CINV2 Antibody

What is CINV2 and why is it significant in research?

CINV2 (Cytosolic Invertase 2) is an enzyme involved in sugar transport and carbon metabolism pathways . It plays a critical role in plant physiology by catalyzing the hydrolysis of sucrose into glucose and fructose in the cytosol. The significance of CINV2 in research stems from its involvement in fundamental metabolic processes that affect plant growth, development, and response to environmental stresses. Antibodies against CINV2 are valuable tools for studying its expression, localization, and functional interactions in various experimental systems.

What are the recommended validation steps for a CINV2 antibody?

When validating a CINV2 antibody, researchers should implement multiple approaches:

• Western blot analysis: Confirm specificity by detecting a band of the expected molecular weight (~63-68 kDa for CINV2)

• Immunoprecipitation: Verify the antibody can capture native CINV2 protein

• Immunofluorescence: Assess proper cytosolic localization pattern

• Knockout/knockdown controls: Test antibody on samples lacking CINV2 expression

• Cross-reactivity testing: Ensure the antibody doesn't recognize related invertase family members

• Epitope mapping: Characterize the specific binding region

How can I assess CINV2 antibody specificity in different experimental systems?

Specificity assessment requires a multi-platform approach:

When working across species, validation must be performed separately for each organism due to potential epitope variations in CINV2 homologs.

How does CINV2 expression vary across different tissue types?

CINV2 demonstrates tissue-specific expression patterns, with particularly high expression in metabolically active tissues. While the search results don't provide comprehensive expression data for CINV2 specifically, research indicates CINV2 is involved in sugar transport pathways and is found in phloem-adjacent tissues . When studying CINV2 expression:

• Consider tissue-specific controls when quantifying expression levels

• Account for developmental stages in your experimental design

• Be aware that stress conditions can alter normal expression patterns

• Use multiple detection methods (antibody-based western blot, RT-qPCR) to confirm expression profiles

What experimental approaches are most effective for studying CINV2 function?

To study CINV2 function effectively, consider these methodological approaches:

• Genetic manipulation: CRISPR/Cas9-mediated knockout or RNAi-based knockdown to assess phenotypic changes

• Protein interaction studies: Immunoprecipitation with CINV2 antibodies followed by mass spectrometry to identify binding partners

• Metabolic analysis: Measure glucose/fructose levels in response to CINV2 perturbation

• RNA silencing: Study transcriptional regulation of sugar transport pathways, as CINV2 has been implicated in phloem-restricted genetic processes

• Subcellular localization: Immunofluorescence with CINV2 antibodies to track protein distribution

What are the latest approaches for developing highly specific antibodies against CINV2?

Recent advances in antibody development offer promising approaches for generating CINV2-specific antibodies:

• Computational design: Fine-tuned RFdiffusion networks now enable de novo design of antibody variable domains that can bind to specific epitopes with atomic precision . This approach could be adapted to design antibodies targeting specific functional domains of CINV2.

• Memory B-cell selection: Isolating memory B cells that produce potent antibodies, similar to approaches used in viral antibody development . This method focuses on selecting B cells that produce antibodies with desired binding characteristics.

• Structure-guided epitope selection: Using structural data to identify unique, accessible regions of CINV2 that distinguish it from other invertase family members.

• Phage display optimization: Incorporating negative selection steps against related invertases to enhance specificity.

The computational approach using fine-tuned RFdiffusion networks has demonstrated success in designing antibodies that bind user-specified epitopes with high accuracy, with cryo-EM validation showing near-identical binding to the design model .

How can I optimize antibody affinity for CINV2 when studying low-abundance samples?

