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Generate intrinsically disordered peptide conformations via machine learning

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# IdpSAM: latent diffusion model for peptide conformation generation

## About
Repository implementing [idpSAM](https://todo.com) in [PyTorch](https://pytorch.org). IpdSAM is a latent [diffusion model](https://en.wikipedia.org/wiki/Diffusion_model) for generating Cα conformations of [intrinsically disordered proteins](https://en.wikipedia.org/wiki/Intrinsically_disordered_proteins) (IDPs) and peptides. The model was trained on a dataset of Markov Chain Monte Carlo simulations of 3,259 intrinsically disordered regions whose sequences were obtained from the [DisProt](https://www.disprot.org) database. The simulations were carried out using [ABSINTH](https://pubmed.ncbi.nlm.nih.gov/18506808/), an [implicit solvent model](https://en.wikipedia.org/wiki/Implicit_solvation), implemented in the [CAMPARI 4.0](https://campari.sourceforge.net/V4/index.html) package. Here we provide code and weights of a pre-trained idpSAM model.

## Applications
This repository can be used for the following applications (see below for more information):
* Generate Cα ensembles with a pre-trained idpSAM model.
* Generate all-atom ensembles with a pre-trained idpSAM model and the [cg2all model](https://github.com/huhlim/cg2all) for all-atom reconstruction.

# Installation

## Local system
We recommend to install and run this package in a new [Conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) that you create from the `sam.yml` file in this repository. If you follow this strategy, use these commands:

1. Clone the repository:
   ```bash
   git clone https://github.com/giacomo-janson/idpsam.git
   ```
   and go into the root directory of the repository.
2. Install the dedicated conda environment and dependencies:
   ```bash
   conda env create -f sam.yml
   ```
3. Activate the environment:
   ```bash
   conda activate sam
   ```
4. Install the `sam` Python library in editable mode (it will just put the library in [$PYTHONPATH](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONPATH)):
   ```bash
   pip install -e .
   ```
5. Optional, only if you want to perform all-atom reconstruction when using the idpSAM inference script. Install the [cg2all package](https://github.com/huhlim/cg2all) inside the `sam` environment created above:
   ```bash
   pip install git+http://github.com/huhlim/cg2all
   ```
   Note: this is the command for performing a [CPU-only installation](https://github.com/huhlim/cg2all/#installation) of cg2all. You can also attempt the GPU installation, which involves more steps. If you can't install cg2all with GPU support, the CPU installation is still good for idpSAM applications. This is because for short peptides cg2all is reasonably fast when running on a CPU.

## Run on the cloud
If you want to quickly use idpSAM on the cloud (no installations needed on your system), we have a [idpSAM Colab notebook](#running-remotely).

# Usage

## Generate conformational ensembles

### Running locally
You can generate a structural ensemble of a custom peptide sequence via the  `scripts/generate_ensemble.py` inference script. Its usage is:
```bash
python scripts/generate_ensemble.py -c config/models.yaml -s MFDNASTRNNKRERGKRQGKQTRTQRHADRSQT -o peptide -n 1000 -a -d cuda
```
Here is a description of the arguments:
* `-c`: configuration file for idpSAM. Use the default one provided in the `config` directory of the repository.
* `-s`: amino acid sequence of the instrinsically disordered peptide that you want to model.
* `-o`: output path. In this example, the command will save a series of output files named `peptide.*`. These are DCD trajectory files storing the conformations you generated and PDB files that you can use as topologies for parsing DCD files. Files with the `ca` code store only Cα atoms (the original output of idpSAM), files with the `aa` code store all-atoms conformations reconstructed by the [cg2all model](https://github.com/huhlim/cg2all) as a post-processing step.
* `-n`: number of conformations to generate.
* `-a`: flag for using cg2all to reconstruct all-atom details from Cα traces. You must first install the cg2all package to use this option. 
* `-d`: PyTorch device for the idpSAM models. If you want to generate ensembles with large number of conformations, we strongly reccommend to use GPU support, via the `cuda` value here. By default, the cg2all mode will run on CPU, since it is still fast.

There are also other options that you can tweak. Use the `--help` flag to get the full list of them.

### Running remotely
You can easily generate a Cα (and optionally all-atom) ensemble for a custom peptide using a [Colab notebook on the cloud](https://research.google.com/colaboratory/) and download the ensemble on your local system. The output will consists of DCD files, that you can parse with [MDTraj](https://github.com/mdtraj/mdtraj) for example. If you plan to generate large ensembles (> 1000 conformations), it will probably take hours of time if using a CPU runtime. If possible, use a GPU runtime to accelerate (few minutes of time) idpSAM.

Launch the notebook using the link below:

[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/giacomo-janson/idpsam/blob/main/notebooks/idpsam_experiments.ipynb)

# Updates
* 31/12/2023: initial release.

# References
Janson G and Feig M. Transferable deep generative modeling of intrinsically disordered protein conformations. BioRxiv (2024).

[![DOI](https://zenodo.org/badge/731574642.svg)](https://zenodo.org/doi/10.5281/zenodo.10636298)

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