At3g09800 Antibody

What is At3g09800 and why is it significant in plant research?

At3g09800 is a gene in Arabidopsis thaliana that encodes a protein involved in plant growth and development pathways. The protein produced by this gene participates in essential cellular processes, potentially interacting with brassinosteroid (BR) signaling components. Brassinosteroids are crucial phytohormones perceived by cell surface receptor kinases like BRI1, which trigger downstream signaling cascades affecting plant development . Studying At3g09800 through antibody-based techniques allows researchers to investigate protein localization, expression levels, and interactions with other proteins in the BR signaling pathway, providing insights into fundamental plant biology.

What are the optimal sample preparation methods for At3g09800 antibody experiments?

Sample preparation for At3g09800 antibody experiments requires careful protein extraction protocols to maintain protein structure and antigenicity. Begin with flash-freezing plant tissue in liquid nitrogen followed by grinding to a fine powder. For total protein extraction, use a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, and protease inhibitor cocktail. For nuclear protein enrichment, employ differential centrifugation techniques. After extraction, samples should be clarified by centrifugation at 12,000 × g for 15 minutes at 4°C. Protein concentration should be determined using Bradford or BCA assays prior to immunoblotting or immunoprecipitation. For immunoprecipitation experiments, pre-clearing lysates with Protein A/G agarose reduces non-specific binding. Following protocols similar to those used for other plant transcription factors can improve success rates, as seen in studies of BZR1-interacting proteins .

How do I validate At3g09800 antibody specificity for my experiments?

Antibody validation is critical for ensuring experimental reliability. For At3g09800 antibody validation, implement multiple complementary approaches:

• Western blot analysis using both wild-type samples and knockout/knockdown lines, expecting absence of signal in the latter

• Peptide competition assay where pre-incubation with the immunizing peptide should abolish the antibody signal

• Immunoprecipitation followed by mass spectrometry to confirm antibody pulls down the target protein

• Testing antibody on recombinant At3g09800 protein with epitope tags for confirmation

A thorough validation strategy similar to that used for BZR1-MH and bzr1-1D-MH fusion proteins should be employed, including probing with appropriate secondary antibodies and using imaging systems like the Odyssey Infrared Imaging System for detection . Additionally, include positive controls such as known brassinosteroid signaling components and negative controls to establish specificity boundaries.

What are the optimal conditions for using At3g09800 antibodies in Western blotting?

For Western blotting with At3g09800 antibodies, optimal conditions include:

• Protein separation: Use 10-12% SDS-PAGE gels, loading 20-50 μg of total protein per lane

• Transfer: Semi-dry transfer at 15V for 45 minutes onto PVDF membranes (0.45 μm pore size)

• Blocking: 5% non-fat dry milk in TBST (TBS with 0.1% Tween-20) for 1 hour at room temperature

• Primary antibody incubation: Dilute At3g09800 antibody 1:1000 in blocking solution, incubate overnight at 4°C

• Washing: 3 × 10 minutes with TBST

• Secondary antibody: Use IRDye 800-conjugated anti-mouse/rabbit IgG (depending on primary antibody host) at 1:10,000 dilution for 1 hour at room temperature

• Detection: Develop using an infrared imaging system similar to the Odyssey system used for BZR1 detection

Include molecular weight markers to confirm the expected size of At3g09800 protein. For enhanced detection of low-abundance proteins, consider using chemiluminescent substrates with longer exposure times. Always run positive and negative controls alongside experimental samples to validate results.

How can I optimize immunoprecipitation protocols for At3g9800 protein interaction studies?

Optimizing immunoprecipitation (IP) protocols for At3g9800 protein interaction studies requires careful consideration of buffer components, incubation conditions, and washing stringency. Follow this approach:

• Lysate preparation: Extract proteins in a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.5% NP-40, 1 mM EDTA, 3 mM DTT, and protease inhibitor cocktail

• Pre-clearing: Incubate lysate with Protein A/G beads for 1 hour at 4°C to reduce non-specific binding

• Antibody binding: Add 2-5 μg of At3g9800 antibody to 1 mg of pre-cleared lysate, incubate overnight at 4°C with gentle rotation

• Bead capture: Add 40 μl of Protein A/G beads, incubate for 3 hours at 4°C

• Washing: Perform 5 washes with washing buffer (similar to lysis buffer but with 0.1% NP-40)

• Elution: Use either low-pH glycine buffer (pH 2.8) followed by neutralization, or SDS sample buffer heated to 95°C for 5 minutes

For detecting interacting partners, consider employing techniques similar to those used for BZR1-interacting proteins, where TEV protease was used to release target proteins from IgG-bound proteins . This approach can help maintain protein-protein interactions while reducing background.

