Fra e 1.0101

What is Fra e 1.0101 and what are its fundamental properties?

Fra e 1.0101 is a recombinant allergen derived from Fraxinus excelsior (European ash tree) that binds IgE-type human antibodies. It belongs to the Ole e 1-like protein family of allergens. The protein has a molecular weight of 18 kDa and an isoelectric point of pH 6.91. Its molar extinction coefficient is 14815, with an A280 (1 mg/mL) value of 0.833 . The protein is expressed using recombinant baculovirus (Autographa californica multiple nuclear polyhedrosis virus; AcMNPV) infection of Spodoptera frugiperda Sf9 insect cells, with the full-length cDNA fused to a deca-histidine purification tag . Fra e 1.0101 shares 86.9% sequence identity with Ole e 1, and both display similar degradation patterns during endolysosomal processing .

How does Fra e 1.0101 structurally compare to other members of the Ole e 1-like protein family?

Fra e 1.0101 shares the conserved β-barrel fold structure characteristic of the Ole e 1-like protein family, which is stabilized by three disulfide bridges. Homology modeling studies have demonstrated significant structural similarity between Fra e 1 and other Ole e 1-like proteins such as Ole e 1, Sal k 5, Che a 1, Phl p 11, and Pla l 1 . Molecular dynamics simulations have identified highly flexible regions within the molecule, primarily localized on random coils . Despite the high sequence identity (86.9%) between Fra e 1 and Ole e 1, there are some minor structural differences, particularly in the first third of the protein. One notable difference is at position 91, where Fra e 1 contains a negatively-charged aspartic acid residue compared to the polar, neutral asparagine in Ole e 1 .

What methodologies are recommended for recombinant production of Fra e 1.0101?

For optimal recombinant production of Fra e 1.0101, researchers should consider the following methodological approach:

• Expression System: Utilize the baculovirus expression system with Spodoptera frugiperda Sf9 insect cells, which has been shown to effectively produce functionally active Fra e 1.0101 .

• Construct Design: Design a full-length cDNA construct coding for Fra e 1.0101 fused to a deca-histidine purification tag to facilitate downstream purification .

• Purification Strategy: Implement affinity chromatography using the His-tag, followed by size exclusion chromatography to achieve high purity.

• Quality Control: Verify protein purity through SDS-PAGE (aim for >80% purity) and confirm identity via Western blotting with patient samples .

• Functional Assessment: Validate immunological function through immunodot analyses with positive/negative samples to confirm IgE-binding capacity .

• Storage Optimization: Store the purified protein in a buffer with neutral to slightly alkaline pH containing 20% glycerol as a cryoprotective agent at -70°C or below, avoiding repeated freeze/thaw cycles .

How does the endolysosomal degradation of Fra e 1.0101 compare to other Ole e 1-like proteins?

The endolysosomal degradation of Fra e 1.0101 follows patterns similar to other Ole e 1-like proteins, particularly Ole e 1, with which it shares 86.9% sequence identity. Comparative studies using microsomal fractions of JAWS II dendritic cells have revealed seven identical cleavage sites between Fra e 1 and Ole e 1, co-localizing in the sequence and aligning well with the secondary structure .

When compared to other Ole e 1-like proteins such as Sal k 5, Che a 1, Phl p 11, and Pla l 1, Fra e 1 shows conserved cleavage sites around residues 100 and 120, which are located in one of the most accessible flexible loops of the protein . Interestingly, all tested allergens in this family share these conserved cleavage sites despite sequence divergence, suggesting that the three-dimensional structure plays a crucial role in determining proteolytic accessibility .

What structural elements influence the immunogenic properties of Fra e 1.0101?

The immunogenic properties of Fra e 1.0101 are influenced by several structural elements:

Understanding these structural elements is essential for predicting and potentially modifying the allergenicity of Fra e 1.0101 in research and therapeutic contexts.

How can computational approaches be utilized to study Fra e 1.0101 allergenicity?

