At3g59210 Antibody

What is At3g59210 and why is it important in plant molecular biology research?

At3g59210 encodes an F-box/LRR-repeat protein that belongs to the F-box/RNI-like superfamily in Arabidopsis thaliana. F-box proteins are critical components of SCF (SKP1, Cullin, F-box) ubiquitin ligase complexes that regulate protein degradation through the ubiquitin-proteasome pathway. This specific F-box protein plays potential roles in plant development, stress responses, and cellular signaling networks.

The systematic study of At3g59210 requires specific antibodies for protein detection, localization, and functional characterization. The commercially available At3g59210 antibody is a rabbit polyclonal antibody designed specifically for Arabidopsis thaliana research, enabling detection of this protein in various experimental contexts .

What are the technical specifications of available At3g59210 antibodies?

The most commonly used At3g59210 antibody is a rabbit polyclonal antibody that recognizes epitopes within the F-box/LRR-repeat protein At3g59210. The antibody has the following specifications:

This antibody has been validated for specific detection of the target protein in Arabidopsis thaliana samples, making it suitable for various molecular biology applications .

How should I design Western blot experiments using At3g59210 antibody?

When designing Western blot experiments with At3g59210 antibody, consider these methodological approaches:

• Sample preparation: Extract total protein from plant tissues using a buffer containing protease inhibitors to prevent degradation of the target protein. For membrane-associated F-box proteins, consider using specialized extraction buffers containing mild detergents.

• Gel electrophoresis parameters: Use 10-12% SDS-PAGE gels for optimal separation of F-box proteins (~45-60 kDa range). Load 20-50 μg of total protein per lane, depending on expression levels.

• Transfer conditions: Optimize transfer time and voltage based on protein size; typically 1 hour at 100V or overnight at 30V at 4°C for efficient transfer of F-box proteins.

• Blocking and antibody dilution: Use 5% non-fat dry milk or BSA in TBST for blocking. Start with a 1:1000 dilution of At3g59210 antibody for initial optimization, then adjust based on signal intensity.

• Detection methods: Both chemiluminescence and fluorescence-based detection systems are suitable; chemiluminescence often provides better sensitivity for low-abundance F-box proteins.

• Essential controls:

  • Positive control (tissue with known expression of At3g59210)

  • Negative control (tissue from knockout/knockdown lines)

  • Loading control (anti-actin or anti-tubulin antibody)

For quantitative analysis, include a dilution series of samples to ensure measurements are within the linear range of detection .

What are the critical optimization parameters for ELISA using At3g59210 antibody?

For ELISA optimization with At3g59210 antibody, focus on these key parameters:

• Antigen coating concentration: Titrate recombinant protein or plant extract (0.1-10 μg/mL) to determine optimal coating concentration.

• Antibody titration: Test serial dilutions (1:500 to 1:10,000) of At3g59210 antibody to determine the optimal concentration that provides specific signal with minimal background.

• Buffer optimization:

  • Coating buffer: Compare carbonate buffer (pH 9.6) vs. phosphate buffer (pH 7.4)

  • Blocking buffer: Test BSA vs. non-fat milk (3-5%) in PBS or TBS

  • Washing buffer: PBS-T or TBS-T with 0.05-0.1% Tween-20

• Incubation conditions:

  • Temperature: Compare room temperature vs. 37°C

  • Duration: Optimize antigen coating (overnight at 4°C) and antibody incubation times (1-2 hours)

• Detection system: HRP-conjugated secondary antibody with TMB or ABTS substrate for colorimetric detection, or AP-conjugated secondary with pNPP for enhanced sensitivity.

Validation should include spike-and-recovery experiments with recombinant At3g59210 protein to assess assay accuracy and linearity .

How can I use At3g59210 antibody for immunoprecipitation studies?

Immunoprecipitation with At3g59210 antibody requires careful optimization. Follow this methodological approach:

• Lysate preparation: Extract proteins under native conditions using a buffer containing:

  • 50 mM Tris-HCl (pH 7.5)

  • 150 mM NaCl

  • 1% NP-40 or 0.5% Triton X-100

  • 1 mM EDTA

  • Protease inhibitor cocktail

  • Phosphatase inhibitors (if studying phosphorylation)

• 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 At3g59210 antibody to 500-1000 μg of pre-cleared lysate and incubate overnight at 4°C with gentle rotation.

• Immunoprecipitation: Add 30-50 μL of protein A/G beads and incubate for 2-4 hours at 4°C with gentle rotation.

• Washing: Perform 4-5 washes with decreasing salt concentrations to reduce background while maintaining specific interactions.