What are effective strategies for using At3g9800 antibodies in immunolocalization studies?

For effective immunolocalization of At3g9800 protein in plant tissues:

• Fixation: Fix tissue samples in 4% paraformaldehyde in PBS for 2 hours at room temperature

• Embedding: Embed in paraffin or prepare for cryosectioning (10-15 μm sections)

• Antigen retrieval: Perform citrate buffer (pH 6.0) heat-induced epitope retrieval

• Blocking: Use 3% BSA with 0.1% Triton X-100 in PBS for 1 hour at room temperature

• Primary antibody: Apply At3g9800 antibody at 1:100-1:200 dilution overnight at 4°C

• Secondary antibody: Use fluorescent-conjugated secondary antibodies at 1:500 dilution

• Counterstaining: DAPI for nuclei (1 μg/ml) for 10 minutes

• Mounting: Mount in anti-fade medium and seal with nail polish

For co-localization studies, combine At3g9800 antibody with antibodies against known cellular markers or potential interacting proteins. Include appropriate controls, including omission of primary antibody and use of pre-immune serum, to validate specificity of staining patterns.

How can I apply active learning strategies to improve At3g9800 antibody-antigen binding prediction?

Active learning can significantly enhance antibody-antigen binding prediction for At3g9800 research by reducing experimental costs while maximizing data quality. Recent research has shown that active learning strategies can reduce the number of required antigen mutant variants by up to 35% compared to random sampling approaches . For At3g9800 antibody research, implement the following strategy:

• Initial dataset generation: Begin with a small set of experimentally validated antibody-antigen binding pairs involving At3g9800 and related proteins

• Model training: Train a preliminary machine learning model on this initial dataset

• Uncertainty sampling: Identify antibody-antigen pairs for which the model has high uncertainty

• Batch selection: Choose diverse samples from high-uncertainty candidates to maximize information gain

• Experimental validation: Test selected samples experimentally

• Model updating: Retrain the model with newly generated data

• Iteration: Repeat steps 3-6 until desired performance is achieved

This approach can accelerate the learning process by approximately 28 steps compared to random selection baselines, as demonstrated in recent library-on-library antibody-antigen studies . Particularly for out-of-distribution prediction scenarios, where test antibodies and antigens differ from training data, these strategies show significant advantages.

What methods can resolve contradictory results when using At3g9800 antibodies from different sources?

When faced with contradictory results using At3g9800 antibodies from different sources, implement a systematic troubleshooting approach:

• Epitope mapping: Determine the specific epitopes recognized by each antibody source, as antibodies targeting different regions of At3g9800 may yield varying results due to:

  • Differential protein folding affecting epitope accessibility

  • Post-translational modifications masking epitopes

  • Protein-protein interactions blocking antibody binding sites

• Cross-validation using orthogonal techniques:

  • Confirm protein identity using mass spectrometry

  • Employ genetic approaches (knockout/knockdown) to validate specificity

  • Use epitope-tagged versions of At3g9800 with commercial anti-tag antibodies

• Comprehensive antibody validation:

  • Test each antibody on recombinant At3g9800 protein

  • Compare reactivity against wild-type and knockout samples

  • Evaluate batch-to-batch variation through standardized control samples

• Systematic experimental design:

  • Include all antibodies in parallel experiments under identical conditions

  • Document detailed experimental protocols including blocking agents, incubation times, and detection methods

  • Establish quantitative metrics for comparing antibody performance

This methodical approach can identify the source of discrepancies and establish which antibody provides the most reliable results for specific experimental applications.

How can I design experiments to investigate At3g9800 protein-protein interactions in brassinosteroid signaling pathways?