Computational approaches offer powerful tools for studying Fra e 1.0101 allergenicity:

• Homology Modeling: As demonstrated in research with Ole e 1-like proteins, homology modeling can be employed to predict the three-dimensional structure of Fra e 1.0101 based on known structures of related proteins. Two complementary software analyses (Swiss Model server and MOE) have successfully generated reliable homology models of Fra e 1.0101 despite varying sequence identities with template structures .

• Molecular Dynamics Simulations: MD simulations using software like NWChem can identify highly flexible regions in Fra e 1.0101, which often correlate with proteolytically accessible sites and potential epitopes. These simulations provide dynamic information that static structural models cannot reveal .

• In Silico MHC Class II Binding Prediction: Tools such as ProPred can be used to predict potential T cell epitopes within Fra e 1.0101 by analyzing peptide binding to MHC class II molecules. This approach helps identify immunologically relevant regions of the protein .

• Docking Experiments: In silico docking between Fra e 1.0101 and proteases such as cathepsin S can predict preferred cleavage sites, as demonstrated for Ole e 1. These predictions can be validated through experimental methods like mass spectrometry .

• Sequence and Structure Alignment: Comparing the sequence and structure of Fra e 1.0101 with other allergenic and non-allergenic proteins can identify conserved motifs associated with allergenicity.

• Machine Learning Algorithms: Advanced machine learning approaches can integrate multiple parameters (sequence, structure, physicochemical properties) to predict the allergenic potential of Fra e 1.0101 variants or modified versions.

What experimental design is optimal for studying the endolysosomal degradation of Fra e 1.0101?

An optimal experimental design for studying the endolysosomal degradation of Fra e 1.0101 should include:

This comprehensive experimental design allows for detailed characterization of Fra e 1.0101 degradation patterns, identification of key cleavage sites, and comparison with other allergens in the Ole e 1-like protein family.

How should researchers approach structural studies of Fra e 1.0101?

Researchers should implement a multi-technique approach for comprehensive structural characterization of Fra e 1.0101:

• Homology Modeling:

  • Utilize multiple modeling software (e.g., Swiss Model server and MOE) for comprehensive analysis .

  • Use the highest resolution structures of Ole e 1-like proteins as templates.

  • Validate models through energy minimization and Ramachandran plot analysis.

• X-ray Crystallography:

  • Optimize crystallization conditions based on physicochemical properties (pI 6.91, molecular weight 18 kDa) .

  • Consider using the purified protein with intact His-tag or after tag removal.

  • Use hanging drop or sitting drop vapor diffusion methods with systematic screening of conditions.

  • For data collection and refinement, follow approaches similar to those used for Act c 10.0101 and Pun g 1.0101, which share structural similarity with Ole e 1-like proteins .

• NMR Spectroscopy:

  • Prepare isotopically labeled Fra e 1.0101 (15N, 13C) for multidimensional NMR studies.

  • Perform experiments at the protein's optimal pH stability range (neutral to slightly alkaline) .

  • Use HSQC, NOESY, and TOCSY experiments to determine solution structure.

• Molecular Dynamics Simulations:

  • Conduct simulations using NWChem or similar software to identify flexible regions .

  • Compare dynamics of Fra e 1.0101 with Ole e 1 to identify functional differences.

  • Analyze the impact of the Asn/Asp difference at position 91 on local structure.

• Circular Dichroism (CD) Spectroscopy:

  • Characterize secondary structure elements (β-sheets, random coils).

  • Perform thermal denaturation studies to assess structural stability.

  • Compare CD spectra with other Ole e 1-like proteins to identify structural differences.

• Small-Angle X-ray Scattering (SAXS):

  • Obtain low-resolution structural information in solution.

  • Validate homology models against experimental SAXS data.

• Comparative Analysis:

  • Create a comprehensive structural comparison between Fra e 1.0101 and other Ole e 1-like proteins.

  • Focus on the β-barrel core structure and flexible loop regions.

  • Correlate structural features with endolysosomal degradation patterns.

