At3g63360 Antibody

What is the At3g63230 protein in Arabidopsis thaliana and what antibodies are available for its detection?

At3g63230 is a senescence-associated protein in Arabidopsis thaliana consisting of 124 amino acids. It is characterized as an uncharacterized protein that plays a role in senescence-related processes. Currently available antibodies include combinations of mouse monoclonal antibodies targeting different regions of the protein (N-terminus, C-terminus, and middle/non-terminus sequences). These antibody combinations are designed for direct use in applications such as Western blotting .

What are the typical applications for Arabidopsis antibodies in plant research?

Arabidopsis antibodies are widely used in various molecular biology techniques including:

• Western blotting for protein detection and quantification

• Enzyme-linked immunosorbent assay (ELISA) for measuring protein concentrations

• Immunohistochemistry (IHC) for visualizing protein localization within tissues

• Immunoprecipitation for isolating protein complexes

• Chromatin immunoprecipitation (ChIP) for studying protein-DNA interactions

The antibody selection should be based on the specific application and experimental goals. Most commercially available antibodies, like those for At3g63230, are tested for specific applications with ELISA titers reaching approximately 10,000, corresponding to detection sensitivity of approximately 1 ng of target protein on Western blots .

How do I optimize antibody concentrations for Western blot detection of plant proteins?

When optimizing antibody concentrations for Western blot detection of plant proteins like At3g63230, follow these methodological steps:

• Begin with the manufacturer's recommended dilution (typically 1 μg/mL for primary antibodies as seen with similar antibodies)

• Perform a dilution series (e.g., 0.5, 1, 2, 5 μg/mL) to determine optimal concentration

• Include appropriate controls (positive control with known expression, negative control)

• Optimize blocking conditions to reduce background

• Consider different detection systems (chemiluminescence, fluorescence)

• For plant proteins specifically, add protease inhibitors during extraction to prevent degradation

• Validate antibody specificity using knockout lines or competing peptides when available

Optimal antibody dilutions should produce clear specific bands with minimal background, similar to the specific detection of proteins at expected molecular weights as demonstrated with other antibodies in similar applications .

How do I select the most appropriate antibody combination for detecting low-abundance plant proteins?

When working with low-abundance plant proteins such as senescence-associated proteins like At3g63230, consider these methodological approaches:

• Epitope coverage analysis: Select antibody combinations targeting multiple regions (N-terminus, C-terminus, and middle region) to increase detection probability and signal amplification. Companies offering antibodies against At3g63230 provide combinations specifically designed for this purpose .

• Pre-enrichment strategies: Consider subcellular fractionation or immunoprecipitation to concentrate your protein of interest before detection.

• Signal enhancement techniques: Utilize signal amplification systems such as tyramide signal amplification (TSA) or biotin-streptavidin systems.

• Sensitivity comparison: Compare the ELISA titers of available antibodies - higher titers (e.g., 10,000 for At3g63230 antibodies) generally correlate with better detection limits .

• Sample preparation optimization: Modify extraction buffers to maximize protein solubilization while preserving epitope recognition.

• Detection system selection: Choose highly sensitive detection systems (e.g., enhanced chemiluminescence) for visualizing low-abundance proteins.

For protein tracking across developmental stages or stress responses, combinations of antibodies targeting different regions provide redundancy that improves detection reliability .

What controls should I include when using antibodies against uncharacterized plant proteins?

When working with antibodies against uncharacterized plant proteins like At3g63230, implement these essential controls:

• Negative controls:

  • Isotype control antibodies (same isotype as primary antibody but non-specific)

  • Samples from knockout/knockdown plants lacking the target protein

  • Primary antibody omission to assess secondary antibody specificity

  • Pre-absorption with immunizing peptide to confirm specificity

• Positive controls:

  • Recombinant protein expression systems

  • Tissues/cells known to express the target protein

  • Overexpression lines if available

• Cross-reactivity assessment:

  • Test against closely related proteins (e.g., testing AG-3 antibodies against AG-2 proteins as seen in search result )

  • Include multiple tissue types to confirm expression patterns

• Technical validation:

  • Multiple antibodies targeting different epitopes of the same protein

  • Alternative detection methods (mass spectrometry, RNA expression)

  • Gradient dilution series to confirm signal linearity

These controls are particularly important for "hard" target proteins (as classified by AbClass™ for At3g63230) where antibody generation and validation are more challenging.

