# PDeepPP: A Comprehensive Protein Language Model Hub

PDeepPP is a hybrid protein language model designed to predict post-translational modification (PTM) sites, analyze biologically relevant features, and support a wide range of protein sequence analysis tasks. This repository serves as the central hub for accessing and exploring various specialized PDeepPP models, each fine-tuned for specific tasks, such as PTM site prediction, bioactivity analysis, and more.

The models in this repository can all be used on their corresponding datasets on GitHub ([https://github.com/fondress/PDeepPP])

---

## Overview

PDeepPP integrates state-of-the-art transformer-based self-attention mechanisms with convolutional neural networks (CNNs) to capture both global and local features in protein sequences. By leveraging pretrained embeddings from `ESM` and incorporating modular architecture components, PDeepPP offers a robust framework for protein sequence analysis.

This repository contains links to multiple task-specific PDeepPP models. These models are pre-trained or fine-tuned on publicly available datasets and are hosted on Hugging Face for easy access.

---

## Key Features

- **Flexible Architecture**: Combines self-attention and convolutional operations for robust feature extraction.
- **Task-Specific Models**: Includes pre-trained models for PTM prediction, bioactivity classification, and more.
- **Dataset Support**: Models are validated on datasets such as PTM and BPS, ensuring performance on real-world tasks.
- **Extensibility**: Users can fine-tune the models on custom datasets for new tasks.

---

## Available Models

### General Models
- [PDeepPP Main](https://huggingface.co/fondress/PDeepPP)

### Task-Specific Models

#### Post-Translational Modifications (PTMs)
- [PDeepPP Phosphorylation (Serine)](https://huggingface.co/fondress/PDeepPP_Phosphoserine)
- [PDeepPP Phosphorylation (Tyrosine)](https://huggingface.co/fondress/PDeepPP_Phosphorylation-Y)
- [PDeepPP Glycosylation (N-linked)](https://huggingface.co/fondress/PDeepPP_N-linked-glycosylation-N)
- [PDeepPP Glycosylation (O-linked)](https://huggingface.co/fondress/PDeepPP_O-linked-glycosylation)
- [PDeepPP Methylation (Lysine)](https://huggingface.co/fondress/PDeepPP_Methylation-K)
- [PDeepPP Methylation (Arginine)](https://huggingface.co/fondress/PDeepPP_Methylation-R)
- [PDeepPP SUMOylation](https://huggingface.co/fondress/PDeepPP_SUMOylation)
- [PDeepPP Ubiquitin](https://huggingface.co/fondress/PDeepPP_Ubiquitin)
- [PDeepPP S-Palmitoylation](https://huggingface.co/fondress/PDeepPP_S-Palmitoylation)
- [PDeepPP N6-acetyllysine(K)](https://huggingface.co/fondress/PDeepPP_N6-acetyllysine-K)
- [PDeepPP Hydroxyproline (P)](https://huggingface.co/fondress/PDeepPP_Hydroxyproline-P)
- [PDeepPP Hydroxyproline (K)](https://huggingface.co/fondress/PDeepPP_Hydroxyproline-K)
- [PDeepPP Pyrrolidone-carboxylic-acid (Q)](https://huggingface.co/fondress/PDeepPP_Pyrrolidone-carboxylic-acid-Q)

#### Bioactivity Prediction
- [PDeepPP ACE](https://huggingface.co/fondress/PDeepPP_ACE)
- [PDeepPP BBP](https://huggingface.co/fondress/PDeepPP_BBP)
- [PDeepPP DPPIV](https://huggingface.co/fondress/PDeepPP_DPPIV)
- [PDeepPP Toxicity](https://huggingface.co/fondress/PDeepPP_Toxicity)
- [PDeepPP Antimalarial (Main)](https://huggingface.co/fondress/PDeepPP_Antimalarial-main)
- [PDeepPP Antimalarial (Alternative)](https://huggingface.co/fondress/PDeepPP_Antimalarial-alternative)
- [PDeepPP Anticancer (Main)](https://huggingface.co/fondress/PDeepPP_Anticancer-main)
- [PDeepPP Anticancer (Alternative)](https://huggingface.co/fondress/PDeepPP_Anticancer-alternative)
- [PDeepPP Antiviral](https://huggingface.co/fondress/PDeepPP_Antiviral)
- [PDeepPP Antioxidant](https://huggingface.co/fondress/PDeepPP_Antioxidant)
- [PDeepPP Antibacterial](https://huggingface.co/fondress/PDeepPP_Antibacterial)
- [PDeepPP Antifungal](https://huggingface.co/fondress/PDeepPP_Antifungal)
- [PDeepPP Antimicrobial](https://huggingface.co/fondress/PDeepPP_Antimicrobial)
- [PDeepPP Anti-MRSA](https://huggingface.co/fondress/PDeepPP_Anti-MRSA)
- [PDeepPP Antiparasitic](https://huggingface.co/fondress/PDeepPP_Antiparasitic)
- [PDeepPP Bitter](https://huggingface.co/fondress/PDeepPP_bitter)
- [PDeepPP Umami](https://huggingface.co/fondress/PDeepPP_umami)
- [PDeepPP Neuro](https://huggingface.co/fondress/PDeepPP_neuro)
- [PDeepPP Quorum](https://huggingface.co/fondress/PDeepPP_Quorum)
- [PDeepPP TTCA](https://huggingface.co/fondress/PDeepPP_TTCA)

---

## Model Architecture

PDeepPP is built on a hybrid architecture that includes:

- **Self-Attention Global Features**: Captures long-range dependencies in protein sequences.
- **TransConv1d Module**: Combines transformer layers with convolutional layers for local feature extraction.
- **PosCNN Module**: Incorporates position-aware convolutional operations to enhance sequence representation.

---

## How to Use

To use any of the models, you need to install the required dependencies, such as `torch` and `transformers`:

```bash
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers
```
Here’s a quick example of how to load and use a model:

```python
from transformers import AutoModel, AutoTokenizer

# Load the model
model_name = "fondress/PDeepPP_ACE"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)

# Example input
protein_sequence = "VELYP"
# Preprocess the sequence (refer to specific model documentation for preprocessing steps)

# Forward pass
outputs = model(input_ids=processed_input)
logits = outputs.logits
```

## Training and Customization

You can fine-tune PDeepPP for custom tasks using your own datasets. The model supports:

- **Custom PTM types**: Extend the model to predict additional post-translational modifications.
- **Sequence classification tasks**: Adapt the model to classify protein sequences based on custom labels.
- **Feature extraction for downstream analyses**: Use PDeepPP to generate embeddings for tasks like clustering or similarity calculation.

Refer to the `PDeepPPConfig` class in the source repository for details on available hyperparameters and customization options.

---
 ## Citation
 If you use any of the PDeepPP models in your research, please cite the associated paper or repository:

```
 @article{your_reference,
  title={`PDeepPP`: A Hybrid Model for Protein Sequence Analysis},
  author={Author Name},
  journal={Journal Name},
  year={2025}
}
```