At2g47970 Antibody

What is At2g47970 and why is it important in plant research?

At2g47970 encodes the NPL4-like protein 2 in Arabidopsis thaliana (Mouse-ear cress), a widely used model organism in plant biology. This protein belongs to the NPL4 family that functions in nuclear pore localization . Based on comparative genomic studies, At2g47970 is located on chromosome 2 at position 19,636,488 in the Arabidopsis genome . The protein has a molecular weight of approximately 39,788 Da and plays roles in cellular processes that are still being actively investigated. Understanding this protein's function contributes to our knowledge of nuclear trafficking mechanisms in plants, which can have implications for plant development, stress responses, and cellular homeostasis.

What are the key specifications of commercially available At2g47970 antibodies?

Most commercial At2g47970 antibodies are polyclonal antibodies raised in rabbits using recombinant Arabidopsis thaliana At2g47970 protein as the immunogen . These antibodies are typically provided in liquid form with preservatives like 0.03% Proclin 300 and stabilizers such as 50% glycerol in PBS (pH 7.4) . They are generally non-conjugated and unmodified, making them suitable for various applications including ELISA and Western Blot analysis. When working with these antibodies, researchers should note that small volumes may occasionally become entrapped in the seal of the product vial during shipment and storage, which may require brief centrifugation to dislodge .

How do I properly store and handle At2g47970 antibodies to maintain their activity?

For optimal preservation of antibody activity, At2g47970 antibodies should be stored at -20°C or -80°C immediately upon receipt . Avoid repeated freeze-thaw cycles as they can denature the antibody and reduce its efficacy. When handling the antibody for experiments, it's advisable to aliquot the stock solution into smaller volumes before freezing to minimize freeze-thaw cycles. Before use, thaw the antibody on ice and centrifuge briefly if necessary to collect any solution trapped in the cap. For working solutions, maintain the antibody at 4°C for short-term use (up to one week) and return to -20°C for long-term storage. Always handle antibodies with clean pipette tips to prevent contamination.

What are the validated applications for At2g47970 antibodies in plant research?

At2g47970 antibodies have been validated for several experimental applications, primarily ELISA (Enzyme-Linked Immunosorbent Assay) and Western Blot analysis . In Western blotting, these antibodies can detect the native NPL4-like protein 2 in plant tissue extracts, allowing researchers to study protein expression patterns across different developmental stages or in response to various treatments. Similar to applications with other plant protein antibodies, researchers have used comparable antibodies to detect both target proteins and related family members in wild-type seed extracts . When designing experiments, it's important to include appropriate positive and negative controls to ensure specificity and validate results.

How should I optimize Western blot protocols specifically for At2g47970 antibodies?

For optimal Western blot results with At2g47970 antibodies, consider the following protocol adjustments:

• Sample preparation: Extract total protein from Arabidopsis tissues using a buffer containing protease inhibitors to prevent degradation. For nuclear proteins like NPL4-family members, nuclear extraction protocols may yield better results.

• Gel selection: Use 10-12% SDS-PAGE gels for optimal separation of the ~40 kDa At2g47970 protein.

• Transfer conditions: Transfer proteins to PVDF or nitrocellulose membranes at 100V for 1 hour or 30V overnight at 4°C.

• Blocking: Block with 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature.

• Primary antibody incubation: Dilute the At2g47970 antibody at 1:1000 to 1:5000 in blocking buffer and incubate overnight at 4°C.

• Washing and secondary antibody: Wash 3-5 times with TBST and incubate with anti-rabbit HRP-conjugated secondary antibody (1:5000-1:10000) for 1 hour at room temperature.

• Detection: Use enhanced chemiluminescence (ECL) for detection, with exposure times ranging from 30 seconds to 5 minutes depending on expression levels.

This protocol should be further optimized based on your specific experimental conditions and the particular antibody batch being used.

What controls should I include when performing immunolocalization studies with At2g47970 antibodies?

