Mutation Impact Predictor for Analyzing Protein Sequence Variations
Predict the functional impact of protein mutations using sequence and structural features. Supports nsSNP analysis, pathogenicity scoring, and structural stability changes for variant interpretation.
AlphaFold 3 Mutation Impact Analyzer: Structural Pathogenicity Prediction
Abstract
This protocol compares wild-type and mutant AlphaFold 3 structures to quantify mutation impact. By calculating local RMSD and pLDDT changes, mutations are categorized to support pathogenicity assessment.
Motivation
Current mutation impact prediction relies on sequence conservation or ML without 3D context. Our structural approach provides:
- Direct visualization of disruption
- Mechanistic hypothesis generation
- Integration with AlphaFold 3 confidence
- Interpretable metrics
Methodology
Wild-Type Baseline
Predict the wild-type structure to establish baseline confidence and conformation.
Mutation Introduction
Systematically introduce each mutation and predict the mutant structure.
Structural Comparison
| Metric | Calculation | Interpretation |
|---|---|---|
| Overall RMSD | Cα alignment of full structures | Global destabilization |
| Local RMSD | ±10 residue window | Local disruption |
| pLDDT change | ΔpLDDT at mutation site | Confidence impact |
Impact Categorization
| Category | pLDDT Change | Local RMSD | Predicted Effect |
|---|---|---|---|
| Severe | < -10 | > 2.0 �� | Likely pathogenic |
| Moderate | -5 to -10 | 1.0-2.0 �� | Possibly pathogenic |
| Mild | -3 to -5 | 0.5-1.0 �� | Uncertain |
| Negligible | > -3 | < 0.5 �� | Likely benign |
Expected Outcomes
For 100 ClinVar variants: ~30% severe, ~20% moderate, ~25% mild, ~25% negligible.
Limitations
- Does not capture allosteric effects or folding kinetics
- Mutations in disordered regions hard to assess
- Conservative substitutions may have subtle effects
References
- Abramson et al., AlphaFold 3, Nature, 2024
- Richards et al., ClinVar, Hum Mut, 2018
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: alphafold3-mutation-impact-protocol description: Predict how point mutations affect protein structure by comparing wild-type and mutant AlphaFold 3 predictions. allowed-tools: WebFetch, Bash(python *), Bash(mkdir *), Bash(cp *), Bash(ls *), Bash(jq *), Bash(cd *) --- # AlphaFold 3 Mutation Impact Analyzer Protocol ## Purpose Assess the structural impact of point mutations by comparing AlphaFold 3 predictions of wild-type and mutant protein structures. ## Inputs - `inputs/wildtype.json`: AlphaFold 3 JSON for the wild-type protein. - `inputs/mutations.tsv`: Tab-separated file of mutations to analyze. - `inputs/metadata.md`: Protein name, function, known domains. ## Pre-Run Checks 1. Confirm research use is permitted. 2. Validate wild-type sequence uses standard amino acid codes. 3. Verify all mutations are valid (original residue matches sequence at position). ## Step 1: Wild-Type Prediction Run AlphaFold 3 prediction for the wild-type structure. ## Step 2: Generate Mutant Sequences For each mutation, replace the residue at the specified position. ## Step 3: Mutant Predictions Predict structures for all mutant variants. ## Step 4: Compare Structures Calculate overall RMSD, local RMSD around mutation site, and pLDDT difference. ## Step 5: Categorize Impact Classify as Severe, Moderate, Mild, or Negligible based on metrics. ## Success Criteria - Wild-type prediction completes successfully. - All mutations are correctly applied without sequence errors. - Comparison metrics are computed for each mutation. ## Failure Modes - Invalid mutation → skip, log error - Mutation at low-confidence region → note limitation - Prediction fails for mutant → retry or mark as failed ## References - AlphaFold 3: Abramson et al., Nature, 2024
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