At5g42100 Antibody

What is At5g42100 and why is it significant in plant research?

At5g42100 encodes Glucan endo-1,3-beta-glucosidase 10 (EC 3.2.1.39) in Arabidopsis thaliana, a key enzyme involved in cell wall metabolism and plant defense responses . The protein (UniProt ID: Q9FHX5) plays important roles in:

• Plant immune responses through pathogen-associated molecular pattern recognition

• Cell wall remodeling during growth and development

• Stress response mechanisms

Understanding this protein's localization and function contributes to our knowledge of plant developmental biology and stress physiology, making antibodies against it valuable research tools for plant scientists .

How are At5g42100 antibodies typically produced and characterized?

At5g42100 antibodies are predominantly produced as rabbit polyclonal antibodies using one of two main approaches:

• Recombinant protein approach: Using larger antigenic subsequences (typically ~100 amino acids)

  • Higher success rate (up to 55% of antibodies show high-confidence detection)

  • Example: Recombinant Arabidopsis thaliana At5g42100 protein (amino acids 27-425) as immunogen

• Peptide approach: Using smaller peptides (up to 15 amino acids)

  • Lower success rate as noted in comparative studies

The characterization process typically involves:

• ELISA and Western Blot validation

• Bioinformatic analysis to identify potential antigenic regions

• Database searches using blastX to check for potential cross-reactivity

• Validation against mutant backgrounds where available

What factors affect antibody specificity and how can researchers evaluate At5g42100 antibody quality?

The specificity of At5g42100 antibodies is influenced by several factors:

• Production method: Recombinant protein antibodies generally show higher specificity than peptide antibodies for plant proteins

• Antigenic region selection: Bioinformatic analysis is critical for choosing antigenic regions with:

  • <40% similarity score at amino acid level with other proteins

  • Sufficient size (~100 amino acids) for recombinant approaches

• Affinity purification: Research shows this process "massively improved the detection rate" for Arabidopsis antibodies

Evaluation strategies include:

• Testing against corresponding mutant backgrounds (e.g., At5g42100 knockout lines)

• Western blot analysis for single-band detection at expected molecular weight

• Immunocytochemistry validation for expected subcellular localization patterns

• Cross-reactivity assessment against related proteins

What are the recommended protocols for optimizing Western Blot with At5g42100 antibodies?

For optimal Western Blot results with At5g42100 antibodies, researchers should consider:

• Sample preparation:

  • Use fresh Arabidopsis tissue (preferably roots where expression is documented)

  • Include protease inhibitors during extraction

  • Denature proteins completely before loading

• Blocking optimization:

  • Test different blocking agents (BSA vs. non-fat milk)

  • Optimize blocking time and temperature

• Antibody dilution:

  • Start with manufacturer's recommended dilution

  • Titrate antibody concentration if background issues occur

  • Typical recommended dilutions must be determined by end-user

• Validation controls:

  • Include At5g42100 mutant extracts as negative controls

  • Consider using recombinant At5g42100 protein as positive control

  • Include molecular weight markers to confirm target band size

How should researchers approach immunolocalization studies using At5g42100 antibodies?

Successful immunolocalization with At5g42100 antibodies requires:

• Fixation optimization:

  • Test different fixatives (paraformaldehyde vs. glutaraldehyde)

  • Optimize fixation time to preserve antigen accessibility

• Antigen retrieval:

  • Consider heat-induced epitope retrieval if needed

  • Test different pH conditions for maximum epitope exposure

• Controls:

  • Primary antibody omission controls

  • Preimmune serum controls

  • Peptide competition assays to demonstrate specificity

  • Comparison with subcellular markers for colocalization studies

• Detection method selection:

  • Fluorescent secondary antibodies for co-localization studies

  • Enzymatic detection (HRP/DAB) for permanent preparations

Research shows that 22 of 38 high-quality Arabidopsis root protein antibodies were of immunocytochemistry grade, suggesting careful validation is essential .

How can At5g42100 antibodies contribute to subcellular localization and protein interaction studies?