Designing experiments to investigate At3g9800 protein-protein interactions in brassinosteroid signaling requires multifaceted approaches:

• In vivo approaches:

  • Bimolecular Fluorescence Complementation (BiFC): Fuse split fluorescent protein fragments to At3g9800 and potential interacting partners

  • Co-immunoprecipitation with At3g9800 antibodies followed by mass spectrometry

  • Proximity-dependent biotin labeling (BioID or TurboID) with At3g9800 as the bait protein

• In vitro approaches:

  • Pull-down assays with recombinant At3g9800 and candidate interactors

  • Surface Plasmon Resonance (SPR) to measure binding kinetics

  • AlphaScreen assays for high-throughput interaction screening

• Genetic approaches:

  • Yeast two-hybrid screening against BR signaling component libraries

  • Genetic suppressor/enhancer screens in At3g9800 mutant backgrounds

  • CRISPR-based genetic interaction mapping

Drawing from established protocols for brassinosteroid signaling components like BZR1, implement TAP-tag (Tandem Affinity Purification) approaches similar to those used for bzr1-1D-TAPH fusion proteins . This method allows for stringent purification of protein complexes, reducing false positives. To validate interactions, conduct reciprocal co-immunoprecipitation experiments and test the effect of brassinosteroid treatment on interaction dynamics.

What are common pitfalls in At3g9800 antibody experiments and how can they be avoided?

Common pitfalls in At3g9800 antibody experiments include:

• Non-specific binding: Often results in multiple bands on Western blots or diffuse immunostaining

  • Solution: Increase blocking agent concentration (5-10% BSA or milk)

  • Include competing proteins like 0.1-0.5% gelatin in antibody dilution

  • Perform more stringent washing steps with increased detergent concentration

• Low signal-to-noise ratio:

  • Solution: Optimize antibody concentration through titration experiments

  • Increase antigen amount (for Western blots)

  • Employ signal amplification systems like tyramide signal amplification for immunohistochemistry

• Batch-to-batch antibody variation:

  • Solution: Purchase larger lots of validated antibody

  • Maintain reference samples for standardization

  • Perform comprehensive validation for each new batch

• Sample degradation:

  • Solution: Add protease inhibitor cocktails immediately during extraction

  • Maintain cold chain throughout sample processing

  • Minimize freeze-thaw cycles

• Cross-reactivity with related proteins:

  • Solution: Validate using knockout/knockdown controls

  • Pre-absorb antibody against recombinant related proteins

  • Consider generating monoclonal antibodies for increased specificity

Similar challenges have been observed when working with plant transcription factors like those in the brassinosteroid signaling pathway, where careful purification protocols were required to maintain protein integrity while ensuring specific detection .

How can I quantify At3g9800 protein levels accurately in different plant tissues?

Accurate quantification of At3g9800 protein levels requires rigorous methodology and appropriate controls:

• Sample normalization approaches:

  • Total protein normalization using Ponceau S or REVERT total protein stain

  • Housekeeping protein controls (though be aware these may vary across tissues)

  • Spiking samples with known quantities of recombinant proteins for standard curves

• Quantitative Western blotting:

  • Use fluorescent secondary antibodies for wider linear dynamic range

  • Include calibration curves with recombinant At3g9800 protein

  • Employ image analysis software for densitometry with background subtraction

  • Perform technical and biological replicates (minimum n=3)

• ELISA-based quantification:

  • Develop sandwich ELISA with capture and detection antibodies recognizing different At3g9800 epitopes

  • Include standard curves with purified recombinant protein

  • Validate assay for potential matrix effects from different tissue types

• Mass spectrometry:

  • Implement absolute quantification (AQUA) with isotope-labeled peptide standards

  • Select At3g9800-specific peptides that ionize efficiently and lack modification sites

  • Account for extraction efficiency differences between tissues

When comparing protein levels across tissues or treatments, present data with appropriate statistical analyses and measures of variability, avoiding common pitfalls such as presenting data without statistical analyses or measurements of data variability as warned against in research methodology guidelines .

How do I interpret At3g9800 localization patterns that differ from computational predictions?