For immunolocalization studies with At2g47970 antibodies, include the following controls to ensure result validity:

• Positive control: Include wild-type Arabidopsis samples known to express At2g47970 protein. This helps confirm that your antibody detection system is working properly.

• Negative control: Use samples from At2g47970 knockout or knockdown lines, if available, to confirm antibody specificity.

• Secondary antibody control: Omit the primary antibody but include all other steps to detect any non-specific binding of the secondary antibody.

• Preabsorption control: Preincubate the antibody with excess recombinant At2g47970 protein before staining to demonstrate binding specificity.

• Isotype control: Use an irrelevant antibody of the same isotype (IgG) and host species (rabbit) at the same concentration to identify non-specific binding.

For immunofluorescence microscopy, include DAPI nuclear staining to verify the expected nuclear pore localization pattern that would be consistent with the NPL4 protein family function .

How can I verify the specificity of an At2g47970 antibody?

Verifying antibody specificity is crucial for reliable results. For At2g47970 antibodies, implement these validation approaches:

• Western blot analysis: Confirm that the antibody detects a band of the expected molecular weight (~40 kDa) in wild-type Arabidopsis extracts, with reduced or absent signal in At2g47970 mutant lines.

• Immunoprecipitation followed by mass spectrometry: Perform IP with the antibody and analyze the precipitated proteins by mass spectrometry to confirm that At2g47970 is among the enriched proteins.

• Cross-reactivity testing: Test the antibody against recombinant proteins of other NPL4 family members to assess potential cross-reactivity, particularly important since plant genomes often contain multiple related genes.

• Epitope mapping: If multiple antibodies against different epitopes of At2g47970 are available, compare their specificity profiles to ensure consistent detection.

• Genetic validation: Compare antibody staining patterns between wild-type plants and those with altered At2g47970 expression (overexpression, knockdown, or knockout lines).

This multi-method approach ensures the antibody's specificity and reliability for downstream applications.

What are the known cross-reactivity issues with At2g47970 antibodies?

While specific cross-reactivity data for At2g47970 antibodies is limited in the search results, general principles about plant protein antibodies apply. Polyclonal antibodies against plant proteins may cross-react with related family members, especially those sharing high sequence homology. The NPL4 protein family has conserved domains across plant species, potentially leading to cross-species reactivity.

Based on experiences with other plant antibodies, we know that antibodies can sometimes detect multiple isoforms or related proteins. For example, some antibodies against embryogenesis abundant proteins can detect both targeted and related proteins in seed extracts . This cross-reactivity can be either advantageous (when studying protein families) or problematic (when specific detection is required).

To address potential cross-reactivity concerns:

• Perform sequence alignment analysis of At2g47970 with related proteins to predict potential cross-reactivity.

• Include appropriate controls (knockout/knockdown lines) in experiments.

• Consider pre-absorbing the antibody with recombinant proteins of closely related family members to increase specificity.

• When possible, validate results using complementary techniques like gene expression analysis or mass spectrometry.

What are the most common problems encountered when using At2g47970 antibodies in Western blots?

Researchers working with plant protein antibodies including At2g47970 antibodies commonly encounter these issues in Western blots:

• Weak or no signal:

  • Possible causes: Low protein expression, antibody degradation, insufficient transfer, or suboptimal antibody dilution

  • Solutions: Increase sample loading, use fresh antibody, optimize transfer conditions, or try different antibody concentrations

• Multiple bands:

  • Possible causes: Cross-reactivity with related proteins, protein degradation, or post-translational modifications

  • Solutions: Optimize extraction buffers with protease inhibitors, increase blocking time/concentration, or perform pre-absorption with related proteins

• High background:

  • Possible causes: Insufficient blocking, excessive antibody concentration, or inadequate washing

  • Solutions: Increase blocking time/concentration, dilute antibody further, extend washing steps, or try different blocking agents

• Unexpected band size:

  • Possible causes: Post-translational modifications, splice variants, or sample degradation

  • Solutions: Use different sample preparation methods, include denaturing agents, or compare with recombinant protein standards

When working specifically with the nuclear pore-associated NPL4 family proteins like At2g47970, optimizing nuclear protein extraction protocols may improve detection results.

