--- 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 | 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**: ```bash 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 --- Source attribution: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Education & Research, Healthcare & Life Sciences Roles: Clinical Research, Medical Writer, Researcher Specialties: API Tools: Aav_Designer.Py Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: b16522a9c24e14775c260ca6a23afd86fd4ee4d1924a4e15e8347ab6d596833b Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.