FTB Antibody

FAQs for Researchers on FTB Antibody Research

Advanced Research Questions

• How do I resolve contradictions between computational models predicting FTB antibody fitness?

  • Use ensemble approaches: Compare predictions from Rosetta (physics-based) and deep learning models (e.g., AntiBERTy, IgLM) for consensus .

  • Validate discordant predictions experimentally via high-throughput mutagenesis screens (e.g., Koenig et al.’s 4275-mutation dataset) .

  Table 2: Model Performance Across Antibody Properties

  Model	Expression (R²)	Thermostability (R²)	Immunogenicity (R²)
  Rosetta	0.41	0.58	0.32
  AntiBERTy	0.56	0.49	0.61
  IgLM	0.52	0.53	0.59

• What strategies improve FTB antibody specificity in polyclonal environments?

  • Employ NGS-based clonal analysis (e.g., Geneious Biologics) to identify dominant lineages and filter nonspecific variants .

  • Use in silico paratope docking tools (e.g., ABodyBuilder) to predict cross-reactivity with homologous antigens .

Data & Contradiction Analysis

• How should I interpret conflicting transfer ratios of FTB antibodies in maternal-infant cohorts?

  • Stratify by gestational age: Preterm infants exhibit lower IgG transfer ratios (median: 1.2 vs. 1.7 in full-term) .

  • Control for maternal vaccination status, which elevates cord IgG by 30–50% for influenza targets .

• Why do in vitro neutralizing assays fail to correlate with in vivo efficacy for some FTB antibodies?

  • Incorporate glycan profiling: ABH antigen tagging on viral S proteins (e.g., SARS-CoV-2) alters neutralization sensitivity .

  • Use pseudovirus systems expressing host-specific glycans to better mimic physiological conditions .

Methodological Recommendations

• For NGS data: Cluster sequences by CDR3 homology (≥85% identity) and annotate using AIRR-C standards .

• For immunogenicity risk: Combine T-cell epitope mapping (e.g., NetMHCIIpan) with structural stability metrics .