DeePFAS

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Encoding MS/MS spectra to chemical representation for identification of PFAS

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# DeePFAS: Deep Learning-Enabled Rapid Annotation of PFAS: Enhancing Non-Targeted Screening through Spectral Encoding and Latent Space Analysis

This repository provides implementations and code examples for [DeePFAS: Deep Learning-Enabled Rapid Annotation of PFAS: Enhancing Non-Targeted Screening through Spectral Encoding and Latent Space Analysis](https://pubs.acs.org/doi/10.1021/acs.est.5c09769). DeePFAS projects raw MS/MS data into the latent space of chemical structures for PFAS (Per- and Polyfluoroalkyl Substances) identification, facilitating the inference of structurally similar compounds by comparing spectra to multiple candidate molecules within this latent chemical space.

## Getting started

### Installation
Run the following code to install DeePFAS

``` shell

git clone git@github.com:CMDM-Lab/DeePFAS.git

conda create -n DeePFAS python=3.10.0 --yes
conda activate DeePFAS
cd DeePFAS/DeePFAS

pip install -r requirements.txt

```

### Quickstart

#### Download pretrained models

**Option 1:** Download the models automatically by running the following shell script.

**Option 2:** Manually download the model parameters from <a>https://zenodo.org/records/15083140</a>
- Copy `ae_best_model.pt` into the `ae/ae_saved` directory.
- Copy the DeePFAS model files into the `DeePFAS/deepfas_saved` directory.
- The default model is set to `deepfas_r2_over_best_model.pt`. You can change the model by modifying the `save_model_path` parameter in `DeePFAS/config/deepfas_config.json`.

```shell

cd DeePFAS/DeePFAS
./download_models.sh

```

#### Download the mass spectra of a wastewater sample (WWTP3)
The wastewaster sample was provided by Yi-Ju Chen. Please see the article [Emerging Perfluorobutane Sulfonamido Derivatives as a New Trend of Surfactants Used in the Semiconductor Industry](https://pubs.acs.org/doi/10.1021/acs.est.3c04435) by for details.
```shell

cd DeePFAS/DeePFAS
./download_wwtp3.sh

```

#### Download the mass spectra of PFAS standard mixtures (std_150)
The PFAS standard mixtures was provided by Yi-Ju Chen. Please see the article [Emerging Perfluorobutane Sulfonamido Derivatives as a New Trend of Surfactants Used in the Semiconductor Industry](https://pubs.acs.org/doi/10.1021/acs.est.3c04435) for details
```shell

cd DeePFAS/DeePFAS
./download_std_150.sh

```

#### Download the mass spectra of NIST PFAS database with MGF file format (Mascot Generic Format)

The NIST PFAS Database (version 1.1) is a public database and can be downloaded on <a>https://data.nist.gov/od/id/mds2-2905</a> with SQLite format. The MGF-format file was extracted and converted by Heng Wang.

```shell

cd DeePFAS/DeePFAS
./download_nist_pfas.sh

```

#### Download PubChem molecule database with chemical embedding generated by AutoEncoder

A small molecule database `mol_dataset/mol_database.hdf5` includes approximately 50000 molecules
for rapid testing and PFAS annotation. Larger molecule database within chemical embedding 
is available on [huggingface](https://huggingface.co/datasets/wanghengZzz/Chemical_embeddings_PubChem/tree/main)

#### PFAS annotation

Please convert MS/MS spectra as `.mgf` format and execute script `test_deepfas.sh` to quickstart PFAS annotation

```shell

cd DeePFAS/DeePFAS
./test_deepfas.sh

```

### Convert MS/MS spectra data to `.mgf` format
`.mgf` file is converted by python package `pyteomics`

``` python

from pyteomics import mgf
import numpy as np
data = []

intensity = [0.1, 1.0, 0.3, 0.4]
m_z = [11.1, 23.23, 111.44, 55.2]
spectrum = {
    'params': {
        # identifier of spectra in .mgf file (necessary)
        'title': 0,
        # ms level (necessary)
        'mslevel': 2,
        # precursor m/z (necessary)
        'pepmass': 562.957580566406,
        # adduct type (necessary)
        'precursor_type': '[M-H]-',
        # In eval mode, canonicalsmiless is necessary (unnecessary)
        'canonicalsmiles': 'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
        # collision energy (necessary)
        # absolute collision energy (ACE) format: 'collision_energy': 12
        # normalized collision energy (NCE) format: 'collision_energy': 'NCE=37.5%'
        'collision_energy': 'NCE=37.5%'
    },
    # m/z array (necessary)
    'm/z array': np.array(intensity), 
    # intensity array (necessary)
    'intensity array': np.array(m_z)
}

data.append(spectrum)
mgf.write(data, 'spectra.mgf', file_mode='w', write_charges=False)

```

### Generate customized molecule library
Molecule library and its chemical embedding are stored as `.hdf5` format in order to save storage space. Overwrite path of molecule file to `dataset_path` in `gen_latent_space_config.json`

``` shell

cd DeePFAS/DeePFAS
python3 ae/gen_latent_space.py \
 --deepfas_config_pth DeePFAS/config/deepfas_config.json \
 --ae_config_pth ae/config/gen_latent_space_config.json \
 --latent_space_out_pth your_file_name.hdf5 \
 --chunk_size 100000 \
 --compression_level 9

```

### A simple GUI interface

1. **Download Models**  
   Make sure you have downloaded the required models mentioned in the first section.

2. **Load MS2 Spectra**  
   Click `Load MS2 Spectra (.mgf)` to import the input MS2 spectra in `.mgf` format.

3. **Load Molecule Database**  
   Click `Load Molecule Database (.hdf5)` to load the reference molecular database.  
   You can use the sample database provided at: `mol_dataset/mol_database.hdf5`

4. **Set Output Directory**  
Click `Load Output Results Dir` to choose the folder where results will be saved.

``` shell

cd DeePFAS/DeePFAS
# (Linux / MacOS)
./run_DeePFAS.sh
# (Windows)
run_DeePFAS.bat

```

DeePFAS

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