How can I assess antibody specificity in plant tissue sections for immunohistochemistry?

To rigorously assess antibody specificity for immunohistochemistry in plant tissues, follow these methodological steps:

• Tissue preparation optimization:

  • Test multiple fixation protocols (paraformaldehyde, glutaraldehyde)

  • Compare different antigen retrieval methods (heat-induced epitope retrieval, enzymatic retrieval)

  • Optimize section thickness (5-10 μm typically works well)

• Validation controls:

  • Include knockout/knockdown plant tissues as negative controls

  • Use competitive blocking with immunizing peptides

  • Compare staining patterns with in situ hybridization or reporter gene expression

  • Include tissues with known expression patterns as positive controls

• Signal verification:

  • Test antibody dilution series to identify optimal concentration

  • Compare multiple antibodies against different epitopes of the same protein

  • Confirm subcellular localization patterns align with protein function

  • Use fluorescent secondary antibodies for co-localization studies with organelle markers

• Technical approaches:

  • Use tyramide signal amplification for low-abundance proteins

  • Implement CLARITY or similar tissue clearing techniques for whole-mount imaging

  • Consider automated staining platforms for consistency across experiments

This approach is similar to validated immunohistochemistry protocols used for other antibodies, such as those described for human tissues in search result .

How can machine learning approaches improve antibody-antigen binding prediction for plant antibodies?

Advanced machine learning strategies can significantly enhance antibody-antigen binding prediction for plant proteins like At3g63230:

• Active learning implementation: Recent research has developed fourteen novel active learning strategies specifically for antibody-antigen binding prediction in library-on-library settings. These approaches can reduce the number of required antigen mutant variants by up to 35% and accelerate the learning process by 28 steps compared to random baseline methods .

• Out-of-distribution prediction enhancement: Machine learning models face challenges when predicting interactions between antibodies and antigens not represented in training data. Novel algorithms have demonstrated significant improvement in out-of-distribution performance using the Absolut! simulation framework .

• Feature engineering optimization:

  • Incorporate protein structural information (secondary/tertiary structures)

  • Include physicochemical properties of amino acids

  • Integrate evolutionary conservation data

  • Account for post-translational modifications

• Model selection strategies:

  • Compare ensemble methods with deep learning approaches

  • Implement transfer learning from human/mouse antibody datasets

  • Utilize graph neural networks to capture structural relationships

  • Apply attention mechanisms to identify critical binding regions

• Validation and benchmarking:

  • Implement cross-validation across different plant species

  • Benchmark against experimental binding data

  • Validate predictions with surface plasmon resonance or bio-layer interferometry

These computational approaches can significantly reduce experimental costs while improving antibody design and selection processes for challenging plant targets .

What are the best approaches for resolving discrepancies in antibody-based detection of stress-responsive plant proteins?

When encountering conflicting results in antibody-based detection of stress-responsive plant proteins, implement these advanced troubleshooting methodologies:

• Stress condition standardization:

  • Carefully control timing, intensity, and duration of stress treatments

  • Document plant developmental stage and growth conditions precisely

  • Consider circadian regulation of protein expression

  • Record comprehensive metadata for experimental conditions

• Protein isoform discrimination:

  • Design epitope-specific antibodies targeting unique regions of highly similar proteins

  • Implement high-resolution techniques like 2D gel electrophoresis before Western blotting

  • Use mass spectrometry to confirm protein identity in immunoprecipitated samples

  • Consider potential post-translational modifications induced by stress conditions

• Data visualization enhancements:

  • Implement appropriate visual aids for data presentation (zebra striping for complex comparisons, color or bar encoding for maximum value identification)

