FOLD3 Antibody
Description
AlphaFold 3 in Antibody Design
AlphaFold 3, released in 2024, predicts antibody-antigen interactions with 50% greater accuracy than traditional methods. Key innovations include:
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Antibody-protein binding: Predicts binding affinities for therapeutic targets (e.g., SARS-CoV-2 spike protein) .
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CDR-H3 optimization: Generates CDR-H3 sequences with improved binding kinetics.
| Model | CDR-H3 AAR (%) | Binding Affinity (ΔΔG, kcal/mol) |
|---|---|---|
| ASSD (MLE) | 40.81 | -8.454 |
| ASSD (MLE + RL) | 40.53 | -8.162 |
| RefineGNN | 26.33 | -5.437 |
| Performance on the RAbD benchmark for antibody-antigen complexes . |
AlphaFold 3’s predictions enable rapid in silico screening of antibody candidates, reducing experimental timelines from years to days .
Case Study: SARS-CoV-2 Neutralizing Antibodies
VL6-57-class antibodies:
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Target a conserved epitope (residues S371-S373-S375) on SARS-CoV-2’s spike protein.
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Neutralization potency is reduced by Omicron mutations (e.g., S371L/F, S373P) .
| Antibody | Light Chain | IC₅₀ (Wildtype) | IC₅₀ (Omicron BA.1) |
|---|---|---|---|
| C121 | VL6-57 | 12 nM | >2,000 nM |
| C135 | VL6-57 | 70 nM | 8 nM |
| Data from . |
Therapeutic antibodies:
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S309: Cross-neutralizes SARS-CoV and SARS-CoV-2 by targeting a conserved glycan site .
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CB6: Blocks ACE2 binding with 2.49 nM affinity, validated in cryo-EM structures .
Clinical Implications of Antibody Engineering
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Vaccine efficacy: Heterologous mRNA boosting after inactivated vaccines enhances neutralizing antibody titres by 8–12-fold .
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Glycan modulation: N343 glycosylation in SARS-CoV-2 spike protein reduces antibody neutralization by 8–22-fold, highlighting escape mechanisms .
Challenges and Future Directions
Product Specs
Constituents: 50% Glycerol, 0.01M PBS, pH 7.4
