At5g38930 Antibody

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Product Specs

Buffer
Preservative: 0.03% ProClin 300. Constituents: 50% Glycerol, 0.01M PBS, pH 7.4.
Form
Liquid
Lead Time
14-16 weeks lead time (made-to-order)
Synonyms
At5g38930 antibody; K15E6.16 antibody; K15E6_110Germin-like protein subfamily 1 member 10 antibody
Target Names
At5g38930
Uniprot No.

Target Background

Function
This protein may be involved in plant defense mechanisms. Despite conservation of the active site, it is unlikely to possess oxalate oxidase activity.
Database Links

KEGG: ath:AT5G38930

STRING: 3702.AT5G38930.1

UniGene: At.55234

Protein Families
Germin family
Subcellular Location
Secreted, extracellular space, apoplast.

Q&A

What is AT5g38930 and why would researchers need antibodies against it?

AT5g38930 is a gene in the model plant Arabidopsis thaliana. Researchers develop antibodies against its protein product to study protein expression, localization, and function. While specific information about AT5g38930's function isn't detailed in current literature, antibodies against this protein would allow researchers to track its expression across different tissues, developmental stages, or stress conditions through techniques like Western blotting, immunohistochemistry, and immunoprecipitation .

What are the key considerations when selecting an AT5g38930 antibody for Arabidopsis research?

When selecting an antibody against AT5g38930:

  • Specificity verification: Confirm the antibody specifically recognizes AT5g38930 protein without cross-reactivity to other Arabidopsis proteins.

  • Validation documentation: Check if the manufacturer provides validation data in Arabidopsis systems specifically.

  • Application compatibility: Ensure the antibody has been validated for your intended application (Western blot, immunoprecipitation, etc.).

  • Host species compatibility: Consider the host species to avoid cross-reactivity in multi-antibody experiments.

  • Epitope information: Know which region of AT5g38930 the antibody targets, as this affects recognition of potential isoforms or modified forms .

What methods can I use to validate an AT5g38930 antibody before experimental use?

To validate an AT5g38930 antibody:

  • Gene knockdown/knockout verification: Test the antibody in tissues from AT5g38930 knockdown/knockout plants to confirm signal reduction or elimination.

  • Overexpression testing: Test in samples overexpressing AT5g38930 to verify increased signal.

  • Multiple cell line/tissue testing: Compare antibody reactivity across different tissues with known expression levels based on transcriptomic data.

  • Orthogonal method comparison: Compare protein expression detected by the antibody with RNA expression data from RT-qPCR.

  • Multiple antibody verification: Use two different antibodies targeting different epitopes of AT5g38930 .

Note: Negative results from knockdown experiments must be interpreted cautiously as they could result from:

  • Insufficient knockdown at RNA level

  • Inefficient protein reduction at the timepoint studied

  • Off-target effects of RNAi

How should I design proper controls for AT5g38930 antibody experiments in Arabidopsis?

A robust experimental design should include:

  • Positive controls:

    • Recombinant AT5g38930 protein (if available)

    • Tissues with confirmed high expression of AT5g38930

    • Overexpression lines of AT5g38930

  • Negative controls:

    • Knockout/knockdown lines of AT5g38930

    • Pre-immune serum (for polyclonal antibodies)

    • Isotype control (for monoclonal antibodies)

    • Secondary antibody-only control

  • Technical controls:

    • Loading controls (e.g., anti-actin or anti-tubulin)

    • Competitor peptide blocking to verify specificity

What is the optimal protein extraction method for detecting AT5g38930 in Arabidopsis tissues?

