At1g47056 Antibody

What is AT1G47056 and why would researchers need an antibody against it?

AT1G47056 is a gene in Arabidopsis thaliana that has been studied in the context of plant molecular biology. Researchers develop antibodies against the protein products of such genes to detect, quantify, and characterize the expression patterns and functions of these proteins in various experimental settings. Based on available research data, AT1G47056 has been recombined into expression vectors such as the pK7-HFN destination vector, which suggests its protein product may play roles in plant development or stress responses . Antibodies against AT1G47056 would enable researchers to:

• Detect protein expression in different plant tissues

• Track subcellular localization of the protein

• Study protein-protein interactions

• Examine post-translational modifications

• Validate gene knockout or overexpression lines

The protein encoded by AT1G47056 may be involved in signaling pathways related to pathogen-associated molecular pattern (PAMP) perception, given its context in research related to plant immunity .

How can I validate the specificity of an AT1G47056 antibody?

Validating antibody specificity is crucial for reliable experimental results. For AT1G47056 antibody validation, follow this methodological approach:

• Western blot analysis with positive and negative controls:

  • Use protein extracts from wild-type plants (positive control)

  • Use protein extracts from AT1G47056 knockout mutants (negative control)

  • Look for a single band of the expected molecular weight in wild-type samples and absence of this band in knockout samples

• Immunoprecipitation followed by mass spectrometry:

  • Perform IP with the AT1G47056 antibody

  • Analyze precipitated proteins by mass spectrometry

  • Confirm AT1G47056 protein as the predominant identified protein

• Immunohistochemistry with controls:

  • Compare staining patterns between wild-type and knockout tissues

  • Perform peptide competition assays to demonstrate specificity

Similar validation methods have been shown effective for other antibodies as demonstrated in recombinant antibody research, where specificity was confirmed through multiple approaches including Western blot analysis and immunohistochemistry .

What expression systems are recommended for producing an AT1G47056 antibody?

Based on research methodologies for similar plant protein antibodies, the following expression systems are recommended:

For AT1G47056, based on the available information about its expression in plants with the pK7-HFN destination vector, a recombinant approach similar to those used for other research antibodies would be appropriate . E. coli DH5-alpha has been used for propagating the gene construct, suggesting this bacterial system might be suitable for initial expression studies .

What are the recommended protocols for using AT1G47056 antibody in immunoprecipitation experiments?

For optimal results in immunoprecipitation (IP) experiments with AT1G47056 antibody, follow this methodological protocol:

• Plant tissue preparation:

  • Harvest fresh tissue (preferably young leaves or seedlings)

  • Grind in liquid nitrogen to a fine powder

  • Extract proteins in a buffer containing:

    • 50 mM Tris-HCl pH 7.5

    • 150 mM NaCl

    • 1% Triton X-100

    • 0.5% sodium deoxycholate

    • Protease inhibitor cocktail

  • Centrifuge at 14,000 × g for 15 minutes at 4°C

  • Collect supernatant

• Immunoprecipitation procedure:

  • Pre-clear lysate with Protein A/G beads for 1 hour at 4°C

  • Incubate pre-cleared lysate with AT1G47056 antibody (2-5 μg per mg of protein) overnight at 4°C

  • Add Protein A/G beads and incubate for 2-3 hours at 4°C

  • Wash beads 4-5 times with wash buffer

  • Elute proteins with SDS-PAGE loading buffer

• Controls to include:

  • IgG isotype control

  • Input sample (pre-IP lysate)

  • Unbound fraction

This protocol is based on general immunoprecipitation methods that have proven successful with plant proteins and similar antibodies in immunoprecipitation applications .

How can I optimize antibody-based detection of AT1G47056 in the context of pathogen-associated molecular pattern responses?

For researchers investigating AT1G47056 in relation to plant immune responses, optimizing antibody-based detection requires specialized approaches:

• Timing considerations:

  • Plant immune responses are highly dynamic

  • Create a time-course experiment sampling at 0, 15, 30, 60, 120, and 240 minutes post-elicitor treatment

  • Cold treatment (4°C for 48 hours) prior to experiments may affect expression patterns, as noted in experimental designs for similar studies

• Tissue preparation protocol:

  • Ensure rapid tissue fixation to preserve protein modifications

  • Cross-linking with 1% formaldehyde for 10 minutes can help preserve protein-protein interactions

  • For subcellular fractionation, use a sucrose gradient approach to isolate membrane-associated proteins

• Signal amplification methods:

  • For low-abundance proteins, consider tyramide signal amplification (TSA)

  • Use of HRP-conjugated secondary antibodies with enhanced chemiluminescence detection

  • Quantify signals using digital imaging systems with appropriate controls

• Multiplexing with other immune markers:

  • Co-stain with markers for known components of the PAMP response pathway

  • Use fluorescent secondary antibodies with distinct emission spectra

  • Perform confocal microscopy to analyze co-localization patterns

Given the potential role of AT1G47056 in pathogen response pathways, contextualizing its detection within the framework of pattern-triggered immunity would provide valuable insights into its function .

