---
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.