  • Standardize quantification methods for western blot and immunohistochemistry data

  • Use statistical approaches appropriate for non-normally distributed data often encountered in stress studies

• Multi-method validation:

  • Correlate protein levels with transcript abundance (RT-qPCR or RNA-seq)

  • Apply proteomics approaches as independent validation

  • Use genetic approaches (overexpression, knockdown) to confirm antibody specificity

• Dynamic range considerations:

  • Ensure detection methods can accommodate both basal and stress-induced protein levels

  • Test multiple extraction methods to ensure complete protein solubilization

  • Consider enrichment strategies for low-abundance stress-responsive proteins

This approach is particularly relevant for stress-responsive proteins like RGG box-containing RNA-binding proteins that show differential expression under osmotic stress, ABA, and salt treatments .

How can epitope mapping improve antibody performance for detecting plant RNA-binding proteins?

Epitope mapping is crucial for optimizing antibody performance when working with plant RNA-binding proteins like AtRGGA (similar to the senescence-associated proteins):

• Structural prediction integration:

  • Use AlphaFold or similar tools to predict protein structure

  • Identify surface-exposed regions likely to serve as effective epitopes

  • Avoid regions involved in RNA binding that might be inaccessible when the protein is functionally engaged

  • Target regions with high evolutionary conservation for cross-species applicability

• Peptide array technology:

  • Implement overlapping peptide arrays covering the entire protein sequence

  • Measure binding affinity to identify high-affinity epitopes

  • Validate epitope accessibility in native protein using hydrogen-deuterium exchange mass spectrometry

  • Distinguish between linear and conformational epitopes

• Functional epitope considerations:

  • For RNA-binding proteins like AtRGGA, avoid designing antibodies against the RGG box domain if protein-RNA interaction studies are planned

  • Target protein regions that remain accessible during stress responses or subcellular relocalization events

  • Consider epitopes outside of dimerization interfaces for proteins forming complexes

• Custom antibody design:

  • Develop epitope-specific monoclonal antibodies for increased specificity

  • Consider recombinant antibody technologies (scFv, nanobodies) for improved access to conformational epitopes

  • Implement epitope determination services for existing antibody combinations to deconvolute the most effective individual monoclonal antibodies

• Application-specific epitope selection:

  • Choose different epitopes for Western blotting (denatured proteins) versus immunoprecipitation (native proteins)

  • Target post-translational modification sites for detecting activated forms of signaling proteins

  • Develop phospho-specific antibodies for monitoring stress-responsive phosphorylation events

This approach is particularly valuable for RNA-binding proteins involved in stress responses, which may undergo conformational changes affecting epitope accessibility .

How can antibodies be used to investigate protein-RNA interactions in stress-responsive pathways?

Advanced methodologies for using antibodies to study protein-RNA interactions in stress-responsive pathways include:

• RNA immunoprecipitation (RIP) optimization:

  • Select antibodies targeting regions outside RNA-binding domains

  • Implement crosslinking strategies to capture transient interactions

  • Adjust extraction conditions to preserve native protein-RNA complexes

  • Include RNase inhibitors throughout the protocol

  • Validate results with reciprocal approaches (RNA pulldown)

• CLIP-seq adaptation for plant research:

  • Optimize UV crosslinking parameters for plant tissues

  • Develop plant-specific CLIP protocols considering cell wall barriers

  • Implement bioinformatic pipelines specifically for plant transcriptomes

  • Include controls for non-specific RNA binding

• Stress-specific considerations:

  • Design time-course experiments to capture dynamic interactions

  • Compare multiple stress conditions (osmotic, salt, drought, ABA treatment)

  • Integrate with transcriptomic and proteomic data for comprehensive pathway analysis

  • Consider subcellular compartmentalization of interactions during stress

• Visualization approaches:

  • Implement RNA-protein co-localization using fluorescent antibodies and RNA probes

  • Develop proximity ligation assays for in situ visualization of interactions

  • Apply super-resolution microscopy techniques for detailed spatial analysis

This approach is particularly relevant for studying RNA-binding proteins like AtRGGA, which have been implicated in osmotic stress responses and are regulated by ABA and salt treatments .