At5g42100 antibodies can be leveraged for advanced protein localization studies through:

• Co-immunoprecipitation (Co-IP) to identify protein interaction partners:

  • Use At5g42100 antibodies to pull down the target protein

  • Identify binding partners through mass spectrometry

  • Validate interactions with reciprocal Co-IP experiments

• Subcellular fractionation combined with immunoblotting:

  • Compare At5g42100 distribution across cellular compartments

  • Use alongside established subcellular markers (e.g., BiP for ER, γ-cop for Golgi, PM-ATPase for plasma membrane)

• Super-resolution microscopy:

  • Combine with fluorescently labeled secondary antibodies

  • Achieve nanometer-scale resolution of protein localization

  • Co-localize with organelle markers for precise compartmentalization

These approaches have been successfully implemented for other Arabidopsis root proteins and can be adapted for At5g42100 studies .

What are emerging techniques for enhancing antibody performance through engineering?

Recent advances in antibody engineering applicable to At5g42100 research include:

• Bispecific antibody development:

  • Creation of antibodies with dual binding specificity

  • Potential for simultaneous detection of At5g42100 and interaction partners

  • Strategies include DutaFab approach (spatially segregating CDRs) and tetra-VH IgGs

• Fc region engineering:

  • Modifications to enhance binding to protein A for improved purification

  • Alterations to reduce non-specific binding in plant tissues

  • Glycoengineering to increase specificity

• Machine learning for antibody-antigen binding prediction:

  • Library-on-library approaches to identify specific interacting pairs

  • Active learning strategies to improve experimental efficiency

  • Reduction in required antigen mutant variants by up to 35%

These technologies could significantly improve the specificity and utility of At5g42100 antibodies in future research applications.

How can researchers address common technical challenges with At5g42100 antibodies?

When working with At5g42100 antibodies, researchers frequently encounter these challenges:

• Cross-reactivity with related glucanases:

  • Perform specificity tests using recombinant related proteins

  • Consider using knockout/knockdown lines as negative controls

  • Employ peptide competition assays to confirm specificity

• Variable performance between antibody lots:

  • Characterize each new lot against reference standards

  • Maintain positive control samples from previous experiments

  • Document optimal working conditions for each lot

• Tissue-specific expression levels:

  • Adjust protein loading based on known expression patterns

  • Consider tissue-specific extraction protocols

  • Use enrichment techniques for tissues with lower expression

• Post-translational modifications affecting epitope recognition:

  • Test multiple antibodies targeting different regions

  • Consider treatment with deglycosylation enzymes if glycosylation interferes

  • Compare native vs. denatured detection conditions

How might computational approaches improve At5g42100 antibody development?

Emerging computational methods promise to enhance antibody development through:

• In silico epitope prediction:

  • More accurate identification of antigenic regions

  • Reduction in potential cross-reactivity

  • Improved developability profiles

• Active learning strategies for antibody-antigen binding:

  • Reduction in required experimental data points

  • Accelerated learning processes by up to 28 steps compared to random approaches

  • Better prediction of out-of-distribution interactions

• Developability profile screening:

  • Early assessment of expression yields

  • Biophysical stability prediction

  • Solubility and aggregation risk evaluation

These computational approaches could significantly reduce the time and resources needed for developing high-quality At5g42100 antibodies while improving their performance characteristics.

What are the implications of At5g42100 research for broader plant science?

Understanding At5g42100 through antibody-based research contributes to several important plant science domains:

• Plant immunity and pathogen response:

  • Role of glucanases in recognizing fungal cell wall components

  • Signaling cascades triggered by glucan recognition

  • Potential applications in crop protection

• Cell wall dynamics during development:

  • Contribution to cell expansion and differentiation

  • Role in specialized cell types (e.g., pollen tubes, root hairs)

  • Interaction with other cell wall-modifying enzymes

• Abiotic stress responses:

  • Potential functions during drought, salt, or temperature stress

  • Hormonal regulation of glucanase expression

  • Biotechnological applications for stress tolerance

Further antibody-based research can clarify these roles and potentially lead to agricultural applications.