• Elution and analysis: Elute bound proteins using either:

  • Denaturing conditions (SDS sample buffer at 95°C for 5 minutes)

  • Native conditions (excess antigen peptide competition)

• Validation controls:

  • Input sample (10% of starting material)

  • No-antibody control (beads only)

  • Irrelevant antibody control (same isotype)

This approach allows identification of protein binding partners and post-translational modifications of At3g59210 .

What strategies can be employed to study At3g59210 protein interactions with other ubiquitin pathway components?

To investigate interactions between At3g59210 and other ubiquitin pathway components:

• Co-immunoprecipitation (Co-IP):

  • Use At3g59210 antibody to pull down the protein complex

  • Probe with antibodies against potential interactors (ASK1/SKP1, CUL1, RBX1)

  • Alternatively, immunoprecipitate with antibodies against known SCF components and probe with At3g59210 antibody

• Yeast two-hybrid screening:

  • Use At3g59210 as bait to screen for novel interactors

  • Validate interactions using Co-IP with the antibody

• Bimolecular Fluorescence Complementation (BiFC):

  • Express At3g59210 fused to one half of a fluorescent protein

  • Express potential interactors fused to the complementary half

  • Reconstitution of fluorescence indicates interaction

  • Validate with At3g59210 antibody via Western blot

• Mass spectrometry analysis:

  • Immunoprecipitate with At3g59210 antibody

  • Perform LC-MS/MS analysis of co-precipitating proteins

  • Quantify enrichment compared to controls

  • Confirm novel interactions with reciprocal Co-IP

• In vitro protein binding assays:

  • Express recombinant At3g59210 and potential interactors

  • Perform pull-down assays

  • Detect with At3g59210 antibody

This integrated approach can reveal the composition and dynamics of SCF complexes containing At3g59210 .

How can I address non-specific binding issues when using At3g59210 antibody?

Non-specific binding is a common challenge with polyclonal antibodies. Address this methodically:

• Optimize blocking conditions:

  • Test different blocking agents (BSA, non-fat milk, casein, commercial blocking buffers)

  • Increase blocking time (1-2 hours at room temperature or overnight at 4°C)

  • Test higher blocking agent concentrations (3-5%)

• Antibody dilution optimization:

  • Test higher dilutions (1:2000 to 1:5000) to reduce non-specific binding

  • Prepare antibody in fresh blocking buffer

  • Pre-absorb antibody with proteins from negative control samples

• Washing optimization:

  • Increase wash duration and number of washes

  • Add low concentrations of detergent (0.1-0.5% Tween-20)

  • Use higher salt concentration in wash buffer (up to 500 mM NaCl)

• Validation with genetic controls:

  • Compare wild-type and At3g59210 knockout/knockdown samples

  • Confirm specificity with antigen competition assay

  • Use secondary antibody-only control to identify secondary antibody non-specific binding

• Cross-adsorption technique:

  • Incubate antibody with plant extracts from knockout lines

  • Remove complexes by centrifugation

  • Use the supernatant containing antibodies depleted of cross-reactive components

These approaches systematically reduce non-specific binding while preserving specific signal .

What are the best practices for quantification and statistical analysis of At3g59210 expression data?

For robust quantification and statistical analysis of At3g59210 expression:

• Western blot quantification:

  • Use digital image acquisition with a wide dynamic range

  • Ensure exposure times produce signal within the linear range

  • Normalize to consistent loading controls (GAPDH, actin, tubulin)

  • Use densitometry software with background subtraction

  • Include calibration curves with recombinant standards when possible

• ELISA data analysis:

  • Generate standard curves using 4- or 5-parameter logistic regression

  • Perform technical triplicates and biological replicates

  • Calculate coefficient of variation (CV) for technical replicates (<15% acceptable)

  • Analyze sample dilution linearity to confirm quantification validity

• Statistical considerations:

  • Perform normality tests before choosing parametric/non-parametric tests

  • Use appropriate statistical tests based on experimental design:

    • Two groups: t-test or Mann-Whitney U test

    • Multiple groups: ANOVA or Kruskal-Wallis with appropriate post-hoc tests

  • Calculate effect sizes (Cohen's d or similar) to assess biological significance

  • Report p-values with appropriate corrections for multiple comparisons

• Visualization best practices:

  • Present data as box plots or violin plots rather than bar graphs

  • Include individual data points

  • Use consistent scales across comparable experiments

  • Clearly indicate sample sizes and statistical tests used

Following these guidelines ensures reproducible and statistically sound analysis of At3g59210 expression data .

How can machine learning approaches enhance antibody-based detection of At3g59210?