How can I improve antibody sensitivity for detecting low-abundance At2g47970 protein?

For detecting low-abundance At2g47970 protein, implement these sensitivity enhancement strategies:

• Sample enrichment:

  • Perform subcellular fractionation to enrich nuclear proteins where NPL4 family proteins are primarily localized

  • Use immunoprecipitation to concentrate the protein before Western blotting

• Signal amplification:

  • Employ more sensitive detection systems like enhanced chemiluminescence (ECL) Plus or SuperSignal West Femto

  • Consider using biotin-streptavidin amplification systems with biotinylated secondary antibodies

• Protocol optimization:

  • Increase antibody incubation time (overnight at 4°C)

  • Reduce washing stringency slightly (shorter wash times or lower detergent concentration)

  • Try different membrane types (PVDF often provides higher sensitivity than nitrocellulose)

  • Use signal enhancers like 0.05% SDS or 0.1% Tween-20 in primary antibody solution

• Alternative detection methods:

  • Consider using more sensitive techniques like ELISA or proximity ligation assay (PLA)

  • Try immunofluorescence with signal amplification for localization studies

• Protein loading:

  • Increase total protein loading (up to 50-100 μg per lane)

  • Use loading controls to normalize signals across samples

These approaches can significantly improve detection sensitivity for low-abundance proteins like At2g47970.

What strategies can help troubleshoot non-specific binding with At2g47970 antibodies?

To address non-specific binding issues with At2g47970 antibodies:

• Blocking optimization:

  • Test different blocking agents (5% milk, 3-5% BSA, commercial blocking buffers)

  • Extend blocking time to 2-3 hours at room temperature or overnight at 4°C

  • Add 0.1-0.3% Tween-20 to blocking buffer to reduce hydrophobic interactions

• Antibody dilution and incubation:

  • Increase antibody dilution (try 1:2000, 1:5000, or higher)

  • Prepare antibody in fresh blocking buffer

  • Add 0.05-0.1% SDS to antibody solution to reduce non-specific interactions

  • Pre-absorb antibody with plant extract from At2g47970 knockout lines

• Washing conditions:

  • Increase number of washes (5-6 times, 10 minutes each)

  • Use higher concentration of Tween-20 (0.1-0.2%) in wash buffer

  • Consider adding low salt (150-300 mM NaCl) to wash buffer

• Cross-linker treatment:

  • Treat membrane with protein cross-linkers after transfer to stabilize proteins

  • Use reversible protein stains to confirm transfer efficiency before blocking

• Sample preparation:

  • Include additional reducing agents in sample buffer

  • Pre-clear lysates before loading to remove components that cause non-specific binding

Testing multiple approaches systematically will help identify the optimal conditions for reducing non-specific binding while maintaining specific signal detection.

How can computational approaches improve experimental design for At2g47970 antibody-based research?

Computational approaches can significantly enhance experimental design for At2g47970 antibody-based research:

• Epitope prediction and antibody design:

  • Utilize computational protocols like IsAb to design antibodies with improved specificity and affinity

  • Perform in silico epitope mapping to identify unique regions of At2g47970 that minimize cross-reactivity with related proteins

  • Apply alanine scanning simulations to predict antibody-antigen interaction hotspots

• Structural analysis and docking:

  • Use RosettaAntibody to predict the 3D structure of antibody-antigen complexes

  • Apply RosettaRelax to minimize energy and improve the accuracy of docking predictions

  • Perform two-step docking (global and local) to identify optimal binding conformations

• Cross-reactivity prediction:

  • Use BLAST and other sequence alignment tools to identify potential cross-reactive proteins

  • Apply molecular dynamics simulations to assess binding stability and specificity

  • Create homology models of related proteins to evaluate potential binding interfaces

• Experimental data analysis:

  • Implement finite mixture models for antibody data analysis to differentiate between specific and non-specific binding populations

  • Use machine learning approaches to predict antibody-antigen binding patterns based on existing datasets

  • Apply active learning strategies to optimize experimental design and reduce the number of required experiments

These computational approaches can save time and resources by helping researchers design more specific antibodies, predict potential experimental outcomes, and optimize protocols before conducting wet-lab experiments.