What are the considerations for using machine learning approaches to predict AT1G47056 antibody-antigen binding characteristics?

Leveraging machine learning for antibody-antigen binding prediction requires specialized approaches:

• Data collection and preprocessing:

  • Compile a dataset of known antibody-antigen interactions including structural and sequence data

  • Encode protein sequences using appropriate representation methods (one-hot encoding, BLOSUM matrices, or embedding techniques)

  • Address class imbalance issues common in binding/non-binding datasets

• Feature selection for AT1G47056 specific modeling:

  • Focus on physicochemical properties of amino acid residues

  • Include secondary structure predictions

  • Incorporate solvent accessibility features

  • Consider evolutionary conservation scores

• Model selection and training:

  • Random forests or gradient boosting for interpretable models

  • Deep learning approaches (CNNs or RNNs) for complex sequence data

  • Active learning strategies to prioritize most informative experiments

Research has shown that active learning approaches can reduce the number of required experimental measurements by up to 35% compared to random sampling strategies, making them particularly valuable for antibody-antigen binding prediction . The appropriate learning algorithm selection depends on:

The implementation of out-of-distribution prediction techniques is crucial as most test antibodies and antigens will not be represented in training data .

How can I design epitope-specific AT1G47056 antibodies for distinguishing between closely related protein isoforms?

Designing epitope-specific antibodies requires careful analysis and planning:

• Sequence analysis approach:

  • Perform multiple sequence alignment of AT1G47056 with related proteins

  • Identify regions of high sequence divergence

  • Focus on surface-exposed regions (predicted by structural modeling)

  • Avoid regions with post-translational modifications that may block antibody access

• Epitope selection criteria:

  • Length: 10-20 amino acids

  • Secondary structure: preferably in loop regions

  • Hydrophilicity: moderate to high

  • Sequence uniqueness: BLAST against proteome to ensure specificity

• Validation methodologies:

  • Peptide array analysis to confirm epitope recognition

  • Competitive ELISA with related protein isoforms

  • Western blot analysis with recombinant isoforms

  • Immunohistochemistry in tissues expressing different isoforms

For structural confirmation, X-ray crystallography has been valuable in revealing binding mechanisms, as demonstrated with other antibodies where novel allosteric mechanisms of inhibition were discovered .

What methods are recommended for quantifying AT1G47056 protein expression in different Arabidopsis tissues and under various stress conditions?

For precise quantification of AT1G47056 protein across diverse experimental conditions:

• Sample preparation protocols:

  • Standardize tissue collection (time of day, plant age, growth conditions)

  • Employ rapid freezing in liquid nitrogen to prevent protein degradation

  • Use a consistent protein extraction buffer containing phosphatase and protease inhibitors

  • Determine protein concentration using Bradford or BCA assays

• Quantitative Western blot methodology:

  • Include serial dilutions of recombinant AT1G47056 protein as a standard curve

  • Use fluorescent secondary antibodies for wider linear range

  • Employ internal loading controls (anti-actin or anti-GAPDH)

  • Analyze using digital imaging systems with appropriate software

• ELISA-based quantification:

  • Develop a sandwich ELISA using:

    • Capture antibody: anti-AT1G47056

    • Detection: biotinylated anti-AT1G47056 or antibody against different epitope

    • Visualization: streptavidin-HRP with TMB substrate

  • Include standard curves with recombinant protein

• Mass spectrometry-based approaches:

  • Selected reaction monitoring (SRM) for absolute quantification

  • Include isotopically labeled peptide standards

  • Target at least 3 peptides unique to AT1G47056

  • Process samples using filter-aided sample preparation (FASP)

Expression data should be analyzed in the context of experimental treatments, such as pathogen-associated molecular pattern exposure, to correlate protein levels with functional responses .

How can I address non-specific binding issues when using AT1G47056 antibody in plant tissue with high phenolic compound content?