Optimizing antibody affinity for detecting low-abundance CINV2 requires:

• Affinity maturation techniques:

  • In vitro directed evolution through display technologies

  • Structure-guided mutation of complementarity-determining regions (CDRs)

  • Computational design to improve binding energetics

• Signal amplification methods:

  • Tyramide signal amplification for immunohistochemistry

  • Proximity ligation assays for enhanced sensitivity

  • Polymerized reporter enzyme systems

• Sample preparation optimization:

  • Enrichment of CINV2-containing fractions

  • Optimized extraction buffers to maintain protein integrity

  • Reduced non-specific binding through buffer optimization

When evaluating antibody binding, molecular docking predictions can assess interaction energies similar to approaches used in other antibody research . Key parameters to analyze include HADDOCK score, van der Waals energy, electrostatic energy, and desolvation energy .

What are the recommended protocols for immunoprecipitation of CINV2 in plant tissues?

For effective immunoprecipitation of CINV2 from plant tissues, follow these methodological guidelines:

• Buffer optimization: Use extraction buffers containing 50mM Tris-HCl (pH 7.5), 150mM NaCl, 5mM EDTA, 0.1% Triton X-100, supplemented with protease inhibitors and 1% glycerol to stabilize CINV2.

• Sample preparation:

  • Grind tissue in liquid nitrogen to prevent protein degradation

  • Maintain low temperature (4°C) throughout the procedure

  • Clarify lysates by centrifugation at 14,000×g for 15 minutes

• Antibody binding:

  • Pre-clear lysate with Protein A/G beads

  • Incubate with CINV2 antibody (2-5μg) overnight at 4°C

  • Add fresh Protein A/G beads for 2-3 hours

  • Perform stringent washes (4-5 times) with decreasing salt concentrations

• Controls: Include relevant controls similar to those used in AGO protein immunoprecipitation studies , such as:

  • Input sample before immunoprecipitation

  • IgG control antibody

  • Immunoprecipitation from CINV2 knockout tissue

• Elution strategies: Gentle elution with peptide competition or more stringent SDS-based elution depending on downstream applications.

How can I troubleshoot cross-reactivity issues with CINV2 antibodies?

When facing cross-reactivity challenges:

For critical applications, consider using multiple antibodies targeting different CINV2 epitopes to confirm findings, similar to approaches used in studying memory B-cell-derived antibodies .

How should I analyze CINV2 antibody binding data to assess epitope specificity?

When analyzing CINV2 antibody binding data:

• Binding affinity metrics: Calculate and compare:

  • HADDOCK scores

  • Van der Waals energy

  • Electrostatic energy

  • Desolvation energy

  • Buried surface area

  • PRODIGY's ΔG predictions

  These parameters have proven valuable in antibody binding analysis .

• Statistical approaches:

  • Apply Kruskall-Wallis tests to compare binding across multiple conditions

  • Use paired Wilcoxon-Mann-Whitney tests for direct comparisons

  • Establish statistical significance at 95% confidence level

• Visualization methods:

  • Generate heat maps of binding energies across different epitopes

  • Create structural models highlighting interaction interfaces

  • Plot affinity distributions for different antibody clones

• Correlation analysis: Assess relationships between computational predictions and experimental binding data, similar to correlations observed between in silico predictions and empirical IC50 values in antibody studies .

What approaches can differentiate between CINV2 isoforms when using antibodies?

To differentiate between CINV2 isoforms:

• Epitope mapping: Identify isoform-specific regions for targeted antibody development.

• Western blot optimization:

  • Use high-resolution SDS-PAGE (8-10%)

  • Extend running time to separate closely migrating isoforms

  • Consider using Phos-tag gels for phosphorylated isoforms

• Immunoprecipitation followed by mass spectrometry:

  • Enrich CINV2 with a pan-CINV2 antibody

  • Perform tryptic digestion

  • Analyze peptide fragments by LC-MS/MS

  • Identify isoform-specific peptides

• Recombinant protein controls: Express each isoform to validate antibody specificity and establish detection thresholds.

• Computational validation: Use molecular docking predictions to assess antibody binding to different isoforms, similar to methods used to predict antibody binding to different viral variants .