What are the methodological considerations for developing antibodies against plant proteins involved in drought and salt stress tolerance?

Development of effective antibodies against plant proteins involved in drought and salt stress tolerance requires specialized methodological considerations:

• Target protein selection strategies:

  • Prioritize proteins with differential expression under stress conditions

  • Focus on proteins with known roles in stress signaling pathways

  • Consider proteins like AtRGGA that are specifically regulated during osmotic stress

  • Target proteins in stomatal regulation pathways that control transpiration

• Epitope design considerations:

  • Avoid regions that undergo post-translational modifications during stress

  • Consider protein conformational changes induced by stress conditions

  • Target regions unique to specific family members for discriminating closely related proteins

  • Include species-conserved epitopes for cross-species applications

• Validation under stress conditions:

  • Verify antibody performance under the ionic conditions present during salt stress

  • Confirm epitope accessibility during protein relocalization events

  • Test recognition of stress-induced protein variants or isoforms

  • Validate in multiple tissues relevant to stress responses (roots, leaves, stomata)

• Application-specific optimization:

  • Develop dual-specificity antibodies recognizing both native and denatured forms

  • Optimize fixation protocols for preserving stress-induced protein associations

  • Consider native protein extraction methods that preserve stress-responsive complexes

  • Implement chromatin immunoprecipitation protocols for stress-responsive transcription factors

• Advanced screening approaches:

  • Implement active learning strategies to improve antibody-antigen binding prediction

  • Utilize phage display technologies for difficult targets

  • Consider synthetic antibody alternatives for challenging epitopes

These approaches are particularly relevant for studying proteins like RNA-binding proteins that regulate tolerance to salt and drought stress through dynamic interactions with target RNAs .

How can antibody-based techniques be integrated with functional genomics approaches to study plant stress responses?

Integration of antibody-based techniques with functional genomics creates powerful research platforms for studying plant stress responses:

• Multi-omics integration strategies:

  • Correlate protein levels (detected by antibodies) with transcriptomic changes

  • Connect protein-protein interactions with genetic interaction networks

  • Link post-translational modifications to metabolomic shifts during stress

  • Map protein localization changes to subcellular proteome dynamics

• CRISPR-based functional validation:

  • Generate precise gene edits targeting antibody epitopes as functional validation

  • Create epitope-tagged endogenous proteins for antibody-based pulldown

  • Implement CUT&RUN or CUT&Tag approaches for improved chromatin profiling

  • Develop multiplexed imaging approaches combining CRISPR-based labeling with antibody detection

• High-throughput phenotyping correlation:

  • Link protein expression patterns to automated phenotyping data

  • Develop tissue-specific antibody applications for spatial resolution of stress responses

  • Implement machine learning to correlate antibody-detected protein levels with stress tolerance phenotypes

  • Create predictive models relating protein biomarkers to plant performance under stress

• Temporal resolution enhancement:

  • Design time-course experiments capturing protein dynamics during stress onset and recovery

  • Develop biosensor antibody applications for live monitoring of protein activity

  • Implement microfluidic approaches for high-resolution temporal analysis

  • Connect protein temporal dynamics to transcriptional regulatory networks

• Translational applications:

  • Use antibodies as validation tools for genes identified in GWAS studies

  • Develop antibody-based screening platforms for crop improvement programs

  • Create diagnostic antibody tools for early detection of stress responses

  • Implement antibody-based techniques for monitoring transgenic or genome-edited lines

This integrated approach maximizes the value of antibody-based techniques for understanding complex stress response networks, such as those involving proteins like AtRGGA that regulate tolerance to salt and drought stress .

Comparison of Antibody Applications for Plant Protein Detection

Troubleshooting Guide for Common Antibody Issues in Plant Research

These comprehensive tables provide researchers with practical guidelines for optimizing antibody-based techniques in plant research, particularly for challenging targets like stress-responsive proteins or senescence-associated proteins such as At3g63230.