baker-laboratory/rf_diffusion_all_atom

Public RFDiffusionAA repo

Code for RFDiffusion AA

Setup/Installation

1. Clone the package

git clone https://github.com/baker-laboratory/rf_diffusion_all_atom.git
cd rf_diffusion_all_atom

2. Download the container used to run RFAA.

wget http://files.ipd.uw.edu/pub/RF-All-Atom/containers/rf_se3_diffusion.sif

3. Download the model weights.

wget http://files.ipd.uw.edu/pub/RF-All-Atom/weights/RFDiffusionAA_paper_weights.pt

4. Initialize git submodules

git submodule init
git submodule update

5. Install Apptainer

Install apptainer if you do not already have it on your system. This will allow you to run our code without installing any python packages using a prepackaged sif: https://apptainer.org/docs/admin/main/installation.html

Inference

Small molecule binder design

To generate a binder to the ligand OQO from PDB 7v11, run the following:

Example (ligand binder):

/usr/bin/apptainer run --nv rf_se3_diffusion.sif -u run_inference.py inference.deterministic=True diffuser.T=100 inference.output_prefix=output/ligand_only/sample inference.input_pdb=input/7v11.pdb contigmap.contigs=[\'150-150\'] inference.ligand=OQO inference.num_designs=1 inference.design_startnum=0

Note: The --nv flag must be omitted if not using a GPU.

Explanation of arguments:

• inference.deterministic=True seeds the random number generators used so that results are reproducible. i.e. running with inference.design_startnum=X will produce the same reusults. Note that torch does not guarantee reproducibility across CPU/GPU architectures: https://pytorch.org/docs/stable/notes/randomness.html
• inference.num_designs=1 specifies that 1 design will be generated
• contigmap.contigs=[\'150-150\'] specifies that the length of the generated protein should be 150
• diffuser.T=100 specifies the number of denoising steps taken.

Expected outputs:

• output/ligand_only/sample_0.pdb The design PDB
• output/ligand_only/sample_0_Xt-1_traj.pdb The partially denoised intermediate structures
• output/ligand_only/sample_0_X0-1_traj.pdb The predictions of the ground truth made by the network at each step

Note that the sequences associated with these structure have no meaning, apart from the given motif. LigandMPNN or similar must be used to generate sequences for the backbones if they are to be used for structure prediction / expression.

To include protein residues A430-435 in the motif, use the argument contigmap.contigs. e.g. contigmap.contigs=[\'10-120,A84-87,10-120\'] tells the model to design a protein containing the 4 residue motif A84-87 with 10-120 residues on either side.

Small molecule binder design with protein motif

Example (ligand binder with protein motif):

/usr/bin/apptainer run --nv rf_se3_diffusion.sif -u run_inference.py inference.deterministic=True diffuser.T=200 inference.output_prefix=output/ligand_protein_motif/sample inference.input_pdb=input/1haz.pdb contigmap.contigs=[\'10-120,A84-87,10-120\'] contigmap.length="150-150" inference.ligand=CYC inference.num_designs=1 inference.design_startnum=0

An end-to-end design pipeline illustrating the design of heme-binding proteins using RFdiffusionAA, proteinMPNN, AlphaFold2, LigandMPNN and PyRosetta is available at: https://github.com/ikalvet/heme_binder_diffusion