AlphaFold 3 Antibody-Antigen Predictor: Structural Basis for Therapeutic Design
AlphaFold 3 Antibody-Antigen Predictor: Structural Basis for Therapeutic Design
Abstract
This protocol predicts antibody-antigen complex structures using AlphaFold 3, with specialized analysis of paratope-epitope interactions.
Motivation
Antibody therapeutics are revolutionizing medicine with >100 FDA approvals. Key challenges include:
- Identifying optimal epitope for neutralization
- Understanding species cross-reactivity
- Designing affinity-matured variants
Our protocol provides predicted binding modes, epitope mapping, and interface quality assessment.
Methodology
Antibody Representation
Antibodies are represented as separate VH and VL chains, or single VHH for nanobodies. CDR regions are identified by sequence position patterns.
Complex Prediction
Include all components (antibody + antigen) in a single AlphaFold 3 prediction.
Interface Analysis
| Metric | Significance |
|---|---|
| Interface pLDDT | Confidence in predicted interface |
| Contact count | Binding surface extent |
| Hydrogen bonds | Specificity determinants |
Expected Outcomes
For well-expressed antibodies: Interface pLDDT 75-90, contact count 30-80, interface area 1000-2000 Ų.
Limitations
- CDR flexibility not fully captured
- Does not predict kinetics (kon, koff)
- May miss conformational changes
References
- Abramson et al., AlphaFold 3, Nature, 2024
- Dechaumes et al., MAbs, 2024
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: alphafold3-antibody-antigen-protocol description: Predict antibody-antigen complex structures using AlphaFold 3, with specialized scoring for paratope-epitope interface quality. allowed-tools: WebFetch, Bash(python *), Bash(mkdir *), Bash(cp *), Bash(ls *), Bash(jq *), Bash(cd *) --- # AlphaFold 3 Antibody-Antigen Complex Predictor Protocol ## Purpose Predict the binding mode between an antibody and its antigen target using AlphaFold 3. ## Inputs - `inputs/antibody.fasta` or `inputs/antibody.json`: Antibody sequence(s). - `inputs/antigen.json`: AlphaFold 3 JSON for the antigen. - `inputs/metadata.md`: Antibody name, origin, desired affinity. ## Pre-Run Checks 1. Confirm research use is permitted. 2. Verify antibody sequences contain valid amino acid codes. 3. Check for signal peptides and clean to mature sequence. ## Step 1: Prepare Antibody Input Prepare as separate VH and VL chains in AlphaFold 3 JSON format. ## Step 2: Validate Individual Components Check CDR regions and verify framework conserved residues. ## Step 3: Run Complex Prediction Submit antibody + antigen to AlphaFold 3. ## Step 4: Extract Paratope-Epitope Metrics Identify CDR residues, epitope residues within 5Å, and calculate interface metrics. ## Step 5: Assess CDR Conformation Quality Check CDR loop conformations and length distributions. ## Success Criteria - Antibody and antigen are correctly formatted and predicted. - Interface metrics are extracted and quantified. - CDR conformations are assessed for quality. ## Failure Modes - Invalid antibody sequence → validate sequence first - Antigen prediction fails → improve antigen prediction before complex ## References - AlphaFold 3: Abramson et al., Nature, 2024
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