This multi-technique approach provides complementary structural information, enabling a comprehensive understanding of Fra e 1.0101's three-dimensional structure and its relationship to function and immunogenicity.

What analytical methods are recommended for characterizing Fra e 1.0101 interactions with IgE antibodies?

For characterizing Fra e 1.0101 interactions with IgE antibodies, researchers should employ the following analytical methods:

• Immunodot Analysis:

  • Utilize immunodot analyses with positive and negative patient samples to screen for IgE reactivity .

  • Optimize protein concentrations and blocking conditions to minimize background.

  • Include appropriate positive (known allergens) and negative controls.

• Enzyme-Linked Immunosorbent Assay (ELISA):

  • Develop a quantitative ELISA to measure IgE binding to Fra e 1.0101.

  • Use sera from allergic patients and healthy controls.

  • Determine EC50 values to quantify binding affinity.

  • Consider competitive ELISA to compare binding with other Ole e 1-like proteins.

• Western Blotting:

  • Perform Western blot analysis with patient samples to confirm specific IgE binding .

  • Include reducing and non-reducing conditions to assess the importance of disulfide bridges.

  • Use both native and denatured Fra e 1.0101 to distinguish conformational from linear epitopes.

• Surface Plasmon Resonance (SPR):

  • Immobilize Fra e 1.0101 on a sensor chip and measure real-time binding kinetics with purified IgE or patient sera.

  • Determine association (kon) and dissociation (koff) rate constants.

  • Calculate equilibrium dissociation constants (KD) for quantitative comparison between different IgE sources.

• Basophil Activation Test (BAT):

  • Assess the biological activity of Fra e 1.0101 by measuring its ability to crosslink IgE on basophils.

  • Quantify activation by measuring upregulation of CD63 or CD203c via flow cytometry.

  • Compare results with skin prick test data if available.

• Epitope Mapping:

  • Utilize peptide arrays of overlapping Fra e 1.0101 fragments to identify linear IgE epitopes.

  • Perform site-directed mutagenesis to confirm key residues involved in IgE binding.

  • Compare epitope profiles with other Ole e 1-like proteins, particularly Ole e 1.

• Inhibition Assays:

  • Conduct cross-inhibition experiments between Fra e 1.0101 and other Ole e 1-like proteins.

  • Quantify degree of cross-reactivity between different allergens.

  • Identify unique and shared epitopes among family members.

These complementary approaches provide a comprehensive characterization of Fra e 1.0101-IgE interactions, crucial for understanding the molecular basis of allergic responses to this allergen.

How should mass spectrometry data for Fra e 1.0101 peptide mapping be analyzed?

Mass spectrometry data for Fra e 1.0101 peptide mapping requires systematic analysis following these recommended steps:

• Raw Data Processing:

  • Process raw MS data using appropriate software (e.g., PEAKS, Mascot, MaxQuant).

  • Apply quality filters for peak intensity, signal-to-noise ratio, and mass accuracy.

  • Perform charge state deconvolution for multiply charged ions.

• Peptide Identification:

  • Search spectra against a protein database containing Fra e 1.0101 sequence.

  • Consider common post-translational modifications (oxidation, deamidation).

  • Set appropriate mass tolerance parameters based on instrument specifications.

  • Apply false discovery rate (FDR) control (typically 1% at peptide level).

• Degradation Pattern Analysis:

  • Identify peptide clusters generated during endolysosomal degradation .

  • Map peptides to the Fra e 1.0101 sequence and structure.

  • Determine N- and C-terminal cleavage sites for each peptide.

  • Create a time-course visualization of peptide generation.

• Cleavage Site Assignment:

  • Assign identified cleavage sites to the primary and secondary structure of Fra e 1.0101 .

  • Compare with Ole e 1 cleavage sites given their 86.9% sequence identity.

  • Identify conserved and unique cleavage patterns.

  • Correlate cleavage sites with structural features (loops, β-sheets).

• Comparative Analysis:

  • Compare peptide profiles with other Ole e 1-like proteins (Ole e 1, Sal k 5, Che a 1, Phl p 11, Pla l 1) .

