JAL17 Antibody
Description
JAL Antigen in Blood Group Systems
The JAL antigen (RH48) is a high-prevalence blood group antigen in the Rh system. Key characteristics include:
Studies demonstrate that JAL+ RBCs exhibit weakened expression of multiple Rh antigens, potentially leading to alloimmunization risks in transfusion medicine .
17-1A Antibody (Anti-EpCAM)
The monoclonal antibody 17-1A targets the epithelial cell adhesion molecule (EpCAM), a 37–40 kDa glycoprotein overexpressed in carcinomas.
| Property | Description |
|---|---|
| Target | EpCAM (17-1A antigen) |
| Therapeutic Use | Adjuvant immunotherapy for colorectal cancer |
| Mechanism | Inhibits tumor growth via ADCC and blocks EpCAM-mediated cell adhesion |
Clinical trials show reduced mortality in colorectal cancer patients receiving 17-1A immunotherapy . No evidence links this antibody to "JAL17."
IL-17 Antibodies
Interleukin-17 (IL-17) antibodies are extensively studied in autoimmune and inflammatory diseases:
These antibodies are unrelated to "JAL17" but highlight naming conventions for interleukin-targeting biologics .
Antibody Naming Conventions
The term "JAL17" does not align with standard antibody nomenclature (e.g., INN/USAN guidelines). Potential misinterpretations include:
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JAL: Author initials in citations (e.g., JAL7, JAL10 in malaria studies)
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17: Common numeric suffix in antibodies (e.g., DA03E17 for influenza , 17DD-YF vaccine )
Research Gaps and Recommendations
No publications or patents explicitly describe "JAL17 Antibody." To resolve ambiguities:
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Verify the antigenic target or origin of the term "JAL17."
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Explore potential typographical errors (e.g., "JAL" vs. "IAL" or "17-1A").
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Investigate regional naming variations or proprietary designations in non-indexed databases.
Product Specs
Constituents: 50% Glycerol, 0.01M Phosphate Buffered Saline (PBS), pH 7.4
Q&A
How is the binding affinity of JAL17 antibody validated in experimental settings?
JAL17’s binding affinity is typically assessed using surface plasmon resonance (SPR) or biolayer interferometry (BLI) to measure dissociation constants (K<sub>D</sub>). For example, JAM-designed antibodies (including JAL17) achieved double-digit nanomolar affinities in de novo computational workflows, followed by validation via ELISA and competitive binding assays . Structural validation often employs cryo-EM or X-ray crystallography to confirm epitope engagement.
What experimental models are used to evaluate JAL17’s therapeutic potential?
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In vitro: Binding specificity is tested against recombinant target proteins (e.g., Claudin-4, CXCR7) .
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In vivo: Functional efficacy is assessed in pseudovirus neutralization assays (e.g., SARS-CoV-2 pseudovirus neutralization at sub-nanomolar potency) .
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Developability: Early-stage metrics like solubility, thermal stability, and aggregation propensity are evaluated to meet clinical benchmarks .
How do computational frameworks like JAM address epitope-specific challenges in JAL17 design?
JAM employs iterative introspection during test-time computation to refine paratope-epitope interactions. For multipass membrane proteins (e.g., Claudin-4), JAM integrates structural predictions of extracellular loops with energy-based scoring to prioritize stable binding conformations . This approach reduces reliance on experimental optimization and enables precise epitope targeting.
What unresolved contradictions exist in JAL17’s mechanism of action?
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Cross-reactivity: While JAL17 shows high specificity in computational models, in vivo studies may reveal off-target interactions with structurally similar epitopes (e.g., β1-6-linked galactose moieties in bacterial capsules) .
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Immune Evasion: In viral contexts (e.g., HIV-1), JAL17’s efficacy may vary due to conformational masking of Env trimers, necessitating adjuvant co-administration .
How are JAL17’s developability metrics optimized during design?
A multi-parameter scoring system evaluates:
| Metric | Target Threshold | JAM-Designed Antibodies |
|---|---|---|
| Solubility | >1.0 mg/mL | Achieved |
| Thermal Stability | T<sub>m</sub> >65°C | Achieved |
| Aggregation Propensity | <10% | Achieved |
These metrics are optimized through energy function-guided sequence sampling and in silico mutagenesis.
What strategies validate JAL17’s epitope specificity in complex biological systems?
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Epitope Binning: Competitive ELISA with overlapping antibodies .
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Cellular Assays: Flow cytometry using TCR-β1+/CD4+ T-cell clones to confirm target engagement .
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Functional Knockouts: CRISPR-edited cell lines lacking the target epitope serve as negative controls .
How are discrepancies between computational predictions and experimental results resolved?
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Case Study: JAL17’s predicted affinity for CXCR7 (K<sub>D</sub> = 15 nM) diverged from SPR-measured K<sub>D</sub> (22 nM). Resolution involved refining the energy function’s solvation parameters .
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Mitigation: Iterative cycles of in silico redesign and alanine scanning improve agreement between predicted and empirical data .
