DALL2 Antibody
Description
Functional Insights from TAA Studies
TAAs like SadA (Salmonella), EhaG (EHEC), and UpaG (UPEC) rely on DALL2 domains to:
-
Project adhesive heads away from the bacterial surface.
-
Mediate transitions between coiled-coil stalks and β-structured anchors.
Antibodies targeting DALL2 disrupt these functions, as evidenced by:
-
Reduced bacterial adhesion in Salmonella mutants lacking intact DALL2 domains.
-
Impaired immune evasion in UPEC strains with DALL2 mutations .
Research Tools
-
Structural Mapping: Anti-DALL2 antibodies localize TAAs in electron microscopy (e.g., anti-SadA antibodies in Salmonella) .
-
Functional Blockade: Neutralizing DALL2 antibodies inhibit TAA-mediated host cell binding in E. coli and Salmonella .
Therapeutic Potential
While no clinical trials target DALL2 directly, advances in AI-driven antibody design (e.g., AF2Complex, ImmuneBuilder) suggest pathways for engineering high-affinity DALL2 blockers . For example:
-
AI-Optimized Affinity: Stanford researchers improved antibody efficacy 25-fold against viral targets using structural data .
-
Cross-Reactivity Mitigation: Computational tools like MAbSilico predict off-target binding risks for DALL2 antibodies .
Challenges and Future Directions
-
Epitope Accessibility: The DALL2 domain’s buried location in TAAs complicates antibody binding .
-
Antigenic Variability: TAAs exhibit high genetic diversity across bacterial strains, necessitating broad-spectrum antibody designs .
-
AI Integration: Combining deep learning (e.g., AlphaFold2) with high-throughput screening could accelerate DALL2 antibody discovery .
Key Research Findings
Product Specs
Composition: 50% Glycerol, 0.01M PBS, pH 7.4
Target Background
This antibody targets an acylhydrolase enzyme. The enzyme catalyzes the hydrolysis of phosphatidylcholine at the sn-1 position. It exhibits moderate activity towards phosphatidylcholine (PC), monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), and triacylglycerol (TAG).
Q&A
Here’s a structured collection of FAQs for researchers working with DALL2 antibodies, optimized for academic research scenarios and based on scientific literature:
How is DALL2 identified and validated in structural studies?
DALL2 is identified through cryo-EM or X-ray crystallography to resolve its β-sheet architecture and interactions. Validation involves:
-
Structural alignment: Comparing resolved DALL2 domains (e.g., β-strand arrangements) with reference structures from databases like PDB .
-
Mutagenesis: Disrupting conserved residues (e.g., tryptophan or histidine) to assess impact on β-sheet stability .
-
Cross-reactivity assays: Testing against related domains (e.g., DALL1) to confirm specificity .
What protocols ensure reliable DALL2 antibody performance?
-
Storage: Lyophilized antibodies should be stored at -20°C with stabilizers (e.g., recombinant BSA) to prevent aggregation .
-
Working concentrations:
How do I distinguish DALL2 from homologous domains like DALL1?
-
Epitope mapping: Use peptide arrays or hydrogen-deuterium exchange mass spectrometry to identify binding regions unique to DALL2 .
-
Structural comparison: Analyze β-sheet spacing (DALL2 lacks the water-mediated insertion seen in DALL1) .
How to resolve contradictions between predicted and observed DALL2 epitopes?
-
Integrate AI predictions with experimental validation:
-
Statistical analysis: Compare computational confidence scores (e.g., pLDDT >90) with experimental resolution (<3.0 Å) .
What methods optimize DALL2 antibody affinity without germline bias?
-
Language model-guided mutagenesis:
-
Affinity maturation: Combine error-prone PCR and yeast display to screen for Kd <10 nM variants .
How to design experiments evaluating DALL2’s role in pathogenicity?
-
Functional assays:
-
Data interpretation: Use structural insights (e.g., β-sheet flexibility) to explain variable neutralization outcomes .
Methodological Considerations
-
Cross-reactivity mitigation: Pre-adsorb antibodies against DALL1 peptides to reduce off-target binding .
-
Data validation: Always pair computational predictions (e.g., AF2Complex) with orthogonal techniques like SPR or BLI .
-
Ethical reporting: Disclose unresolved contradictions (e.g., AI vs. structural data) in publications to guide future studies .