For optimal protein extraction from Arabidopsis tissues:

  • Tissue collection and preparation:

    • Collect 100-300 mg of fresh tissue

    • Flash-freeze in liquid nitrogen

    • Store at -80°C until processing

    • Grind to fine powder with cold mortar and pestle

  • Extraction buffer components:

    • Nuclear extraction buffer for nuclear proteins

    • Consider detergent selection based on AT5g38930's predicted cellular localization

    • Include protease inhibitors to prevent degradation

    • Add phosphatase inhibitors if studying phosphorylation status

  • Specific considerations:

    • If AT5g38930 is membrane-associated, include appropriate detergents

    • If studying protein-protein interactions, use gentler extraction conditions

    • For chromatin-associated proteins, consider crosslinking with 1% formaldehyde before extraction

How can I use an AT5g38930 antibody to study protein expression changes during plant stress responses?

To study AT5g38930 expression during stress responses:

  • Experimental setup:

    • Subject plants to relevant stresses (e.g., cold, heat, drought, pathogens)

    • Collect tissues at multiple timepoints

    • Include unstressed controls

  • Quantitative Western blot approach:

    • Use standardized protein amounts

    • Include loading controls (e.g., anti-PP2A for Arabidopsis)

    • Analyze band intensity with software like ImageJ

    • Calculate relative expression using the 2^-ΔΔCT method

  • Immunolocalization studies:

    • Fix tissues with appropriate fixatives (e.g., 90% acetone or 1% formaldehyde)

    • Section tissues for consistent comparison

    • Use fluorescently-labeled secondary antibodies for co-localization studies

    • Image with consistent microscope settings across samples

How can I use chromatin immunoprecipitation (ChIP) with an AT5g38930 antibody if it's a DNA-binding protein?

For ChIP applications with an AT5g38930 antibody:

  • Sample preparation:

    • Collect 100-300 mg of seedlings

    • Crosslink with 1% formaldehyde for 15 min

    • Quench with glycine for 5 min

    • Freeze in liquid nitrogen and store at -80°C

  • Chromatin isolation and fragmentation:

    • Extract chromatin using nuclear extraction buffer

    • Fragment using sonication (e.g., Ultrasonic Disruptors UD-201)

    • Verify fragment size (200-500 bp is optimal)

  • Immunoprecipitation:

    • Pre-clear chromatin with protein A/G beads

    • Incubate with AT5g38930 antibody overnight at 4°C

    • Use appropriate beads (Dynabeads with Protein A or G)

    • Include negative controls (non-specific IgG, input sample)

  • DNA purification and analysis:

    • Elute DNA from beads overnight at 65°C

    • Purify using a PCR purification kit

    • Quantify with qPCR relative to input samples

    • Normalize to a negative control locus (e.g., TA3 retrotransposon)

How can I assess antibody specificity if I suspect my AT5g38930 antibody has cross-reactivity issues?

To assess potential cross-reactivity issues:

  • Mass spectrometry analysis of immunoprecipitates:

    • Perform immunoprecipitation with the AT5g38930 antibody

    • Analyze pulled-down proteins by mass spectrometry

    • Look for enrichment of unexpected proteins

  • Comparative analysis with multiple antibodies:

    • Test different anti-AT5g38930 antibodies (from different manufacturers or clones)

    • Compare banding patterns across antibodies

    • Investigate discrepancies with proteomic approaches

  • Batch effect investigation:

    • Compare different lots of the same antibody

    • Perform replicate experiments with antibodies from different sources

    • Conduct Western blots of immunoprecipitated samples to identify potential cross-reactivity

Table: Strategies to Verify Antibody Specificity

Verification MethodAdvantagesLimitationsImplementation Notes
Knockout/Knockdown ValidationGold standard for specificityRequires genetic resourcesMay need multiple timepoints to confirm protein reduction
Multiple Cell Types/TissuesTests antibody across expression rangeRequires prior knowledge of expression patternsCompare antibody signal to -omics data
Immunoprecipitation + Mass SpecIdentifies all bound proteinsExpensive, requires specialized equipmentCheck for enrichment ratios of target vs. non-targets
Orthogonal Methods ComparisonCorrelates antibody results with independent methodsMay show discrepancies due to post-transcriptional regulationCompare protein levels to RNA levels
Epitope CompetitionConfirms epitope specificityRequires purified peptide/proteinPre-incubate antibody with excess target

How can I use AT5g38930 antibodies to study protein-protein interactions in Arabidopsis?