Non-specific binding in plant tissues with high phenolic content requires specialized troubleshooting approaches:

• Buffer optimization strategy:

  • Add polyvinylpyrrolidone (PVP) at 2-4% to bind phenolic compounds

  • Include 2-mercaptoethanol (0.2-0.5%) to reduce oxidation

  • Add higher concentrations of detergents (0.1-0.3% Tween-20) to reduce hydrophobic interactions

  • Test different pH conditions (6.0-8.0) to identify optimal specificity

• Blocking protocol refinement:

  • Extended blocking times (2-4 hours at room temperature)

  • Use of specialized blocking agents:

    • 5% milk with 1% BSA

    • Plant-specific blocking reagents containing non-plant proteins

    • Commercial plant-optimized blocking solutions

• Antibody dilution and incubation modifications:

  • Higher antibody dilutions (1:1000 to 1:5000)

  • Longer incubation at lower temperatures (overnight at 4°C)

  • Addition of 0.1-0.2 M NaCl to reduce ionic interactions

• Pre-absorption strategy:

  • Pre-incubate antibody with proteins from knockout/null mutant plants

  • Use this pre-absorbed antibody for subsequent detection

These approaches have proven effective in addressing similar challenges with other plant protein antibodies and would be applicable to optimizing AT1G47056 antibody specificity .

What are the best approaches for using AT1G47056 antibody in co-immunoprecipitation to identify interaction partners?

For successful co-immunoprecipitation (co-IP) experiments with AT1G47056 antibody:

• Crosslinking considerations:

  • Reversible crosslinkers: DSP (dithiobis(succinimidyl propionate)) at 1-2 mM

  • Light-activatable crosslinkers for controlled interaction capture

  • Formaldehyde (0.5-1%) for protein-DNA interactions if relevant

• Lysis buffer optimization:

  • Test multiple detergent conditions:

• Antibody immobilization strategies:

  • Direct coupling to beads (NHS-activated sepharose)

  • Protein A/G beads with antibody crosslinking

  • Magnetic beads for gentler purification

• Analysis of interacting partners:

  • LC-MS/MS analysis with label-free quantification

  • Comparison to control IPs (IgG, unrelated antibody)

  • Statistical analysis to identify significant interactions

  • Validation by reverse co-IP and/or BiFC

This methodological approach has been successfully applied to other plant proteins and would be suitable for investigating AT1G47056 interaction networks, particularly in the context of immune response pathways .

How can I develop a multiplexed immunoassay that includes detection of AT1G47056 alongside other proteins in the same pathway?

Developing a multiplexed immunoassay requires careful consideration of antibody compatibility and detection systems:

• Antibody selection criteria:

  • Choose antibodies raised in different host species (rabbit, mouse, goat, etc.)

  • Verify lack of cross-reactivity between antibodies

  • Ensure similar optimal working conditions (buffer, pH, salt)

  • Test for interference between detection systems

• Platform selection based on research needs:

• Signal separation methods:

  • Fluorescent labels with distinct excitation/emission profiles

  • Sequential detection with stripping and reprobing

  • Size-based separation on Western blots

  • Spectrally distinct quantum dots for improved separation

• Data analysis approach:

  • Normalization to internal standards

  • Correction for background and signal spillover

  • Pathway analysis software to interpret protein relationships

  • Statistical methods for co-expression analysis

This multiplexed approach would be particularly valuable for studying AT1G47056 in the context of receptor-like kinase signaling and pathogen-associated molecular pattern responses in Arabidopsis .

How can AT1G47056 antibody be used to investigate protein localization changes during plant immune responses?

For studying dynamic changes in AT1G47056 localization during immune responses:

• Subcellular fractionation approach:

  • Isolate distinct cellular compartments:

    • Plasma membrane (two-phase partitioning method)

    • Endoplasmic reticulum (sucrose gradient)

    • Nuclear fraction (gentle lysis and differential centrifugation)

    • Cytoplasmic fraction

  • Analyze protein distribution across fractions before and after pathogen treatment

  • Verify fraction purity with compartment-specific markers

• Immunofluorescence microscopy protocol:

  • Fix tissue samples at different timepoints post-elicitation

  • Perform antigen retrieval if necessary

  • Block with appropriate reagents

  • Incubate with primary AT1G47056 antibody, followed by fluorescent secondary antibody

  • Co-stain with organelle markers

  • Analyze using confocal microscopy

• Live cell imaging strategies:

  • Generate plants expressing fluorescently-tagged AT1G47056

  • Validate tag functionality using the AT1G47056 antibody

  • Perform time-lapse imaging during immune elicitation

  • Quantify protein movement and redistribution

• Super-resolution microscopy applications:

  • Implement STED or STORM microscopy for nanoscale resolution

  • Map precise protein movements at the plasma membrane-cytoplasm interface

  • Quantify clustering dynamics in response to elicitors

These approaches would provide valuable insights into the role of AT1G47056 in plant immune responses, particularly in the context of pattern-triggered immunity research .