Machine learning (ML) approaches can significantly improve At3g59210 antibody-based detection:

• Automated Western blot analysis:

  • Convolutional neural networks (CNNs) can identify and quantify bands with greater precision

  • ML algorithms can automatically normalize data across multiple blots

  • Support vector machines can differentiate specific from non-specific signals

• Epitope prediction and antibody design:

  • Deep learning models can predict optimal epitopes for generating improved At3g59210 antibodies

  • ML approaches can identify regions of At3g59210 with high antigenicity and low sequence conservation with related proteins

  • These predictions can guide design of more specific monoclonal antibodies

• Image analysis for immunofluorescence:

  • ML algorithms can perform automated subcellular localization analysis

  • Neural networks can segment cells and quantify signal intensities

  • Reduces bias in image interpretation and increases throughput

• Multi-parameter data integration:

  • ML can integrate At3g59210 antibody-based data with transcriptomics and proteomics datasets

  • Identifies patterns and correlations not apparent in single-method approaches

  • Provides systems-level insights into F-box protein function

Recent developments in protein-specific language models demonstrate 90% accuracy in predicting optimal antibody designs for specific targets, suggesting potential for generating improved At3g59210-specific antibodies .

What novel methodologies are emerging for studying protein-protein interactions involving At3g59210?

Several cutting-edge methodologies are enhancing the study of At3g59210 protein interactions:

• Proximity labeling approaches:

  • BioID: Express At3g59210 fused to a biotin ligase (BirA*)

  • APEX2: Express At3g59210 fused to an engineered peroxidase

  • TurboID: Use enhanced biotin ligase for faster labeling

  • These approaches biotinylate proteins in close proximity, which can be purified and identified by mass spectrometry

  • Validation of interactions requires At3g59210 antibody for confirmation

• Single-molecule techniques:

  • Single-molecule pull-down (SiMPull) combines antibody-based pull-down with single-molecule fluorescence

  • Fluorescence correlation spectroscopy (FCS) to study binding kinetics

  • These approaches provide information on binding stoichiometry and dynamics

• Cryo-electron microscopy:

  • Structural analysis of At3g59210-containing protein complexes

  • Immunogold labeling with At3g59210 antibody for precise localization

  • Reveals molecular architecture of SCF complexes

• CRISPR-based approaches:

  • CRISPR activation/inhibition to modulate At3g59210 expression

  • Endogenous tagging of At3g59210 for pull-down without antibodies

  • Validation of tagged constructs using At3g59210 antibody

• Cross-linking mass spectrometry (XL-MS):

  • Chemical cross-linking captures transient interactions

  • MS analysis identifies cross-linked peptides

  • Creates interaction maps at amino acid resolution

  • At3g59210 antibody used for enrichment prior to cross-linking

These technologies are revolutionizing our understanding of F-box protein interactions and function in plant molecular networks .

What are the current limitations of At3g59210 antibody research and how might these be addressed?

Current limitations in At3g59210 antibody research include:

• Specificity challenges: Polyclonal antibodies may cross-react with related F-box proteins. Future development of monoclonal antibodies or recombinant antibody fragments using phage display could enhance specificity.

• Detection sensitivity: Low endogenous expression levels of At3g59210 can challenge detection. Emerging signal amplification methods like tyramide signal amplification (TSA) or proximity ligation assay (PLA) could address this limitation.

• Structural insights: Current antibodies primarily serve detection purposes but provide limited structural information. Developing conformation-specific antibodies could reveal regulatory mechanisms of At3g59210.

• Cell-type specificity: Current methods often use whole-tissue extracts. Single-cell proteomics approaches combined with antibody-based detection could reveal cell-type-specific expression patterns and functions.

• Temporal dynamics: Standard antibody techniques provide snapshots rather than dynamic information. Developing optogenetic tools verified with antibody-based methods could capture temporal regulation of At3g59210.

How can integrative multi-omics approaches enhance our understanding of At3g59210 function?

Integrative multi-omics approaches can provide comprehensive insights into At3g59210 function:

• Proteomics-transcriptomics integration:

  • Correlate At3g59210 protein levels (detected by antibody) with transcript levels

  • Identify post-transcriptional regulatory mechanisms

  • Develop predictive models of F-box protein expression regulation

• Interactome-phenome mapping:

  • Connect At3g59210 protein interaction networks with phenotypic outcomes

  • Use antibody-based methods to validate key interactions in different physiological contexts

  • Create comprehensive maps of F-box protein regulatory networks

• Spatiotemporal profiling:

  • Combine antibody-based imaging with transcriptomics in defined cell populations

  • Create four-dimensional models of At3g59210 activity during development and stress responses

  • Use this information to predict regulatory outputs in different contexts

• Systems biology modeling:

  • Integrate antibody-derived quantitative data into mathematical models

  • Predict system-level responses to perturbations

  • Test predictions experimentally using CRISPR-based approaches and antibody validation

• Comparative biology approaches:

  • Use At3g59210 antibody in cross-species studies (if epitope is conserved)

  • Identify conserved and divergent functions across plant species

  • Reveal evolutionary adaptations in F-box protein regulatory networks