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Protein design and sculpting using Rosetta and Deep learning methods (RFDiff and Alphafold2)

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# Prosculpt
Protein design and sculpting using Rosetta and Deep learning methods (RFDiff and Alphafold2)

![image](https://raw.githubusercontent.com/ajasja/prosculpt/main/pipeline_pic.png)
## Description 
The script `prosculpt_run.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](https://github.com/RosettaCommons/RFdiffusion/blob/main/README.md)).

The main steps are as follows:
1. 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.). 
2. The RFdiffusion module generates new protein structures (backbone atoms only) based on the options provided.
4. Generated PDB files are preprocessed using helper scripts and ProteinMPNN generates sequences (aminoacid residues) for these structures.
5. Sequences are prepared for the AF2, which folds them into structures.
6. Additional evaluation parameters are calculated for these structures using a separate script with Rosetta.
7. The final output is a CSV file containing AF2 structure path, evaluation parameters etc.

A working Collab can be found [here](https://colab.research.google.com/github/ajasja/prosculpt/blob/main/Prosculpt_Colab.ipynb)

## Requirements  
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).

## Installation
The easiest way to install prosculpt is using a container manager like [apptainer](https://apptainer.org/) (or singularity). To do so, follow these instructions

Begin by cloning prosculpt:
```bash
git clone https://github.com/ajasja/prosculpt.git
cd prosculpt
```
Create a new environment called prosculpt (or any name of your choice) using python or any venv manager and activate it:
```bash
python -m venv prosculpt_venv/
source prosculpt_venv/bin/activate
```
Install it and its dependencies
```bash
pip install .
```
Install pyrosetta into the environment
```bash
pip install pyrosetta --find-links https://west.rosettacommons.org/pyrosetta/quarterly/release.cxx11thread.serialization
```

At a location of your choice download the appropriate SIF files, executing the following commands:
```bash
singularity pull rfdiff.sif docker://rosettacommons/rfdiffusion
singularity pull proteinmpnn.sif docker://rosettacommons/proteinmpnn
singularity pull pymol.sif docker://jysgro/pymol:3.1.0_amd_arm
```

Depending on the prediction model, follow the appropriate instructions (Multiple instructions can be followed and the prediction model selected as desired in the configuration)

For using Boltz2, install boltz2 into the environment
```bash
pip install boltz[cuda] -U
```
it might be necessary to install compilers for boltz2 to run correctly. The exact command for this is system-dependent. If using conda, it can be done by
```bash
conda install -c conda-forge compilers
```

For Alphafold 3 use, request the model parameters following the instructions on the [Alphafold 3 github](https://github.com/google-deepmind/alphafold3). Then download the sif file using
```bash
singularity pull af3_2.sif docker://kosinskilab/alphafold3
```

For using Colabfold (Note: Colabfold is slower and less convenient than using Boltz2 or AF3 and is not recommended)
```bash
singularity pull colabfold.sif docker://unionbio/colabfold:w4KMVR7WrKDlCbdQ1BYrjQ-test
```

Copy or rename the file config/installation.yaml.singularity_template as config/installation.yaml. Then 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)
- If multiple users will be using the same installation, it is good to set the boltz2 cache parameter to the current user so that only one set of parameters are downloaded.

If using Boltz2 or Colabfold it is necessary to run a job to allow them to download the parameters. In a slurm cluster you can run:

Colabfold:
```bash
python run_tests.py -t unconditional
```
Boltz2:
```bash
python run_tests.py -t unconditional_boltz
```

## Usage

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.
```bash
python slurm_runner.py job_parameters.yaml
```

If you want to run Prosculpt locally, it can be called as
```bash
python prosculpt_run.py -cd config_directory -cn config_file_name
```

(note that the config file name must be written without the yaml extension)

## Examples
For usage examples see the /Examples or /Minimal_Prosculpt_script folders

## Skipping RfDiff
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.
If there are non-redesigned chains in the model that you want included in the final model, add the letter chain (without any numbers) to the designable residues.

In order to actually produce diverse results, `--sampling_temp` and `--backbone_noise` passed to ProteinMPNN are increased to `0.3` and `1`, respectively.

## Working with natural proteins
To be modelled correctly, natural proteins require structure prediction softwares 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 the user to input an MSA file for each chain in the reference structure for which the user wants alignment information to be used. To obtain these MSAs just run each chain in the reference structure separately through an MSA server and use the A3M file it produces. The easiest way is to run the sequence for your input structure through Alphafold. 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. For clarification, look at the binder example in the Examples directory.

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.

## Backbone filtering
For backbone filtering, custom scripts called plugins are used. A plugin is a python script containing a function called "filter_backbone" which takes a pdb file and any set of arguments and returns true if the pdb passes the filter and false if it doesn't. The content of the function can be defined as needed by the user. To call filters add the following code to your configuration yaml. Examples can be found in the plugins folder and usage example can be found in the examples folder

```
rfdiff_backbone_filters:
    - filter_name: [filter_name]
      filter_script: [filter script file path]
      delete_failed: [true/false]
```

## Metrics and scoring script

Prosculpt calculates the following metrics

| Metric  | Meaning |
| ------------- | ------------- |
| RMSD  | Full model RMSD to RFDiff backbone  |
| pLDDT  | full model pLDDT  |
| RMSD_sculpted  | RMSD of any residues not present in original structure to RFDiff backbone |
| RMSD_fixed_chains  | RMSD of residues in non-redesigned chains to RFDiff backbone  |
| RMSD_motif  | RMSD of non-redesigned residues in chains containing redesigned residues  |
| pae  | Predicted alignment error  |

In addition, a scoring script can be added to calculate extra metrics. The scoring script is a python file called "scoring_script.py" in the scripts folder that outputs a CSV called "rosetta_scores.csv", with a "model_path" column. This CSV file gets merged with the prosculpt metrics for the final output. A default scoring script is included in the scripts folder, which calculates radius of gyration, charge and sap.

## Inputs: 
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](https://github.com/RosettaCommons/RFdiffusion/blob/main/README.md).
    - 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 called `final_pdbs` 
- `num_designs_rfdiff`: number of structures generated by RFdiffusion
- `num_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.

## Passing additional arguments to ProteinMPNN
Add parameters as required by ProteinMPNN to the `pass_to_mpnn` group in run.yaml (including trailing `--`).

## Passing additional arguments to AlphaFold
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: ""`.

## Examples for contigs
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]'`. 
- 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](https://github.com/RosettaCommons/RFdiffusion/blob/main/README.md#motif-scaffolding).

## Automatic restart 
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.

## Caveats/Troubleshooting
### Binder design
* In the input pdb, target must start with chain `B`, and use subsequent chain letters (C, D ...) if it has multiple chains. 
    * Otherwise, errors arise when passing into ProteinMPNN.
* Targets should not have missing residues s. t. distance between two residues would be larger than `chain_break_cutoff_A`
    * Otherwise, rechain.py will start a new chain at that point, causing errors when passing into ProteinMPNN.
    * Note that even without missing residues, calculated distance after the RfDiffusion step may sometimes be just slightly above 2 Å around prolines, so adjust accordingly. 
* The `chains_to_design` parameter should not be present at all in the .yaml config file! 
    * Otherwise, target will be fixed incorrectly and thus its aminoacids changed.

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