name: 'aav-vector-design-agent'
description: 'AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command

AAV Vector Design Agent

The AAV Vector Design Agent provides AI-driven design of adeno-associated virus vectors for gene therapy applications. It covers capsid selection and engineering, promoter/enhancer design, transgene optimization, and manufacturing considerations.

When to Use This Skill

• When selecting optimal AAV serotype for tissue-specific targeting.
• To design novel capsid variants with enhanced properties.
• For optimizing transgene expression cassettes.
• When predicting immunogenicity and neutralizing antibody escape.
• To design liver-detargeted or CNS-tropic vectors.

Core Capabilities

1. Capsid Selection: Match AAV serotype to target tissue based on tropism profiles.

2. Capsid Engineering: Design modified capsids for enhanced transduction or immune evasion.

3. Promoter Design: Select and optimize tissue-specific or ubiquitous promoters.

4. Transgene Optimization: Codon optimization and regulatory element design.

5. Immunogenicity Prediction: Predict NAb binding and T-cell epitopes.

6. Manufacturing Assessment: Evaluate producibility and purification considerations.

AAV Serotype Tropism

Workflow

1. Input: Target tissue, therapeutic gene, patient population characteristics.

2. Capsid Selection: Rank serotypes by tropism profile match.

3. Capsid Engineering: Design modifications if needed (peptide insertion, point mutations).

4. Cassette Design: Optimize ITR-to-ITR expression cassette.

5. Immunogenicity Analysis: Predict NAb prevalence and T-cell epitopes.

6. Manufacturing Review: Assess production feasibility.

7. Output: Complete vector design with rationale.

Example Usage

User: "Design an AAV vector for liver-directed gene therapy in hemophilia B with low immunogenicity."

Agent Action:

python3 Skills/Gene_Therapy/AAV_Vector_Design_Agent/aav_designer.py \
    --target_tissue liver \
    --therapeutic_gene F9 \
    --indication hemophilia_b \
    --minimize_immunogenicity true \
    --nab_escape true \
    --promoter liver_specific \
    --output aav_design/

Expression Cassette Components

5' ITR - [Promoter] - [5' UTR] - [Transgene] - [WPRE] - [PolyA] - 3' ITR

Packaging limit: ~4.7 kb between ITRs

Promoter Options:
| Promoter | Type | Size | Application |
|----------|------|------|-------------|
| CAG | Ubiquitous | 1.7 kb | Strong expression |
| EF1α | Ubiquitous | 1.2 kb | Constitutive |
| LP1 | Liver-specific | 0.5 kb | Hepatocyte targeting |
| hSyn | Neuron-specific | 0.5 kb | CNS applications |
| MCK | Muscle-specific | 0.6 kb | Myopathies |
| CMV | Ubiquitous | 0.6 kb | High initial (silenced) |

Capsid Engineering Strategies

Directed Evolution:
- Error-prone PCR libraries
- DNA shuffling
- Selection in target tissue

Rational Design:
- Peptide display (insertion in variable loops)
- Point mutations for receptor targeting
- Tyrosine-to-phenylalanine for stability

Machine Learning:
- Sequence-function models
- Generative models for novel capsids
- Tropism prediction

Immunogenicity Considerations

Pre-existing NAbs:
| Serotype | NAb Prevalence |
|----------|----------------|
| AAV2 | 30-60% |
| AAV5 | 15-30% |
| AAV8 | 15-25% |
| AAV9 | 20-35% |

Mitigation Strategies:
- Serotype selection based on patient screening
- Engineered NAb-evading capsids
- Immunosuppression protocols
- Plasmapheresis

AI/ML Components

Tropism Prediction:
- CNN on capsid sequence
- Cell-type specific transduction
- Cross-species translation

Immunogenicity Modeling:
- MHC binding prediction
- T-cell epitope mapping
- NAb epitope prediction

Expression Optimization:
- Codon optimization algorithms
- RNA structure prediction
- miRNA target site avoidance

Manufacturing Considerations

Prerequisites

• Python 3.10+
• Sequence analysis tools
• Immunoinformatics packages
• Structural biology tools

Related Skills

• CRISPR_Design_Agent - For gene editing payloads
• Protein_Engineering - For capsid design
• RNA_Therapeutics - For alternative modalities

Regulatory Considerations

1. Biodistribution: Required for IND
2. Shedding: Vector in bodily fluids
3. Germline transmission: Gonadal presence
4. Integration risk: Random vs site-specific
5. Immunogenicity: Pre-existing and induced

Author

AI Group - Biomedical AI Platform