For protein-protein interaction studies:

  • Co-immunoprecipitation (Co-IP):

    • Use native extraction conditions to preserve protein complexes

    • Perform IP with the AT5g38930 antibody

    • Analyze precipitated proteins by Western blot with antibodies against suspected interactors

    • Alternatively, use mass spectrometry for unbiased interaction discovery

  • Proximity ligation assay (PLA):

    • Fix plant tissues with appropriate fixatives

    • Incubate with AT5g38930 antibody and antibody against suspected interactor

    • Use species-specific PLA probes

    • Analyze fluorescent signal indicating proximity (<40 nm)

  • Dual-label immunofluorescence:

    • Use antibodies raised in different host species

    • Apply fluorophore-conjugated secondary antibodies

    • Analyze co-localization using confocal microscopy

    • Calculate co-localization coefficients using image analysis software

How do I interpret contradictory results between antibody-based detection and gene expression data for AT5g38930?

When facing contradictions between antibody detection and gene expression:

  • Possible biological explanations:

    • Post-transcriptional regulation: RNA levels don't always correlate with protein levels

    • Protein stability differences: Proteins may persist after mRNA degradation

    • Developmental or spatial regulation: Whole-tissue RNA may not reflect localized protein expression

    • Post-translational modifications: May affect antibody recognition

  • Technical considerations:

    • Antibody specificity issues: Verify antibody detects correct protein

    • Sensitivity differences: RNA detection methods may be more sensitive than protein detection

    • Sample preparation differences: Different extraction methods for RNA vs. protein

  • Resolution approaches:

    • Use multiple antibodies targeting different epitopes

    • Perform time-course experiments to detect temporal differences

    • Employ cell/tissue-specific analyses rather than whole-tissue approaches

    • Validate with orthogonal methods (e.g., GFP-tagging of endogenous protein)

What should I do if my AT5g38930 antibody shows unexpected bands on Western blots?

When encountering unexpected bands:

  • Analytical steps:

    • Compare observed vs. expected molecular weight

    • Check for potential isoforms of AT5g38930 in databases

    • Assess if bands could represent modified forms (phosphorylation, glycosylation)

    • Determine if bands could be degradation products

  • Validation approaches:

    • Test in knockout/knockdown tissue to see which bands disappear

    • Perform peptide competition assays to identify specific vs. non-specific bands

    • Use different antibodies against AT5g38930 to compare band patterns

    • Excise bands for mass spectrometry identification

  • Technical optimizations:

    • Adjust blocking conditions to reduce non-specific binding

    • Try different extraction methods to minimize protein degradation

    • Optimize antibody concentration and incubation conditions

    • Consider using gradient gels for better resolution

How can I apply machine learning approaches to improve AT5g38930 antibody-based experimental design?

Machine learning can enhance antibody-based research:

  • Experimental design optimization:

    • Use supervised learning methods (LR, LDA, QDA, NB, KNN) to predict optimal experimental conditions

    • Apply cross-validation, randomizations, and permutations to evaluate method performance

    • Combine multiple learning methods through averaging and stacking for improved predictions

  • Data analysis applications:

    • Predict epitope accessibility based on protein structure modeling

    • Optimize antibody selection by analyzing binding characteristics

    • Improve signal detection through automated image analysis

    • Identify potential cross-reactivity through sequence similarity analysis

  • Active learning implementation:

    • Apply active learning strategies to iteratively improve antibody selection

    • Reduce experimental costs by prioritizing the most informative experiments

    • Enhance out-of-distribution prediction for novel antibody applications

    • Adapt library-on-library approaches for comprehensive antibody characterization

