Protein design and sculpting using Rosetta and Deep learning methods (RFDiff and Alphafold2)
The script rfdiff_mpnn_af2_merged.py runs a pipeline to automate the processes of generating protein structures with RFdiffusion, sequence generation with ProteinMPNN, and folding and evaluation with AF2 and Rosetta. Specifically, the script uses motif scaffolding to generate structures (see RFdiffusion github repository).
The main steps are as follows:
- The main inputs are a protein PDB file and a yaml file that specifies how to generate the new structure around the original protein (which parts to keep, etc.).
- The RFdiffusion module generates new protein structures (backbone atoms only) based on the options provided.
- Generated PDB files are preprocessed using helper scripts and ProteinMPNN generates sequences (aminoacid residues) for these structures.
- Sequences are prepared for the AF2, which folds them into structures.
- Additional evaluation parameters are calculated for these structures using a separate script with Rosetta.
- The final output is a CSV file containing AF2 structure path, evaluation parameters etc.
The script requires the prosculpt package. It assumes that RFdiffusion, proteinMPNN, and AF2 are installed and that the correct paths are provided in the installation.yaml config file. Additionall biopython, hydra-core, pandas and scipy and pyRosetta are required.
For running tests, Python version must be >= 3.7 (it needs the capture_output arg).
The easiest way to install prosculpt is using a container manager like apptainer (or singularity). To do so, follow these instructions
Begin by cloning prosculpt:
git clone https://github.com/ajasja/prosculpt.git
cd prosculptCreate a new environment called prosculpt (or any name of your choice) using python or any venv manager and activate it:
python -m venv prosculpt_venv/
source prosculpt_venv/bin/activateInstall it and its dependencies
pip install .Install pyrosetta into the environment
pip install pyrosetta-installer
python -c 'import pyrosetta_installer; pyrosetta_installer.install_pyrosetta()'At a location of your choice download the appropriate SIF files, executing the following commands:
singularity pull rfdiff.sif docker://rosettacommons/rfdiffusion
singularity pull proteinmpnn.sif docker://rosettacommons/proteinmpnn
singularity pull colabfold.sif docker://unionbio/colabfold:w4KMVR7WrKDlCbdQ1BYrjQ-test
singularity pull pymol.sif docker://jysgro/pymol:3.1.0_amd_armGo into prosculpt/config/installation.yaml and replace the default values with the ones corresponding to your system. Mainly:
- Replace the run command of the sif files with the appropriate commands and paths for your system.
- Replace the prosculpt_python_path with the python path of your prosculpt env (You can get it by running "which python" with the env active)
- Replace the slurm options with those that correspond with your cluster (these can then be overwritten by individual job settings)
Now we will run a single test, to allow colabfold to download the weights by executing: (This step uses slurm. If your system doesn't use slurm, just run prosculpt by yourself once)
python run_tests.py -t unconditionalAfter this has succesfully ran, you can run all tests by executing:
python run_tests.pyAfter all tests are finished, go into the Examples/Examples_out_{yourdatetime} and check the output txt file to verify all tests have passed.
To run Prosculpt on Slurm, use the slurm_runner.py passing it an input yaml file with the job options. Note that slurm_runner no longer supports passing arguments inline with the exception of ++output_folder.
python slurm_runner.py job_parameters.yamlIf you want to run Prosculpt locally, run the rfdiff_mpnn_af2_merged.py directly and modify args as needed.
For usage examples see the /Examples or /Minimal_Prosculpt_script folders
Sometimes you may want to skip RfDiff and only pass the pdb to ProteinMPNN and AF with a few residues to change.
Add skipRfDiff: True and designable_residues: [A8, A9, A10, A13, A85] to the yaml config, where designable_residues contains the residues you want to change.
In order to actually produce diverse results, --sampling_temp and --backbone_noise passed to ProteinMPNN are increased to 0.3 and 1, respectively.
Natural proteins require AF2 to work with multiple sequence alignments. In order to obtain good results when working with natural proteins, the option "use_a3m" needs to be set to True. This option requires MSAs for each chain in the reference structure. To obtain these MSAs just run each chain in the reference structure separately through AF2 and use the A3M file it produces. Each file needs to contain within the name "Chain_X" where X is the chain ID of the corresponding chain in the reference structure file. These alignment files need to be placed together in a directory and the "a3m_dir" option needs to be set to this directory in the yaml config file or passed through command line.
Prosculpt generates new "partial alignment" files for each design where natural proteins are modelled using MSAs and designed proteins (or designed portions of hybrid proteins) are not.
Most input parameters are documented in the examples, as well as the run.yaml config file. However, here's some additional info about them:
contig: explained in detail in the RFdiffusion github repository.- Please note: The whole argument must be enclosed in double quotes and square brackets!
output_dir: output directory. Along the pipeline, each module will create its own subdirectory. The AF2 models are in the end renamed and copied to a subdirectory calledfinal_pdbsnum_designs_rfdiff: number of structures generated by RFdiffusionnum_seq_per_target_mpnn: number of sequences generated per one RFdiffusion structure (each sequence is by default folded 5 separate times by AF2)
A note on outputs: when running on a cluster for each task a separate final_outputs.csv will be created. Run merge_csv.py along with the argument --output_dir to merge all csv files into one file.
Add parameters as required by ProteinMPNN to the pass_to_mpnn group in run.yaml (including trailing --).
Add parameters as required by AlphaFold to the pass_to_af group in run.yaml (including trailing --). Switches should be passed with an empty string value, like this --templates: "".
Contigs are the most important input (for now, guiding potentials are another input to be explored in the future). Here are a few guidelines to ease the start of your projects.
- Ranges starting with letters will be taken from the input.pdb
- Ranges without letters will generate that many residues
/0(mind the space!) will create a chain break- For connecting two parts, you could use a contig such as this:
'[A1-37/30-60/A42-70]'. If the original PDB has another chain B, it will not be given to RFdiffusion in this case. - For connecting two parts with respect to another structure:
'[C33-60/4-7/B1-30/0 C61-120]'. Chain B is given to RFdiffusion but no new structures are generated on it. Note: in the generated structure PDB, the chains will be labeled as A (connected A and B) and B (C in original). - For connecting multiple chains:
'[E10-68/5-15/C4-72/5-15/D78-145/5-15/A1-60/]' - For leveraging symmetry:
'[10/A29-41/10/0 10/B29-41/10/0 10/C29-41/10]'. In this case, the additional symmetry parameter must be:symmmetry=C3(three subunits). Also, you cannot use contigs with interval lengths to be generated e.g.'[10-20/A29-41/10-20]'. Symmetry is not good enough for now, it needs some debugging in the MPNN module and optimization in guiding potentials of RFdiffusion. - Important: if you wish to force a number of newly generated residues, pass it as range:
[A1-7/3-3/A11-12/1-1/A14-84/1-1/A86-96](and not[A1-7/3/A11-12/1/A14-84/1/A86-96]) - More info is available in the RFdiff repo.
In case of errors in any of the sub programs (RfDiffusion, ProteinMPNN, AlphaFold) prosculpt can automatically restart, so that the previous steps are not lost.
To do so, pass auto_restart: n, where n ... number of allowed restarts, to the .yaml config.