Metadata-Version: 2.4
Name: cpplocpred
Version: 1.0.0
Summary: Prediction of Cell-Penetrating Peptides and Subcellular Localization
Home-page: https://github.com/namanm04/CPPLocPred
Author: Nisha Bajiya, Naman Kumar Mehta, Gajendra P.S. Raghava
Author-email: raghava@iiitd.ac.in
Project-URL: Web Server, https://webs.iiitd.edu.in/raghava/cpplocpred/
Project-URL: Bug Tracker, https://github.com/namanm04/CPPLocPred/issues
Keywords: cell-penetrating peptides CPP bioinformatics machine-learning localization
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.0.0
Requires-Dist: scikit-learn>=0.24.0
Requires-Dist: catboost>=0.26
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: project-url
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# CPPLocPred

**Prediction of Cell-Penetrating Peptides and their Subcellular Localization**

[![PyPI version](https://badge.fury.io/py/cpplocpred.svg)](https://pypi.org/project/cpplocpred/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
[![Python 3.6+](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/)

CPPLocPred is a two-stage machine learning tool:
- **Stage 1** — CPP vs Non-CPP (ExtraTrees classifier, AAC features)
- **Stage 2** — Subcellular localization (CatBoost classifiers, DDR features):
  Cytoplasm · Nucleus · Mitochondria · Endo/Lysosome · Others

Web server: https://webs.iiitd.edu.in/raghava/cpplocpred/
GitHub: https://github.com/namanm04/CPPLocPred

---

## Installation

```bash
pip install cpplocpred
```

All model `.pkl` files, motif databases, and `MERCI_motif_locator.pl` are
bundled inside the package — **no separate download needed**.

Dependencies installed automatically: `pandas`, `scikit-learn`, `catboost`

> **Motif search requires Perl** to be installed and on your `PATH`.

---

## Usage

### Job 1 — CPP Prediction

```bash
cpplocpred -i input.fasta -o results.csv -j 1
cpplocpred -i input.fasta -o results.csv -j 1 -t 0.5
```

### Job 2 — Motif Search

```bash
cpplocpred -i input.fasta -o motifs.csv -j 2 -l Nucleus
cpplocpred -i input.fasta -o motifs.csv -j 2 -l Mitochondria -c Koolman
```

### All arguments

| Flag | Description | Default |
|------|-------------|---------|
| `-i` | Input FASTA file | required |
| `-o` | Output CSV file | required |
| `-j` | Job: `1`=Prediction, `2`=Motif Search | `1` |
| `-t` | CPP probability threshold | `0.44` |
| `-m` | Model directory | bundled in package |
| `-l` | Location: Cytoplasm, Nucleus, Mitochondria, Endo_lysosome, Others | `Cytoplasm` |
| `-c` | Motif class: None, Koolman, Betts-Russell, Rasmol | `None` |
| `--motif_dir` | Motif files directory | bundled in package |
| `--perl` | Path to perl executable | `perl` |

---

## Output

### Job 1 — `results.csv`

| Column | Description |
|--------|-------------|
| ID | Sequence identifier |
| Sequence | Amino acid sequence |
| CPP_Probability | Stage 1 probability (0–1) |
| CPP_Prediction | `CPP` / `Non-CPP` / `Invalid` |
| Cytoplasm_Probability | Stage 2 localization score |
| Nucleus_Probability | |
| Mitochondria_Probability | |
| Endo_lysosome_Probability | |
| Others_Probability | |
| Final_Localization | Predicted location(s), `;`-separated |

### Job 2 — `motifs.csv` + `motifs_hits.csv`

- **motifs.csv** — one row per sequence (ID, Length, Total_Hits, Motif_Patterns, …)
- **motifs_hits.csv** — one row per hit (ID, Start, End, Hit_Length, Motif_Pattern, Matched_Residues)

---

## Input format

```
>seq1
RQIKIWFQNRRMKWKK
>seq2
GRKKRRQRRRPPQ
```

Only standard 20 amino acid single-letter codes accepted (ACDEFGHIKLMNPQRSTVWY).

---

## Citation

> Bajiya N., Mehta N.K., Raghava G.P.S. (2025)
> CPPLocPred: Machine learning-based prediction and subcellular localization
> of cell-penetrating peptides. IIIT Delhi.
> https://webs.iiitd.edu.in/raghava/cpplocpred/

---

## License

MIT © 2025 Raghava Group, IIIT Delhi
