---
license: mit
language:
- en
base_model:
- EvolutionaryScale/esmc-300m-2024-12
- EvolutionaryScale/esmc-600m-2024-12
- Rostlab/ProstT5
---
## Table of Contents

* [Protein Location Predictor](#protein-location-predictor)

  * [Features](#features)
  * [Requirements](#requirements)

    * [Supported Python Version](#supported-python-version)
    * [Dependencies ](#dependencies-full-environmentyml)
    * [Hardware Requirements](#hardware-requirements)
  * [Installation](#installation)
  * [Usage](#usage)

    * [GUI Mode](#gui-mode)
  * [Example Input & Output](#example-input--output)
  * [Project Structure](#project-structure)
  * [Contributing](#contributing)

## Protein Location Predictor

A comprehensive GUI application for predicting protein subcellular localization using SVM and Random Forest classifiers using state-of-the-art protein language models including PROST-T5 and ESM-C embeddings as training data.

### Features

* **Multiple Model Support**: Choose from three different prediction models:

  * PROST-T5: Transformer-based protein language model
  * ESM-C 300M: Evolutionary Scale Modeling (300M parameters)
  * ESM-C 600M: Evolutionary Scale Modeling (600M parameters)
* **User-Friendly GUI**: Simple Tkinter-based interface with progress tracking (see screenshot below)
* **Sequential Processing**: Process multiple protein sequences from FASTA files
* **Flexible Output**: Save predictions with confidence scores in text (CSV) format
* **Error Handling**: Comprehensive error handling and user feedback

### Supported Python Version

This project has been tested on **Python 3.10+**.

## Requirements

#### Dependencies (Full environment.yml)

The complete environment definition is located in `environment.yml`. This file includes all necessary packages for PyTorch, Transformers, ESM models, and GUI operation. Here is a brief excerpt:

```yaml
name: tesisEnv
channels:
  - bioconda
  - anaconda
  - conda-forge
  - defaults

# Python version and major packages
dependencies:
  - python=3.10.16
  - pytorch=2.6.0
  - torchvision=0.21.0
  - torchtext=0.18.0
  - transformers=4.46.3
  - scikit-learn=1.6.1
  - biopython=1.85
  - esm=3.1.4
  - numpy=1.26.4
  - joblib=1.4.2
  - tk
  # plus many others (see full file for complete list)
```

To ensure exact reproducibility, use:

```bash
conda env create -f environment.yml
```

### Hardware Requirements

* **Minimum**: 8 GB RAM, CPU-only execution
* **Recommended**: 16 GB+ RAM, NVIDIA GPU with 8 GB+ VRAM
* **Storage**: \~5 GB for model weights and cache

## Installation

1. **Clone the repository** (with Git LFS for large model files):

   ```bash
   git lfs install
   git clone https://huggingface.co/jpuglia/ProteinLocationPredictor
   ```

   If you prefer to skip downloading model weights initially:

   ```bash
   GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/jpuglia/ProteinLocationPredictor
   ```

2. **Navigate into the project directory**:

   ```bash
   cd ProteinLocationPredictor
   ```

3. **Create and activate the Conda environment**:

   ```bash
   conda env create -f environment.yml
   conda activate tesisEnv
   ```

4. **(If skipped above) Download model weights manually**:
   Model files live in the `Models/` directory. If you used `GIT_LFS_SKIP_SMUDGE`, run:

   ```bash
   git lfs pull
   ```

## Usage

### GUI Mode

1. Launch the application:

   ```bash
   python gui.py
   ```
2. In the menu, click **File → Load FASTA** and select your input file (`.fasta`, `.fa`, or `.fas`).
3. Choose one of the prediction models (PROST-T5, ESM-C 300M, or ESM-C 600M).
4. Click **Run Prediction** and monitor the progress bar.
5. When complete, you will be prompted to choose an output directory and filename.

## Example Input & Output

**Input FASTA (`example/input.fasta`):**

```
>protein_1
MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG
>protein_2
MKTIIALSYIFCLVFAHATAKASEQTDNLQWDLAAIDNSGGHNAVDIKQNLQFQCQNNLHGCF
```

**Output CSV (`example/output.csv`):**

```csv
Sequence_ID,Prediction 1,Prediction 2,Prediction 3,Prediction 4,Prediction 5,Prediction 6
protein_1,Cytoplasmic (0.9860),CytoplasmicMembrane (0.0081),Periplasmic (0.0029),Extracellular (0.0019),OuterMembrane (0.0007),Cellwall (0.0003)
protein_2,SignalPeptide (0.7523),Extracellular (0.1234),CytoplasmicMembrane (0.0645),Cellwall (0.0345),Periplasmic (0.0201),OuterMembrane (0.0052)
```

## Project Structure

```
ProteinLocationPredictor/
├── gui.py
├── src/
│   └── my_utils.py
├── Models/
│   ├── ProstT5_svm.joblib
│   ├── ESMC-300m_svm.joblib
│   ├── ESMC-600m_svm.joblib
│   └── ...
├── environment.yml
├── README.md
└── doc/
    └── screenshots/
        └── gui_example.png
```

## Contributing

1. Fork the repository
2. Create a feature branch:

   ```bash
   git checkout -b feature/amazing-feature
   ```
3. Commit your changes:

   ```bash
   git commit -m "Add amazing feature"
   ```
4. Push to your branch:

   ```bash
   git push origin feature/amazing-feature
   ```
5. Open a Pull Request or start a discussion: [Repository Discussions](https://huggingface.co/jpuglia/ProteinLocationPredictor/discussions)