  • Analyze similarities and differences in degradation patterns.

  • Focus on regions showing differential processing (e.g., position 91).

• T Cell Epitope Correlation:

  • Compare identified peptides with predicted and known T cell epitopes.

  • Assess if peptide clusters encompass previously reported T cell epitopes of Ole e 1 .

  • Correlate with in silico MHC class II binding prediction results.

• Visualization and Presentation:

  • Create heat maps or sequence coverage maps showing the intensity of peptide generation over time.

  • Visualize cleavage sites on the 3D structural model of Fra e 1.0101.

  • Develop comparative visualizations showing processing similarities/differences with other Ole e 1-like proteins.

• Statistical Analysis:

  • Apply statistical methods to identify significantly different processing patterns.

  • Use clustering analysis to group peptides based on generation kinetics.

  • Implement normalization strategies when comparing data from different experiments.

This comprehensive analytical approach enables researchers to gain detailed insights into the endolysosomal processing of Fra e 1.0101, providing valuable information about its immunogenicity and potential T cell epitopes.

What are the key considerations when comparing Fra e 1.0101 with other Ole e 1-like proteins?

When comparing Fra e 1.0101 with other Ole e 1-like proteins, researchers should consider several key factors:

• Sequence Similarity Analysis:

  • Calculate pairwise sequence identities (e.g., 86.9% with Ole e 1, lower with others) .

  • Perform multiple sequence alignment to identify conserved and variable regions.

  • Analyze conservation of cysteine residues that form disulfide bridges.

  • Create a table summarizing sequence identity percentages between all Ole e 1-like proteins:

• Structural Comparison:

  • Compare secondary and tertiary structures using homology models .

  • Analyze the conservation of the β-barrel core structure.

  • Evaluate differences in flexible loop regions, particularly around residues 90-99 where Phl p 11 shows a missing hairpin loop .

  • Examine the impact of amino acid substitutions on local structure (e.g., Asn vs. Asp at position 91 in Ole e 1 vs. Fra e 1) .

• Endolysosomal Degradation Patterns:

  • Compare cleavage sites and their locations within secondary structure elements .

  • Identify common degradation patterns (e.g., cleavages around residues 100 and 120) .

  • Analyze protein-specific degradation features (e.g., Fra e 1 and Ole e 1 have seven identical cleavage sites) .

  • Evaluate degradation kinetics and rate differences.

• Glycosylation Analysis:

  • Determine the presence and location of N-glycosylation sites.

  • Assess the impact of glycosylation on endolysosomal processing and IgE binding.

  • Note that Fra e 1 is N-glycosylated at one site, which affects mass spectrometry analysis of corresponding glycopeptides .

• Immunological Cross-reactivity:

  • Evaluate IgE cross-reactivity between Fra e 1.0101 and other Ole e 1-like proteins.

  • Identify shared and unique epitopes.

  • Correlate cross-reactivity with structural and sequence similarities.

• T Cell Epitope Comparison:

  • Compare T cell epitope regions identified through endolysosomal degradation studies .

  • Assess conservation of epitope sequences across family members.

  • Evaluate MHC class II binding predictions for epitope regions.

• Botanical Family Relationships:

  • Group proteins by botanical family (e.g., Oleaceae for Fra e 1 and Ole e 1, Amaranthaceae for Sal k 5 and Che a 1) .

  • Analyze whether degradation patterns correlate with botanical classification.

  • Consider evolutionary relationships between allergen sources.

This comprehensive comparative analysis provides valuable insights into the structural, functional, and immunological relationships between Fra e 1.0101 and other members of the Ole e 1-like protein family, contributing to a better understanding of cross-reactivity patterns and allergen-specific properties.

What optimal experimental design approaches should be used to study Fra e 1.0101?

When designing experiments to study Fra e 1.0101, researchers should implement optimal experimental design principles to maximize information gain while minimizing experimental runs. The following approaches are recommended:

• D-Optimal Design for Protein Production Optimization:

  • Implement D-optimal design to optimize recombinant Fra e 1.0101 expression conditions .