What considerations should be taken into account when using active learning approaches to optimize AT1G47056 antibody development?

Implementing active learning strategies for AT1G47056 antibody development requires specialized methodological approaches:

• Initial training dataset establishment:

  • Begin with a small labeled subset of epitopes with known binding characteristics

  • Include diverse epitope conformations and conditions

  • Incorporate negative examples (non-binding epitopes)

  • Balance representation of different binding affinities

• Algorithm selection and implementation:

  • Uncertainty-based sampling methods (e.g., entropy maximization)

  • Committee-based approaches using multiple models

  • Expected model change methods

  • Density-weighted methods to ensure diverse sampling

• Experimental validation cycle:

  • Select epitopes for testing based on algorithm recommendations

  • Experimentally validate binding properties

  • Incorporate new data into model training

  • Iterate until convergence criteria are met

Research has demonstrated that active learning approaches can reduce the number of required experiments by up to 35% compared to random selection strategies, significantly accelerating the development process . The optimal approach depends on specific project constraints:

By implementing these strategies, researchers can overcome the challenges of out-of-distribution prediction that commonly arise in antibody development projects .

How can I integrate AT1G47056 antibody-based detection with transcriptomic and metabolomic data for systems biology approaches?

For multi-omics integration involving AT1G47056 antibody-based proteomics:

• Coordinated sampling strategy:

  • Design experiments with matched samples for all omics analyses

  • Include appropriate time points (e.g., 0, 1, 3, 6, 12, 24 hours post-treatment)

  • Maintain consistent environmental conditions and plant developmental stages

  • Implement biological and technical replicates consistently across platforms

• Data normalization and integration approaches:

  • Scale and normalize data from each platform independently

  • Apply batch correction methods as needed

  • Use dimensionality reduction techniques (PCA, t-SNE) for initial exploration

  • Implement specialized multi-omics integration algorithms:

    • MOFA (Multi-Omics Factor Analysis)

    • DIABLO (Data Integration Analysis for Biomarker discovery)

    • WGCNA (Weighted Gene Co-expression Network Analysis)

• Correlation and causality analysis:

  • Identify correlation patterns between AT1G47056 protein levels and transcript expression

  • Map protein abundance to metabolite changes in relevant pathways

  • Apply Bayesian network analysis to infer causality

  • Validate key relationships with targeted experiments

• Visualization and interpretation tools:

  • Pathway enrichment analysis incorporating all data types

  • Interactive network visualization (Cytoscape with multi-omics plugins)

  • Temporal trajectory mapping to capture dynamic responses

  • Machine learning for pattern recognition across datasets

This integrated approach would provide comprehensive insights into the role of AT1G47056 in plant immune responses by contextualizing protein-level changes within broader molecular networks .

What emerging technologies are likely to enhance AT1G47056 antibody applications in plant research?

Several cutting-edge technologies are poised to revolutionize AT1G47056 antibody applications:

• Single-cell protein analysis:

  • Adaptation of CyTOF (mass cytometry) for plant cell analysis

  • Development of single-cell Western blot technologies

  • Spatial proteomics approaches to map protein localization in intact tissues

  • Integration with single-cell transcriptomics for comprehensive analysis

• Nanobody and synthetic binding protein alternatives:

  • Development of camelid nanobodies against AT1G47056

  • Designed ankyrin repeat proteins (DARPins) for improved specificity

  • Aptamer-based detection systems for live-cell applications

  • Molecularly imprinted polymers as synthetic antibody alternatives

• Advanced computational modeling:

  • AI-driven epitope prediction for improved antibody design

  • Molecular dynamics simulations to predict antibody-antigen interactions

  • Integration of structural biology data for rational antibody engineering

  • Computational docking to optimize binding affinity and specificity

• In planta antibody expression systems:

  • Plants engineered to express nanobodies against target proteins

  • Inducible expression systems for temporal control

  • Cell-type specific expression for targeted analysis

  • Fusion with fluorescent proteins for live visualization