AlphaFold 3 Protein Stability & Misfolding Predictor
This protocol analyzes protein stability and aggregation propensity using AlphaFold 3 predictions combined with sequence-based aggregation predictors. The workflow identifies unstable regions, predicts aggregation-prone sequences, and analyzes mutation effects on stability, supporting research on proteinopathies including Alzheimer's, Parkinson's, and ALS.
AlphaFold 3 Protein Stability & Misfolding Predictor
Abstract
This protocol analyzes protein stability and aggregation propensity using AlphaFold 3 predictions combined with aggregation predictors.
Motivation
Protein aggregation underlies numerous diseases:
- Amyloid diseases: Alzheimer's (Aβ, tau), Parkinson's (α-syn)
- Serpinopathies: Alpha-1 antitrypsin deficiency
- Channelopathies: CFTR misfolding
Current analysis methods are low-throughput or lack structural context. Our protocol integrates:
- Structural stability assessment
- Aggregation-prone region identification
- Mutation impact analysis
Methodology
Stability Assessment
From AlphaFold 3 predictions:
- pLDDT profile: Low-confidence = potentially disordered
- Domain analysis: Stable vs flexible domains
- Interface analysis: For oligomeric proteins
Aggregation Prediction
| Predictor | Method | Strength |
|---|---|---|
| TANGO | β-aggregation nucleation | Amyloid prediction |
| ZipperDB | Hexapeptide energy | Amyloid segments |
| Waltz | Local sequence patterns | Amyloid-specific |
Expected Outcomes
- Structured proteins: Clear domain boundaries
- Disordered proteins: Low pLDDT throughout
- Aggregation-prone regions: Hydrophobic stretches identified
Limitations
- Does not model aggregation oligomers
- Does not predict folding kinetics
- Aggregation is kinetic - cannot predict onset time
References
- Abramson et al., Nature, 2024
- Fernandez-Escamilla et al., Nat Biotech, 2004
- Chiti & Dobson, Annu Rev Biochem, 2017
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: alphafold3-protein-stability-protocol description: Analyze protein stability and aggregation propensity for disease-related misfolding proteins. allowed-tools: WebFetch, Bash(python *), Bash(mkdir *), Bash(cp *), Bash(ls *), Bash(jq *), Bash(cd *) --- # AlphaFold 3 Protein Stability & Misfolding Predictor Protocol ## Purpose Analyze protein stability and predict aggregation or misfolding propensity for disease-related proteins. ## Inputs - `inputs/wildtype.json`: AlphaFold 3 JSON for the protein. - `inputs/mutations.tsv` (optional): Known pathogenic mutations. - `inputs/disease_context.md`: Disease name, known pathology. - `inputs/metadata.md`: Protein name, UniProt ID, known instability. ## Pre-Run Checks 1. Confirm research use is permitted. 2. Validate protein sequence uses standard amino acid codes. 3. Verify disease context is documented. 4. Note that some proteins are intrinsically disordered. ## Step 1: Wild-Type Structure Prediction Run AlphaFold 3 prediction for the wild-type protein. ## Step 2: Analyze Structural Stability Extract confidence and stability metrics from pLDDT profile. ## Step 3: Aggregation Propensity Analysis Calculate aggregation scores using sequence-based predictors (TANGO, ZipperDB, Waltz). ## Step 4: Mutation Impact Analysis For each mutation, assess effect on aggregation propensity. ## Step 5: Generate Stability Report Document stability assessment and aggregation predictions. ## Success Criteria - Wild-type structure is predicted and interpreted. - Aggregation-prone regions are identified. - Mutations are analyzed for stability impact. ## Failure Modes - Entire protein is disordered → may be expected for some proteins - No predicted aggregation regions → may be false negative ## References - AlphaFold 3: Abramson et al., Nature, 2024
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