rfDiff - GFP Protein Design with RFdiffusion

Colab notebooks for GFP protein design using RFdiffusion, created as lab exercise templates.

These notebooks demonstrate two complementary approaches to protein design using the RFdiffusion framework, applied to Green Fluorescent Protein (GFP, PDB: 1GFL) as a teaching example.

Notebooks

• GFP_Option1_MotifScaffolding.ipynb - Motif scaffolding: fixes the GFP chromophore triad (S65-Y66-G67) and diffuses a new scaffold around it
• GFP_Option2_PartialDiffusion.ipynb - Partial diffusion: noises and rebuilds the full GFP beta-barrel, preserving topology

Parameters Used

Option 1 — Motif Scaffolding

Parameter	Value
contigs	60/A60-75/60
pdb	1GFL
iterations	100
hotspot	A65,A66,A67
num_designs	4
partial_T	auto

Option 2 — Partial Diffusion

Parameter	Value
contigs	A
pdb	1GFL
iterations	100
num_designs	4
partial_T	40

Attribution

These notebooks are based on and derived from the ColabDesign RFdiffusion notebook by Sergey Ovchinnikov (sokrypton) and the RFdiffusion team:

• Original Colab notebook: diffusion.ipynb
• ColabDesign repository: sokrypton/ColabDesign

RFdiffusion Citation

If you use these notebooks or RFdiffusion in your work, please cite:

Watson, J.L., Juergens, D., Bennett, N.R., Trippe, B.L., Yim, J., Eisenach, H.E., Ahern, W., Borst, A.J., Ragotte, R.J., Milles, L.F., Wicky, B.I.M., Hanikel, N., Pellock, S.J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S.V., Lauko, A., De Bortoli, V., Mathieu, E., Ovchinnikov, S., Barzilay, R., Jaakkola, T.S., DiMaio, F., Baek, M., Baker, D. (2023). De novo design of protein structure and function with RFdiffusion. Nature, 620, 1089-1100. https://doi.org/10.1038/s41586-023-06415-8

ProteinMPNN Citation

Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R.J., Milles, L.F., Wicky, B.I.M., Courbet, A., de Haas, R.J., Bethel, N., Leung, P.J.Y., Huddy, T.F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A.K., King, N.P., Baker, D. (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, 378(6615), 49-56. https://doi.org/10.1126/science.add2187

License

Please refer to the ColabDesign and RFdiffusion repositories for their respective licenses.