  • Select factors affecting expression (temperature, induction time, cell density, media composition).

  • Use D-optimality criteria to minimize the variance of parameter estimates .

  • Develop a response surface model to identify optimal production conditions.

  • This approach reduces experimental runs compared to traditional factorial designs while maintaining statistical power .

• Sequential Experimental Design for Stability Studies:

  • Apply sequential analysis techniques for Fra e 1.0101 stability studies .

  • Start with initial screening experiments to identify significant factors.

  • Follow with focused experiments in regions of interest, adjusting experimental conditions based on interim results.

  • This adaptive approach allows efficient exploration of stability conditions (pH, temperature, buffer composition) .

• Response Surface Methodology for Functional Characterization:

  • Implement response surface methodology to characterize Fra e 1.0101 functionality under varying conditions .

  • Develop mathematical models relating IgE binding to experimental factors.

  • Generate contour plots to visualize optimal conditions for protein functionality.

  • This approach enables identification of critical parameters affecting allergenicity .

• Blocking Design for Comparative Studies:

  • Use blocking designs when comparing Fra e 1.0101 with other Ole e 1-like proteins .

  • Group experiments into homogeneous blocks to control for experimental variation.

  • This approach increases precision when comparing degradation patterns or immunological properties .

• Variance Component Analysis for Method Validation:

  • Implement nested designs to estimate sources of variability in Fra e 1.0101 analysis methods.

  • Quantify variation from different sources (batch-to-batch, day-to-day, analyst-to-analyst).

  • Use results to establish robust validation protocols for analytical methods.

How should researchers design studies to investigate Fra e 1.0101 epitopes?

A comprehensive approach to investigate Fra e 1.0101 epitopes should include:

• Integrated Computational-Experimental Design:

  • Begin with in silico prediction of potential B and T cell epitopes.

  • For T cell epitopes, utilize ProPred or similar tools for MHC class II binding prediction .

  • Design experiments to validate computational predictions through complementary methods.

  • This approach reduces experimental costs by focusing on high-probability epitope regions.

• Endolysosomal Degradation Assay Design:

  • Utilize dendritic cell line-derived microsomal fractions to simulate antigen processing .

  • Implement time-course sampling to track peptide generation kinetics.

  • Apply mass spectrometry to identify peptide profiles, focusing on clusters that may represent T cell epitopes .

  • Compare results with Ole e 1 processing data given their high sequence similarity (86.9%) .

• Peptide Microarray for B Cell Epitope Mapping:

  • Design overlapping peptide arrays covering the entire Fra e 1.0101 sequence.

  • Use 15-20 amino acid peptides with 5-amino acid overlaps.

  • Test arrays against sera from ash pollen allergic patients.

  • Include peptides with systematic amino acid substitutions to identify critical binding residues.

• Site-Directed Mutagenesis Experimental Design:

  • Create a panel of Fra e 1.0101 mutants targeting predicted epitope regions.

  • Focus on positions where Fra e 1 differs from Ole e 1 (e.g., position 91 with Asp vs. Asn) .

  • Evaluate IgE binding of mutants compared to wild-type protein.

  • This approach helps identify residues critical for antibody recognition.

• Cross-Inhibition Study Design:

  • Implement a matrix design for inhibition studies between Fra e 1.0101 and other Ole e 1-like proteins.

  • Pre-incubate patient sera with varying concentrations of potential inhibitors.

  • Measure residual IgE binding to immobilized Fra e 1.0101.

  • This design reveals shared epitopes between allergens.

• T Cell Assay Design:

  • Isolate peripheral blood mononuclear cells (PBMCs) from allergic and non-allergic individuals.

  • Stimulate with Fra e 1.0101-derived peptides identified from degradation studies .

  • Measure T cell proliferation and cytokine production.

  • Include peptides from regions showing differential processing compared to Ole e 1.

The combination of these complementary approaches provides a comprehensive characterization of both B and T cell epitopes in Fra e 1.0101, enabling better understanding of its allergenicity and potential development of immunotherapeutic strategies.