When experimental At3g9800 localization patterns diverge from computational predictions, consider these interpretative approaches:

• Evaluation of prediction limitations:

  • Most computational predictions are based on primary sequence analysis and may miss context-dependent signals

  • Algorithms often fail to account for protein-protein interactions that can alter localization

  • Post-translational modifications can create or mask localization signals

• Technical considerations:

  • Fixation methods can alter protein localization (compare PFA vs. methanol fixation)

  • Antibody accessibility issues may prevent detection in certain cellular compartments

  • Overexpression systems can overwhelm normal trafficking machinery

• Biological explanations:

  • Dynamic localization: At3g9800 may shuttle between compartments based on cellular conditions

  • Developmental regulation: Localization patterns may change during plant development

  • Stimulus-dependent changes: Treatments like brassinosteroids may alter localization

• Validation strategies:

  • Use multiple antibodies targeting different epitopes

  • Employ fluorescently-tagged At3g9800 constructs expressed at near-endogenous levels

  • Perform subcellular fractionation followed by Western blotting

  • Combine with electron microscopy immunogold labeling for higher resolution

How can I develop and validate a phospho-specific antibody for At3g9800?

Developing phospho-specific antibodies for At3g9800 requires:

• Phosphorylation site identification:

  • Perform mass spectrometry analysis of purified At3g9800 under various conditions

  • Identify conserved phosphorylation sites through sequence alignment with related proteins

  • Use phosphorylation prediction algorithms to identify high-probability sites

• Peptide design considerations:

  • Design 10-15 amino acid peptides containing the phosphorylated residue in the center

  • Ensure peptide uniqueness through BLAST analysis

  • Add C-terminal cysteine for conjugation to carrier protein if not naturally present

  • Synthesize both phosphorylated and non-phosphorylated versions of the peptide

• Immunization and antibody production:

  • Immunize rabbits with phosphorylated peptide conjugated to KLH

  • Collect serum and perform initial ELISA testing against both phospho and non-phospho peptides

  • Deplete non-phospho-specific antibodies by affinity purification using non-phosphorylated peptide

  • Purify phospho-specific antibodies using phosphorylated peptide column

• Validation strategy:

  • Western blotting comparing samples treated with/without phosphatase

  • Testing on samples from plants treated with kinase inhibitors

  • Analysis of point mutants where the phosphorylation site is mutated (S/T to A)

  • Peptide competition assays with phosphorylated vs. non-phosphorylated peptides

This approach is particularly important for studying the regulation of At3g9800 function, as phosphorylation often plays crucial roles in protein activity and interactions in signaling pathways like those involving brassinosteroids .

What computational approaches can improve At3g9800 antibody epitope prediction and design?

Advanced computational approaches can significantly enhance At3g9800 antibody epitope prediction and design:

• Structure-based epitope prediction:

  • Use AlphaFold2 or RoseTTAFold to generate 3D structure predictions of At3g9800

  • Apply solvent accessibility calculations to identify surface-exposed regions

  • Utilize molecular dynamics simulations to identify stable structural elements

  • Employ docking simulations to evaluate potential antibody-epitope interactions

• Machine learning approaches:

  • Implement ensemble methods combining multiple prediction algorithms

  • Utilize deep learning models trained on experimentally verified epitopes

  • Apply active learning strategies similar to those used in antibody-antigen binding prediction studies

  • Incorporate evolutionary information through position-specific scoring matrices

• Immunogenicity prediction:

  • Assess MHC binding potential for selected epitopes

  • Calculate hydrophilicity, flexibility, and accessibility scores

  • Evaluate sequence conservation to avoid highly variable regions

  • Consider potential post-translational modifications that might affect recognition

• Validation and refinement:

  • Perform alanine scanning mutagenesis in silico to identify critical binding residues

  • Cross-reference predictions with experimentally determined epitopes for related proteins

  • Implement iterative design-test-refine cycles incorporating experimental feedback

These computational approaches can reduce the experimental burden by narrowing down potential epitopes before experimental validation, improving the success rate of antibody development for challenging targets like plant transcription factors.

How can I apply library-on-library screening approaches to characterize At3g9800 antibody specificity?