What are the emerging techniques for improving At2g47970 antibody specificity and affinity?

Several cutting-edge techniques can enhance At2g47970 antibody specificity and affinity:

• Computational affinity maturation:

  • Apply the computational affinity maturation protocol described in the IsAb workflow to design antibodies with theoretically increased affinity and stability

  • Identify key residues through computational alanine scanning to predict potential hotspots for affinity improvement

  • Optimize antibody structure using Rosetta force field to drive it toward its native state and improve binding properties

• Antibody engineering approaches:

  • Implement directed evolution techniques to screen for higher-affinity variants

  • Apply site-directed mutagenesis guided by computational predictions to modify complementarity-determining regions (CDRs)

  • Create single-chain variable fragments (scFvs) or antigen-binding fragments (Fabs) with improved specificity

• Advanced selection methods:

  • Use library-on-library screening approaches to identify specific interacting pairs between antibodies and antigens

  • Apply active learning strategies to efficiently identify optimal antibody variants with reduced experimental burden

  • Employ machine learning models to predict target binding by analyzing many-to-many relationships between antibodies and antigens

• Epitope-focused approaches:

  • Design antibodies targeting unique regions of At2g47970 identified through comprehensive epitope mapping

  • Create multi-specific antibodies that recognize two distinct epitopes on At2g47970 for improved specificity

  • Develop conformation-specific antibodies that recognize the protein in its native structural context

Implementation of these advanced techniques can lead to next-generation At2g47970 antibodies with superior performance characteristics for research applications.

How can I design experiments to investigate potential cross-reactivity between At2g47970 antibodies and other plant proteins?

To systematically investigate potential cross-reactivity of At2g47970 antibodies with other plant proteins:

• In silico analysis:

  • Perform sequence alignment of At2g47970 with other NPL4 family proteins and related sequences

  • Identify regions of high sequence homology that might contribute to cross-reactivity

  • Use epitope prediction tools to map the antibody binding sites and assess their uniqueness

• Recombinant protein panel testing:

  • Express and purify recombinant versions of At2g47970 and related proteins

  • Perform Western blot or ELISA with serial dilutions of each protein

  • Create a cross-reactivity profile based on relative binding affinities

• Knockout/knockdown validation:

  • Test antibody reactivity in plant tissues from wild-type, At2g47970 knockout, and knockouts of related genes

  • Quantify signal reduction in various knockout lines to assess contribution of each protein to the observed signal

  • Design a table listing relative antibody reactivity across different genetic backgrounds:

• Competitive binding assays:

  • Pre-incubate antibody with increasing concentrations of recombinant proteins

  • Assess remaining binding capacity to immobilized At2g47970

  • Calculate IC50 values to quantify relative affinities

• Mass spectrometry validation:

  • Perform immunoprecipitation with the At2g47970 antibody

  • Analyze precipitated proteins by mass spectrometry

  • Identify all proteins captured by the antibody to create a comprehensive cross-reactivity profile

This systematic approach will provide a detailed understanding of antibody specificity and cross-reactivity, essential for accurate interpretation of experimental results.

How are At2g47970 antibodies being used in current plant stress response research?

