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README.md

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+# 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.
+
+## 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)
+
+#### 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](https://huggingface.co/fondress/PDeepPP_Antimalarial-main)
+- [PDeepPP Anticancer](https://huggingface.co/fondress/PDeepPP_Anticancer-main)
+- [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 Bitter](https://huggingface.co/fondress/PDeepPP_bitter)
+- [PDeepPP Umami](https://huggingface.co/fondress/PDeepPP_umami)
+- [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}
+}
+```