--- name: antibody-design-agent description: "---name: antibody-design-agent description: An advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion). license: MIT metadata: author: VUMC / UW Baker Lab version: \"1.0.0\" compatibility:" --- --- name: 'antibody-design-agent' description: 'An advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion).' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Antibody Design Agent This skill brings together cutting-edge tools for antibody engineering, including MAGE (Monoclonal Antibody Generator) and RFdiffusion for Antibodies. It enables the de novo design of antibodies against specific viral or tumoral targets. ## When to Use This Skill * **De Novo Design**: Generating antibody sequences/structures that bind to a specific antigen. * **Epitope Targeting**: Designing VHH or binders for a specific epitope on a target protein. * **Optimization**: Improving the affinity or stability of an existing antibody candidate. * **Viral Defense**: Rapidly generating antibodies against novel viral strains. ## Core Capabilities 1. **MAGE (Monoclonal Antibody Generator)**: Uses a protein language model to generate diverse antibody sequences against unseen viral strains. 2. **RFdiffusion for Antibodies**: Generates 3D antibody structures that bind to a target structure with high precision. 3. **ProteinMPNN**: Optimizes the sequence of the generated structures for solubility and expression. ## Workflow 1. **Target Definition**: Input the PDB structure or sequence of the antigen (target). 2. **Design Phase**: * Use **RFdiffusion** to generate the backbone of the binder (CDR loops). * Use **ProteinMPNN** to design the sequence for the backbone. * *Alternatively*, use **MAGE** to generate sequences directly from viral strain data. 3. **Validation (In Silico)**: Use AlphaFold3 or ESMFold to predict the complex structure and assess binding confidence (pLDDT, PAE). 4. **Selection**: Rank candidates for synthesis. ## Example Usage **User**: "Design a VHH nanobody that binds to the RBD of the SARS-CoV-2 KP.2 variant." **Agent Action**: 1. Retrieves RBD structure for KP.2. 2. Runs `RFdiffusion` with "binder" constraints on the RBD surface. 3. Generates 100 backbone candidates. 4. Sequences them with `ProteinMPNN`. 5. Folds the complexes with `AlphaFold3` to verify binding interface. 6. Returns top 5 sequences. --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Drug_Discovery/Antibody_Design/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD 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. # Mega AI Hub resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Drug_Discovery/Antibody_Design/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Data & Analytics, Development & Engineering, Education & Research, Healthcare & Life Sciences, Product Management Roles: Business Intelligence Analyst, Clinical Research, ML / AI Engineer, Product Manager, Researcher Specialties: API Tools: Alpha Fold, Mage, Proteinmpnn, Rf Diffusion Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: ad28cbf9fad428f2e2098a7713ffe721654c736a993c55fdc3547dae33a1de95 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.