---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. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• aav-vector-design-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

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

Serotype	Primary Tropism	Clinical Use
AAV1	Muscle, CNS	Glybera (muscle)
AAV2	Broad (liver, muscle)	Luxturna (retina)
AAV5	CNS, liver, retina	Hemgenix (liver)
AAV8	Liver, muscle	Multiple trials
AAV9	CNS, cardiac, liver	Zolgensma (CNS)
AAVrh10	CNS, liver	CNS trials
AAVrh74	Muscle	Elevidys (muscle)
AAV-PHP.eB	CNS (mouse)	Research

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

Factor	Impact	Optimization
Capsid yield	Production cost	Sequence modifications
Empty/full ratio	Potency	Purification method
Aggregation	Stability	Formulation
DNA packaging	Transgene size	Cassette design

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