What are the major challenges in Fra e 1.0101 research and how can they be addressed?

Researchers working with Fra e 1.0101 face several significant challenges:

• Protein Production and Stability:

  • Challenge: Maintaining consistent batch-to-batch quality of recombinant Fra e 1.0101 with proper folding and post-translational modifications .

  • Solution: Implement rigorous quality control protocols including SDS-PAGE, Western blotting, mass spectrometry, and functional assays. Store purified protein at -70°C or below with 20% glycerol as a cryoprotective agent and avoid repeated freeze/thaw cycles .

• Structural Analysis Limitations:

  • Challenge: Obtaining high-resolution structural data due to potential flexibility in loop regions.

  • Solution: Combine multiple structural techniques (X-ray crystallography, NMR, SAXS) with computational approaches. Consider protein engineering to stabilize flexible regions for crystallization .

• Epitope Characterization Complexity:

  • Challenge: Distinguishing between linear and conformational epitopes, particularly given the importance of the protein's 3D structure.

  • Solution: Implement integrated approaches combining peptide arrays, site-directed mutagenesis, and structural analysis. Use native and denatured protein in parallel to differentiate epitope types.

• Cross-Reactivity Analysis:

  • Challenge: Understanding the molecular basis of cross-reactivity between Fra e 1.0101 and other Ole e 1-like proteins.

  • Solution: Perform systematic cross-inhibition studies. Create chimeric proteins exchanging regions between Fra e 1.0101 and Ole e 1 to map cross-reactive epitopes .

• Clinical Relevance Assessment:

  • Challenge: Correlating molecular findings with clinical symptoms and severity.

  • Solution: Integrate molecular data with clinical information from well-characterized patient cohorts. Perform basophil activation tests to link structural features with functional outcomes.

• Data Integration and Interpretation:

  • Challenge: Integrating diverse datasets from structural, immunological, and degradation studies.

  • Solution: Develop computational frameworks for integrative analysis. Apply machine learning approaches to identify patterns across multiple data types .

What emerging methodologies show promise for advancing Fra e 1.0101 research?

Several emerging methodologies show significant promise for advancing Fra e 1.0101 research:

• Cryo-Electron Microscopy (Cryo-EM):

  • High-resolution structural analysis without crystallization.

  • Potential to visualize Fra e 1.0101 in complex with antibodies or receptors.

  • May reveal conformational epitopes not accessible through other methods.

• Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS):

  • Probes protein dynamics and solvent accessibility.

  • Can identify epitope regions through differential exchange patterns when bound to antibodies.

  • Provides complementary data to static structural techniques.

• Single-Cell Analysis Technologies:

  • Characterizes individual cellular responses to Fra e 1.0101.

  • Reveals heterogeneity in T and B cell responses.

  • Links genetic factors with immune response patterns.

• Artificial Intelligence for Epitope Prediction:

  • Deep learning approaches for more accurate epitope prediction.

  • Integration of sequence, structure, and experimental data.

  • Potentially identifies novel epitopes missed by traditional methods.

• Humanized Animal Models:

  • Development of mouse models expressing human MHC and T cell receptors.

  • More relevant systems for studying Fra e 1.0101 immunogenicity.

  • Bridges gap between in vitro studies and human responses.

• CRISPR-Based Protein Engineering:

  • Precise modification of Fra e 1.0101 for structure-function studies.

  • Creation of hypoallergenic variants with preserved T cell epitopes.

  • Development of novel research tools and potential therapeutic applications.

• Advanced Computational Simulations:

  • Molecular dynamics simulations at longer time scales.

  • Simulations of protein-protein interactions between Fra e 1.0101 and IgE.

  • In silico prediction of immunogenicity and cross-reactivity .

By embracing these emerging methodologies alongside established techniques, researchers can develop a more comprehensive understanding of Fra e 1.0101's structure, function, and immunological properties, potentially leading to improved diagnostic and therapeutic approaches for ash pollen allergy.