Library-on-library screening offers powerful approaches to comprehensively characterize At3g9800 antibody specificity:

• Antigen library preparation:

  • Generate a complete alanine scan library of At3g9800 protein

  • Create domain-deletion and truncation variants

  • Produce a library of related proteins with varying sequence similarity

  • Express variants as fusion proteins with detection tags for normalization

• Antibody library options:

  • Commercial antibody panels targeting At3g9800 or related proteins

  • Phage display libraries expressing single-chain variable fragments (scFvs)

  • Monoclonal antibody panels from hybridoma collections

  • Synthetic antibody libraries with engineered binding domains

• High-throughput screening platforms:

  • Protein microarrays with spotted antigen variants

  • Bead-based multiplexed assays (e.g., Luminex technology)

  • Next-generation sequencing coupled with display technologies

  • Automated ELISA systems with robotic handling

• Data analysis and interpretation:

  • Apply machine learning models to analyze binding patterns

  • Implement active learning strategies to optimize experimental design and reduce the required number of antigen variants by up to 35%

  • Generate epitope maps based on binding to variant libraries

  • Identify cross-reactivity profiles and potential off-target interactions

This comprehensive approach can accelerate characterization of antibody specificity while reducing experimental costs through intelligent experimental design, following principles similar to those demonstrated in recent out-of-distribution antibody-antigen binding prediction research .

What emerging technologies will enhance At3g9800 antibody research in the next five years?

Several emerging technologies are poised to transform At3g9800 antibody research:

• AI-driven antibody design:

  • Machine learning models trained on antibody-antigen interaction data will enable precise epitope targeting

  • Active learning approaches will reduce experimental costs by intelligently selecting the most informative experiments

  • Computational screening will predict cross-reactivity issues before antibody production

• Single-cell protein analysis:

  • High-resolution spatial transcriptomics combined with antibody detection

  • Single-cell Western blotting for heterogeneity analysis

  • Mass cytometry (CyTOF) with metal-conjugated antibodies for multiplexed detection

  • Microfluidic platforms for analyzing protein expression in individual plant cells

• Advanced microscopy techniques:

  • Super-resolution microscopy surpassing diffraction limits

  • Expansion microscopy for physically enlarged specimens

  • Light-sheet microscopy for rapid 3D imaging of plant tissues

  • Cryo-electron tomography for visualizing protein complexes in near-native states

• Synthetic biology approaches:

  • Genetically encoded sensors based on At3g9800 antibody fragments

  • Programmable binding proteins as antibody alternatives

  • CRISPR-based tagging for endogenous protein tracking

  • Cell-free protein expression systems for rapid antibody screening

These technologies will enable more precise characterization of At3g9800's role in plant development pathways, particularly in relation to brassinosteroid signaling and interactions with proteins like those identified in studies of BZR1-interacting proteins .

How can I integrate At3g9800 antibody data with other -omics approaches for systems biology studies?

Integrating At3g9800 antibody data with other -omics approaches creates a comprehensive systems biology understanding:

• Multi-omics data collection:

  • Proteomics: Antibody-based pulldowns coupled with mass spectrometry

  • Transcriptomics: RNA-seq to correlate transcript and protein levels

  • Metabolomics: Profiling metabolites affected by At3g9800 function

  • Interactomics: Mapping protein-protein interactions using antibody-based techniques

• Data integration methods:

  • Network analysis to identify regulatory hubs and pathways

  • Causal inference models to establish directional relationships

  • Multi-omics factor analysis to identify coordinated changes

  • Bayesian integrative models incorporating prior biological knowledge

• Experimental validation approaches:

  • Perturbation experiments targeting key nodes identified through integration

  • Time-course studies to capture dynamic system behavior

  • Genetic manipulation followed by antibody-based phenotyping

  • Targeted metabolic labeling combined with antibody pulldowns

• Visualization and analysis:

  • Interactive network visualization tools

  • Pathway enrichment analysis incorporating antibody-derived interaction data

  • Machine learning for pattern identification across multiple data types

  • Constraint-based modeling incorporating antibody-validated protein activities

This integrated approach provides a systems-level understanding of At3g9800's role in plant biology, similar to how researchers have studied the broader context of proteins involved in brassinosteroid signaling pathways .