mage-antibody-generator

$npx mdskill add swaruplab/operon/mage-antibody-generator

Generates antigen-conditioned antibody sequences for structural validation.

  • Solves the need for novel antibody sequences targeting a specific antigen.
  • Depends on Python 3.9+, GPU, and a local repository with generation scripts.
  • Decides based on user-provided antigen sequence and requested candidate count.
  • Delivers FASTA files and metadata via file system and optional JSON manifest.

SKILL.md

.github/skills/mage-antibody-generatorView on GitHub ↗
---
name: mage-antibody-generator
description: Ab seq forge
keywords:
  - antibody
  - antigen
  - FASTA
  - generation
  - validation
measurable_outcome: Generate the requested number of antibody sequences (default ≥5) with metadata (model checkpoint, seed) and deliver FASTA files within 10 minutes.
license: MIT
metadata:
  author: MAGE Team
  version: "1.0.0"
compatibility:
  - system: Python 3.9+ / GPU
allowed-tools:
  - run_shell_command
  - read_file
---

# MAGE (Monoclonal Antibody Generator)

Run the MAGE antibody generation workflow to propose antigen-conditioned antibody sequences for downstream structural validation.

## Workflow
1. **Prep env:** `cd repo` and install dependencies, then point to GPU if available.
2. **Run generator:** `python generate_antibodies.py --antigen_sequence <SEQ> --num_candidates N --output_dir ./results`.
3. **Collect outputs:** Provide FASTA paths + metadata, optionally translate into JSON manifest.
4. **Recommend validation:** Suggest AlphaFold/Rosetta checks and wet-lab follow-up.

## Guardrails
- Never imply binding efficacy without structural/experimental confirmation.
- Track model version + seeds to ensure reproducibility.
- Encourage downstream filtering (liability motifs, developability metrics).

## References
- Source instructions in `README.md` and repo scripts.

More from swaruplab/operon

SkillDescription
agentd-drug-discoveryUse the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.
alphafold-databaseAccess AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
antibody-design-agentAn advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion).
archrSingle-cell ATAC-seq analysis with ArchR (R). The mature R-based scATAC pipeline — Arrow files, doublet inference, iterative LSI + Harmony, clustering, gene scores, MACS2 peak calling, motif enrichment, chromVAR deviations, footprinting, scRNA-seq integration (label transfer), trajectory analysis (built-in + Monocle3 + Slingshot), and ArchR's interactive genome browser. Sister protocol to snapatac2 — pick this for established R workflows.
automated-bioinformatics-pipelinesPipeline maestro
autonomous-biomedical-agentAutonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
autonomous-oncology-agentPrecision Oncology
basecallingConvert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Note: Guppy is deprecated; use Dorado for all new analyses.
batch-processingProcess multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.
bindingdb-databaseQuery BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.