diegosouzapw ---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
Capsid Selection: Match AAV serotype to target tissue based on tropism profiles.
Capsid Engineering: Design modified capsids for enhanced transduction or immune evasion.
Promoter Design: Select and optimize tissue-specific or ubiquitous promoters.
Transgene Optimization: Codon optimization and regulatory element design.
Immunogenicity Prediction: Predict NAb binding and T-cell epitopes.
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
Input: Target tissue, therapeutic gene, patient population characteristics.
Capsid Selection: Rank serotypes by tropism profile match.
Capsid Engineering: Design modifications if needed (peptide insertion, point mutations).
Cassette Design: Optimize ITR-to-ITR expression cassette.
Immunogenicity Analysis: Predict NAb prevalence and T-cell epitopes.
Manufacturing Review: Assess production feasibility.
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
- Biodistribution: Required for IND
- Shedding: Vector in bodily fluids
- Germline transmission: Gonadal presence
- Integration risk: Random vs site-specific
- Immunogenicity: Pre-existing and induced
Author
AI Group - Biomedical AI Platform