Table: Comparison of Machine Learning Methods for Antibody Research

MethodAdvantagesLimitationsBest Applications
Logistic Regression (LR)Simple, interpretableMay miss complex relationshipsInitial screening, binary classification
Linear Discriminant Analysis (LDA)Works well with small sample sizesAssumes normal distributionMulti-class problems with limited data
Quadratic Discriminant Analysis (QDA)Captures non-linear relationshipsRequires more training dataComplex binding pattern prediction
Naive Bayes (NB)Fast, works with limited dataAssumes feature independencePreliminary epitope prediction
K-Nearest Neighbors (KNN)No training phase, adaptableComputationally intensive for large datasetsSimilarity-based antibody selection
Combined MethodsHigher accuracy, robustIncreased complexityCross-reactivity prediction, optimal condition selection

How can emerging antibody technologies improve AT5g38930 protein research in Arabidopsis?

Emerging technologies for AT5g38930 research:

  • Nanobody development:

    • Small (15 kDa) antibody fragments derived from camelid antibodies

    • Superior tissue penetration for in vivo imaging

    • Potential for engineering multi-specific binding domains

    • Enhanced stability for challenging experimental conditions

  • Structure-guided antibody engineering:

    • Design antibodies targeting specific epitopes of AT5g38930

    • Create antibodies that distinguish between post-translationally modified forms

    • Develop antibodies with altered binding kinetics for specific applications

    • Generate antibodies that only recognize specific protein conformations

  • Active learning approaches:

    • Implement iterative experimental design to optimize antibody development

    • Reduce required experimental data by up to 35%

    • Accelerate learning process compared to random approaches

    • Enhance prediction of binding characteristics for novel antibody variants

How can I design a sequential immunization strategy to develop more specific antibodies against AT5g38930?

For optimal immunization strategy:

  • Epitope selection and preparation:

    • Analyze AT5g38930 sequence for immunogenic regions

    • Consider highly conserved regions for broad recognition

    • Design epitopes that avoid regions similar to other Arabidopsis proteins

    • Create stabilized protein constructs that maintain native structure

  • Sequential immunization protocol:

    • Prime with germline-targeting immunogen

    • Boost with progressively less-mutated design intermediates

    • Final boost with native protein structure

    • Monitor antibody development through regular serum testing

  • Advanced design considerations:

    • Use mammalian cell display for antibody optimization

    • Implement directed evolution to improve specificity

    • Consider combinatorial libraries exploring multiple immunogenic domains

    • Test antibody cross-reactivity with closely related proteins

What are the latest advances in antibody validation that should be applied to AT5g38930 antibodies?

Recent advances in antibody validation include:

  • Enhanced genetic validation approaches:

    • CRISPR/Cas9 knockout systems for complete target elimination

    • Inducible knockdown systems for temporal control

    • Tagged endogenous proteins as parallel detection systems

    • Multi-allelic validation across different Arabidopsis ecotypes

  • Multi-omics integration:

    • Correlation of antibody signals with transcriptomics data

    • Proteomics verification of antibody-detected proteins

    • Incorporation of protein interaction networks for validation

    • Consideration of post-translational modifications through phosphoproteomics

  • Database integration and standardization:

    • Contribution to antibody validation databases like AbDb

    • Detailed documentation of validation procedures

    • Standardized reporting of antibody performance metrics

    • Cross-referencing with emerging antibody standard initiatives

Table: Recommended Validation Framework for AT5g38930 Antibodies

Validation PillarBasic RequirementsAdvanced ImplementationExpected Outcome
Genetic StrategiesTest in knockout/knockdown linesMulti-allelic testing, inducible systemsConfirmation of specificity in genetic context
Independent Antibody TargetingCompare multiple antibodiesEpitope mapping, competitive bindingVerification of recognition pattern consistency
Expression VerificationCompare to known expression patternsSingle-cell resolution, developmental seriesAlignment with established expression data
Orthogonal Method ComparisonCompare to RNA expressionIntegration with proteomics, metabolomicsMulti-level validation of expression patterns
Technical ValidationReproducibility testingInterlaboratory testing, standardized protocolsRobust performance across experimental conditions

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