nf-binder-design

Nextflow pipelines for de novo protein binder design.

Overview

This project provides Nextflow workflows for de novo design of protein binders:

• RFdiffusion → ProteinMPNN → AlphaFold2(initial guess) → Boltz-2 refolding pipeline
• RFdiffusion Partial Diffusion → Boltz-2 refolding for diversification and optimization
• BindCraft - parallel execution across multiple GPUs
• BoltzGen - design proteins and complexes using BoltzGen
• Boltz Pulldown - an AlphaPulldown-like protocol using Boltz-2

Features

• Flexible workflow options for different binder design strategies
• HPC-ready with support for SLURM and other job schedulers
• Multi-GPU parallelization for BindCraft trajectories
• Plugin system for custom design filters
• Comprehensive reporting with HTML outputs and summary statistics

Quick Links

• Setup Instructions
• RFdiffusion Workflows
• BindCraft Workflow
• BoltzGen Workflow
• Boltz Pulldown
• Utility Scripts
• GitHub Repository
• Examples Directory

License

The pipeline code the comprises nf-binder-design is licensed under the MIT License.

Note that some dependencies, packaged externally as containers, are under less permissive licenses:

⚠️ "Commercial Use Restrictions"

Components of these workflows use RFdiffusion and BindCraft, which depend on PyRosetta/Rosetta. These are free for non-commercial use only. Commercial use requires a paid license agreement with University of Washington. See:

• Rosetta License
• Rosetta Licensing FAQ

Citing

If you use nf-binder-design in your research, please cite:

• Perry, A., Taveneau, C., & Knott, G. J. (2025). nf-binder-design: a Nextflow pipeline for protein binder design (0.1.4). Zenodo. https://doi.org/10.5281/zenodo.16809705

and include citations for the underlying tools used in the workflow as appropriate:

• RFdiffusion

  • Watson, J.L., Juergens, D., Bennett, N.R. et al. "De novo design of protein structure and function with RFdiffusion.", Nature, 620, 1089–1100 (2023). https://doi.org/10.1038/s41586-023-06415-8

  • Bennett, N.R., Coventry, B., Goreshnik, I. et al. Improving de novo protein binder design with deep learning. Nat Commun, 14, 2625 (2023). https://doi.org/10.1038/s41467-023-38328-5

• ProteinMPNN

  • Dauparas, J. et al. Robust deep learning–based protein sequence design using ProteinMPNN. Science, 378,49-56(2022). https://doi.org/10.1126/science.add2187

• Alphafold2

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

• BindCraft

  • Pacesa, M., Nickel, L., Schellhaas, C. et al. One-shot design of functional protein binders with BindCraft. Nature, 645, 1005-1010 (2025). https://doi.org/10.1038/s41586-025-09429-6

• Boltz

  • Passaro, S., Corso, G., Wohlwend, J. et al. Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. bioRxiv (2025). https://doi.org/10.1101/2025.06.14.659707
  • Wohlwend, J., Corso, G., Passaro, S. et al. Boltz-1: Democratizing Biomolecular Interaction Modeling. bioRxiv (2024). https://doi.org/10.1101/2024.11.19.624167

• ColabFold

  • Mirdita, M., Schütze, K., Moriwaki, Y. et al. ColabFold: making protein folding accessible to all. Nature Methods, 19, 679-682 (2022). https://doi.org/10.1038/s41592-022-01488-1

• BoltzGen

  • Stark, H., et al. "BoltzGen: Toward Universal Binder Design." Preprint (2025). https://hannes-stark.com/assets/boltzgen.pdf (accessed November 10, 2025).