🧬 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 supporting two generative backends — BoltzGen (default) and Proteina-Complexa — to design protein binders, nanobodies, and peptides.

📋 Overview

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

• 🎯 BoltzGen (default): Flow-matching generative model for protein design using design YAML specifications
• 🏗️ Proteina-Complexa: Generative diffusion model for protein design using pipeline config YAMLs
• 🧬 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

Both design backends converge into the same downstream pipeline (ProteinMPNN → Boltz-2 → Analysis → Consolidation).

🚀 Quick Start

✅ Prerequisites

• ⚙️ Nextflow (≥23.04.0)
• 🐳 Docker or Singularity
• 🎮 GPU recommended for optimal performance

🧪 Running with Test Profiles

Test the pipeline with one of three available profiles (uses BoltzGen by default):

# 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

BoltzGen (default)

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

Proteina-Complexa

nextflow run main.nf \
  --protein_design_tool complexa \
  --input samplesheet_complexa.csv \
  --complexa_ckpt_dir /path/to/checkpoints \
  --outdir results \
  -profile docker

📝 Input Format

The samplesheet format depends on the chosen design tool (--protein_design_tool). See assets/test_data/ for examples.

BoltzGen Samplesheet (default)

sample_id,design_yaml,structure_files,protocol,num_designs,budget,reuse,target_msa,target_sequence,target_template
design1,designs/my_design.yaml,target.cif,protein-anything,3,2,,target.a3m,target.fasta,

Column	Required	Description
sample_id	✅	Unique sample identifier
design_yaml	✅	Path to BoltzGen design YAML specification
target_sequence	✅	Target sequence FASTA (for Boltz-2 refolding)
structure_files		Comma-separated structure files (PDB/CIF)
protocol		Design protocol (protein-anything, peptide-anything, nanobody-anything, protein-small_molecule)
num_designs		Number of intermediate designs to generate
budget		Number of final diversity-optimized designs to keep
reuse		Reuse previous results (true/false)
target_msa		Pre-computed MSA for target (e.g., .a3m)
target_template		Template structure for Boltz-2 (CIF)

Complexa Samplesheet

sample_id,target_pdb,pipeline_config,target_sequence,target_msa,target_template
design1,target.cif,configs/pipeline.yaml,target.fasta,target.a3m,

Column	Required	Description
sample_id	✅	Unique sample identifier
target_pdb	✅	Target structure (PDB or CIF)
pipeline_config	✅	Complexa Hydra pipeline config YAML
target_sequence	✅	Target sequence FASTA (for Boltz-2 refolding)
target_msa		Pre-computed MSA for target (e.g., .a3m)
target_template		Template structure for Boltz-2 (PDB/CIF)

⚙️ Key Parameters

Design Tool Selection

• --protein_design_tool: Design backend to use — boltzgen (default) or complexa

Common Parameters

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

BoltzGen-Specific Parameters

• --cache_dir: Cache directory for BoltzGen model weights

Complexa-Specific Parameters

• --complexa_ckpt_dir: Path to Complexa checkpoint directory
• --complexa_search_algorithm: Search algorithm (best-of-n, beam-search, etc.)
• --complexa_nsteps: Diffusion sampling steps (default: 400)
• --complexa_batch_size: Generation batch size (default: 16)

See nextflow.config for all available parameters.

📁 Output

Results are organized by sample in the output directory:

results/
├── {sample_id}/
│   ├── boltzgen/          # BoltzGen designs (if using boltzgen)
│   ├── complexa/          # Complexa designs (if using complexa)
│   ├── proteinmpnn/       # Optimized sequences
│   ├── boltz2/            # Refolded structures
│   ├── ipsae/             # Interface scores
│   ├── prodigy/           # Affinity predictions
│   ├── foldseek/          # Structural search results
│   └── consolidated/      # Combined metrics report
└── pipeline_info/         # Execution reports

📚 Citation

If you use this pipeline, please cite:

• BoltzGen: Jing et al. (2024) "Generative Modeling of Molecular Dynamics Trajectories"
• Proteina-Complexa: [Add Complexa 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.

---

Built with ❤️ using Nextflow and Seqera AI

Documentation | Issues | Seqera