Commercial antibodies for plant proteins often face specificity challenges . Rigorous validation is recommended to confirm target recognition.
Future Directions
Product Specs
Constituents: 50% Glycerol, 0.01M Phosphate Buffered Saline (PBS), pH 7.4
Q&A
Here’s a structured collection of FAQs tailored for academic researchers working with the At1g60570 antibody, based on experimental and computational methodologies from recent studies:
How do I design phage display experiments to select At1g60570 antibodies with high specificity?
Methodology:
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Use a minimal antibody library (e.g., a naïve human V domain with variations in CDR3 positions) to balance sequence diversity and experimental tractability .
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Perform counter-selection with "naked" magnetic beads to pre-deplete non-specific binders before selecting against DNA hairpin-coated bead complexes (e.g., "Black" or "Blue" ligands) .
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Include two rounds of selection with amplification steps to enrich high-affinity binders. Monitor sequence evolution via high-throughput sequencing at each step .
What validation methods ensure antibody specificity for structurally similar epitopes?
Approach:
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Use cross-competition assays with soluble ligands to confirm selective binding.
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Apply fluorescence-activated cell sorting (FACS) for quantitative specificity profiling across target and non-target ligands .
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Validate computational predictions via surface plasmon resonance (SPR) or biolayer interferometry (BLI) to measure binding kinetics (e.g., K<sub>D</sub>, k<sub>on</sub>, k<sub>off</sub>) .
How can computational models resolve contradictory specificity data from selection experiments?
Framework:
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Implement a biophysics-informed neural network that disentangles binding modes (e.g., bead-bound vs. ligand-bound states) .
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Train the model on multi-ligand selection data (e.g., "Mix" experiments) to infer hidden thermodynamic contributions to specificity .
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Use energy minimization to design variants with customized profiles (e.g., cross-specificity for homologous antigens) .
Example Workflow:
| Step | Action | Outcome |
|---|---|---|
| 1 | Train model on "Black" and "Blue" ligand selections | Identifies distinct binding modes |
| 2 | Predict binding for untested ligand combinations | Validates model generalizability |
| 3 | Generate sequences via E<sub>sw</sub> optimization | Obtains antibodies with tailored specificity |
What strategies mitigate experimental biases in antibody selection?
Solutions:
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Pseudo-mode modeling: Account for non-binding biases (e.g., phage production inefficiencies) during computational analysis .
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Codon-level analysis: Verify absence of nucleotide-level selection biases to confirm amino acid-driven specificity .
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Multi-experiment training: Combine data from selections against isolated and mixed ligands to reduce overfitting .
How to handle low-affinity antibodies persisting after selection?
Recommendations:
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Increase selection stringency (e.g., reduce ligand concentration in later rounds).
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Apply energy-based filtering during computational design to exclude sequences with unfavorable E<sub>sw</sub> values for non-target ligands .
What computational tools predict cross-specificity risks for therapeutic candidates?
Tools:
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Mode-specific energy landscapes: Compare E<sub>sw</sub> values across homologous antigens (e.g., human vs. murine targets) .
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In silico counter-selection: Maximize energy penalties for binding to off-target epitopes during sequence optimization .