At2g47970 encodes an NPL4 family protein involved in nuclear pore localization, potentially playing roles in stress signaling pathways. While specific research on At2g47970 antibodies in stress response studies is limited in the provided search results, researchers are likely using these antibodies to:

• Track protein abundance changes:

  • Monitor At2g47970 protein levels under various abiotic stresses (drought, salt, temperature extremes)

  • Investigate post-translational modifications that may regulate protein function during stress

• Study protein localization dynamics:

  • Examine changes in subcellular localization during stress responses using immunofluorescence microscopy

  • Investigate potential stress-induced nuclear-cytoplasmic shuttling behaviors

• Identify protein interaction networks:

  • Use co-immunoprecipitation with At2g47970 antibodies to identify stress-responsive protein complexes

  • Apply proximity-dependent biotin labeling combined with immunoprecipitation to map protein neighborhoods

• Functional studies:

  • Compare stress responses between wild-type and At2g47970 mutant plants using antibodies against stress markers

  • Investigate the role of At2g47970 in nuclear transport during stress conditions

Future stress research could employ active learning strategies to optimize experimental designs for investigating At2g47970 function under various stress conditions, potentially reducing the number of required experiments by up to 35% as demonstrated for other antibody-antigen studies .

What novel approaches are being developed for improving the sensitivity of At2g47970 detection in plant tissues?

Emerging approaches to enhance At2g47970 detection sensitivity include:

• Single-molecule detection methods:

  • Implement single-molecule pull-down (SiMPull) assays for detecting low-abundance proteins

  • Apply proximity ligation assays (PLA) to visualize individual At2g47970 molecules and their interactions in situ

• Signal amplification technologies:

  • Utilize tyramide signal amplification (TSA) to enhance immunofluorescence detection by 10-100 fold

  • Implement rolling circle amplification (RCA) for antibody-based detection of low-abundance proteins

• Advanced imaging approaches:

  • Apply super-resolution microscopy techniques (STED, PALM, STORM) for nanoscale visualization of At2g47970 localization

  • Combine light-sheet microscopy with cleared plant tissues for whole-organ protein localization

• Computational enhancement methods:

  • Use machine learning algorithms to distinguish true signals from background noise

  • Apply active learning strategies similar to those developed for antibody-antigen prediction to optimize detection protocols

• Alternative labeling strategies:

  • Develop split-protein complementation assays specific for At2g47970

  • Create fluorescent protein fusions for live-cell imaging as complementary approaches to antibody-based detection

These innovative approaches collectively offer promising avenues for enhancing the sensitivity and specificity of At2g47970 detection in complex plant tissue samples.

How can machine learning and active learning approaches optimize antibody-based experiments for At2g47970 research?

Machine learning and active learning approaches offer significant advantages for optimizing At2g47970 antibody-based experiments:

• Experimental design optimization:

  • Apply active learning strategies to iteratively select the most informative experiments, potentially reducing the number of required experiments by up to 35%

  • Use computational models to predict antibody-antigen binding, accelerating the learning process by approximately 28 steps compared to random sampling approaches

• Data analysis improvements:

  • Implement finite mixture models to analyze antibody data, helping distinguish between specific binding, non-specific binding, and background signals

  • Apply deep learning algorithms to extract patterns from complex immunofluorescence or Western blot images

• Antibody specificity enhancement:

  • Use machine learning to analyze epitope-paratope interactions and predict modifications that could improve specificity

  • Train models on existing antibody cross-reactivity data to predict potential off-target binding

• Protocol optimization:

  • Develop algorithms that can systematically vary experimental parameters (antibody concentration, incubation time, buffer composition) and predict optimal conditions

  • Create digital twins of experimental workflows to simulate outcomes before conducting physical experiments

• Out-of-distribution prediction:

  • Apply models trained to handle out-of-distribution scenarios, particularly valuable when working with novel plant systems or conditions not represented in training data

  • Use library-on-library approaches to identify specific interacting pairs between antibodies and antigens in complex mixtures

These computational approaches can significantly reduce experimental burden while improving data quality and reproducibility in At2g47970 research.