🧬 nf-proteindesign

⚠️ IMPORTANT: This pipeline was developed by Seqera as a proof of principle using Seqera AI. It demonstrates the capabilities of AI-assisted bioinformatics pipeline development but should be thoroughly validated before use in production environments.

A Nextflow pipeline for AI-powered protein design using Boltzgen to design protein binders, nanobodies, and peptides.

📋 Overview

This pipeline automates the process of designing novel protein binders using Boltzgen and provides comprehensive analysis through optional modules:

• 🎯 Boltzgen Design: Generate protein, nanobody, or peptide binders for target structures
• 🧬 ProteinMPNN: Optimize sequences for improved stability and expression
• 🔄 Boltz-2 Refolding: Validate designs through structure prediction
• 📊 IPSAE: Score protein-protein interface quality
• ⚡ PRODIGY: Predict binding affinity
• 🔍 Foldseek: Search structural databases for similar designs
• 📈 Metrics Consolidation: Generate comprehensive analysis reports

🚀 Quick Start

✅ Prerequisites

• ⚙️ Nextflow (≥23.10)
• 🐳 Docker or Singularity
• 🎮 GPU recommended for optimal performance

🧪 Running with Test Profiles

Test the pipeline with one of three available profiles:

# Test protein binder design
nextflow run main.nf -profile test_design_protein,docker

# Test nanobody binder design
nextflow run main.nf -profile test_design_nanobody,docker

# Test peptide binder design
nextflow run main.nf -profile test_design_peptide,docker

Replace docker with singularity if using Singularity containers.

🔬 Running with Your Own Data

nextflow run main.nf \
  --input samplesheet.csv \
  --outdir results \
  -profile docker

📝 Input Format

The pipeline requires a CSV samplesheet with design specifications. See assets/test_data/ for examples:

sample,design_yaml,protocol,num_designs,budget
my_design,design.yaml,protein-anything,10,5

⚙️ Key Parameters

• --input: Path to samplesheet CSV
• --outdir: Output directory (default: ./results)
• --run_proteinmpnn: Enable ProteinMPNN sequence optimization
• --run_boltz2_refold: Enable Boltz-2 structure prediction
• --run_ipsae: Enable IPSAE interface scoring
• --run_prodigy: Enable PRODIGY affinity prediction
• --run_consolidation: Generate consolidated metrics report

See nextflow.config for all available parameters.

📁 Output

Results are organized by sample in the output directory:

results/
├── boltzgen/          # Boltzgen designs and structures
├── proteinmpnn/       # Optimized sequences (if enabled)
├── boltz2/            # Refolded structures (if enabled)
├── ipsae/             # Interface scores (if enabled)
├── prodigy/           # Affinity predictions (if enabled)
├── foldseek/          # Structural search results (if enabled)
└── consolidated/      # Combined metrics report (if enabled)

📚 Citation

If you use this pipeline, please cite:

• Boltzgen: [Add Boltzgen citation]
• ProteinMPNN: Dauparas et al. (2022) Science
• Nextflow: Di Tommaso et al. (2017) Nature Biotechnology

📄 License

This pipeline is distributed under the MIT License. See LICENSE for details.

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Built with ❤️ using Nextflow and Seqera AI

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