Metadata-Version: 2.4
Name: tpbla-thermal
Version: 0.0.3
Summary: TPBLA_ThermAL: ML for amyloid thermodynamic landscape prediction.
Project-URL: Homepage, https://github.com/conor-mckay98/ThermAL
Project-URL: Issues, https://github.com/conor-mckay98/ThermAL/issues
Author: Conor McKay
License: MIT License
        
        Copyright (c) 2025 Conor McKay
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
License-File: LICENSE
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8
Requires-Dist: joblib
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: openpyxl
Requires-Dist: pandas
Requires-Dist: pillow
Requires-Dist: scikit-learn==1.2.2
Requires-Dist: scipy==1.9.3
Requires-Dist: seaborn
Description-Content-Type: text/markdown

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) ![Python](https://img.shields.io/badge/python-3.8+-blue.svg)

![ThermAL Logo](ThermAL.png)

**TPBLA_ThermAL** is a desktop GUI tool for predicting regions that stabilise amyloid fibrils.  
It takes one or more FASTA sequences as input, generates single–residue variants, extracts physicochemical features, and predicts fitness landscapes using a pre-trained Random Forest model.

---

## 🚀 Quick install (development)

```bash
# Clone the repository
git clone https://github.com/conor-mckay98/ThermAL
cd ThermAL

# Install in editable mode
python -m pip install -e .

# Run the GUI
tpbla-thermal

📦 Requirements

    Python 3.8+

    scikit-learn==1.2.2

    scipy==1.9.3

    pandas

    numpy

    joblib

    seaborn

    matplotlib

    openpyxl

    pillow

    tk (comes with most Python distributions, but may require sudo apt-get install python3-tk on Linux)

All dependencies are declared in pyproject.toml and will be installed automatically with pip.
🧠 How it works

ThermAL generates and evaluates variants of input sequences:

    Input: FASTA file(s) with protein sequence(s).

    Variant generation: Creates all single–residue variants.

    Feature extraction:

        Amino Acid Composition (AAC)

        Dipeptide Composition (DPC)

        Sliding window AUC of physicochemical properties (Bulkiness, Polarity, Hydrophobicity, etc.).

    Prediction: Pre-trained Random Forest model scores variant fitness.

    Output: Excel tables and plots, saved per-sequence.

📁 Project Structure

ThermAL/
│
├── src/tpbla_thermal/        ← Python package
│   ├── cli.py                ← CLI entrypoint
│   └── gui.py                ← GUI implementation
│
├── required_docs/            ← model + reference atlases (not tracked in GitHub)
│   ├── 3_B_Atlas.xlsx
│   ├── 3_BT_Atlas.xlsx
│   ├── … (other atlas files)
│   └── ThermAL.joblib
│
├── ThermAL.png               ← logo displayed in GUI
├── ThermAL.ipynb             ← development notebook
├── pyproject.toml             ← build config (dependencies, entrypoints)
├── LICENSE
└── README.md                  ← this file

🎛️ Usage

    Ensure the required_docs/ folder (with all .xlsx atlases and the model file ThermAL.joblib) is present.

    Run:

tpbla-thermal

    In the GUI:

        Select FASTA File → choose your .fasta or .fa.

        Run Analysis → progress bars update as AAC/DPC and feature extraction run.

        Results are written into a folder named after each sequence header.

🔍 Outputs

Inside each job folder (named after the FASTA header), you’ll find:

    Predicted_fitness_with_1_letter_mutations.xlsx
    Full table of variants, predictions, and mutation codes.

    heatmap_simple.xlsx
    Pivot table of mean predicted fitness per mutation/position.

    heatmap.png
    Visual heatmap (blue→white→red), wild-type cells bordered in black.

    sliding_window.xlsx
    5-residue sliding window average of predicted fitness.

    sliding_window_with_foldx.png
    Plot highlighting stabilising regions.

📖 Citation

If you use this tool, please cite:

> McKay et al., 2025
> *"TPBLA_ThermAL: Machine learning for amyloid thermodynamic landscape prediction"*
"TPBLA_ThermAL: Machine learning for amyloid thermodynamic landscape prediction"

(CITATION.cff coming soon for GitHub citation support.)
🤝 Contact

Any problems or questions?
📧 conor_mckay98@aol.com

🔗 [LinkedIn](https://www.linkedin.com/in/conor-mckay-babba7171/)

PhD Student @ University of Leeds
