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AFusion: AlphaFold3 GUI(Graphical User Interface)

Documentation Status

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Transform your protein structure prediction workflow with AFusion - a sleek, intuitive graphical interface that makes AlphaFold3 accessible to everyone. No more command-line hassles - just point, click, and predict.

Demo site (generate input JSON files ONLY)

Usable visualization site (fully usable)

Table of Contents

✨ Key Features

Installation & Setup

  • GUI Installer: Step-by-step visual installation wizard
  • Auto-Configuration: Handles environment setup and dependencies

Core Functionality

  • Visual Workflow: Clean, modern interface for job configuration
  • Multi-Entity Support: Process proteins, RNA, DNA, and ligands
  • Advanced Options: Customize MSA, templates, and modifications

Execution & Control

  • 🚀 Integrated Pipeline: Direct AlphaFold3 execution from GUI
  • 🖥️ Live Monitoring: Real-time process tracking and console output
  • 🧩 Batch Processing: Python API for automated predictions

🌟 New Features!

  • AlphaFold 3 Output Analysis System: Automatically analyze and visualize results with customizable visualizations and generate detailed PDF reports for streamlined insights.
  • Enhanced Visualization Features (New in this fork):
    • Multiple visualization styles (cartoon, stick, line, sphere)
    • Flexible coloring schemes (confidence, chain, secondary structure, rainbow)
    • Interactive residue selection and chain sequence viewer
    • Advanced export capabilities for scientific and 3D printing formats
    • Improved PAE matrix visualization and confidence metrics display

Prerequisites

Before using AFusion, ensure that you have the following:

  1. 🐳 Docker Installed: Docker is required to run AlphaFold 3. Install Docker from the official website.

  2. 🧬 AlphaFold 3 Installed: AFusion requires AlphaFold 3 to be installed and set up on your system. Follow the installation instructions provided in the AlphaFold 3 GitHub Repository to deploy AlphaFold 3. Or you can run step-by-step GUI by:

    afusion install

  3. 🐍 Python 3.10 or Higher: AFusion is built with Python and requires Python 3.10 or higher.

Installation and Running

  1. Install AFusion

    Run the following command in your terminal to install AFusion:

    pip install afusion

  2. Run AFusion GUI

    After installation, you can start AFusion by running:

    afusion run

    This will launch the AFusion graphical user interface (GUI) in your default web browser.

Please Note:

  • 🧬 AlphaFold 3 Installation: Ensure you have correctly installed AlphaFold 3, including model parameters and required databases, following the AlphaFold 3 Installation Guide.

  • ⚙️ Docker Configuration: After installing Docker, make sure it is running properly and that your user has permission to execute Docker commands.

  • 📦 Streamlit is Included in Dependencies: AFusion's installation will automatically install all required dependencies, including Streamlit. There's no need to install it separately.

Usage

Launching AFusion

  1. 🚀 Start the Streamlit App

    From the project directory, run:

    afusion run

  2. 🌐 Access the Application

    • The application will launch, and Streamlit will provide a local URL (e.g., http://localhost:8501).
    • Open the provided URL in your web browser to access AFusion.

Using the GUI

Find more about input in here.

1. Welcome Page

  • 👋 Logo and Introduction: You'll see the AFusion logo and a brief description.
  • 📑 Navigation Sidebar: Use the sidebar on the left to navigate to different sections of the app.

2. Job Settings

  • 🏷️ Job Name: Enter a descriptive name for your job.
  • 🔢 Model Seeds: Provide integer seeds separated by commas (e.g., 1,2,3).

3. Sequences

  • 🔬 Number of Entities: Select how many entities you want to add (Proteins, RNA, DNA, Ligand).
  • 📋 Entity Details: For each entity:
    • ⚛️ Entity Type: Select the type (Protein, RNA, DNA, Ligand).
    • 🆔 Entity ID: Provide an identifier for the entity.
    • 🧬 Sequence Input: Enter the sequence information.
    • ✏️ Modifications: Optionally add modifications with their types and positions.
    • 📂 MSA Options: Choose MSA generation options and provide MSA data if applicable.
    • 📜 Templates: Optionally add template data with mmCIF content and indices.

4. Bonded Atom Pairs (Optional)

  • 🔗 Add Bonds: Check the box to add bonded atom pairs.
  • ⚛️ Define Bonds: For each bond, provide details for the first and second atoms, including entity IDs, residue IDs, and atom names.

5. User Provided CCD (Optional)

  • 📜 User CCD Input: Paste or enter custom CCD data in mmCIF format.

6. Generated JSON

  • 📄 Review JSON Content: The application generates the JSON input file based on your entries. You can review it here.

7. AlphaFold 3 Execution Settings

  • 🗂️ Paths Configuration:

    • 📁 AF Input Path: Specify the path to the AlphaFold input directory (e.g., /home/user/af_input).
    • 📂 AF Output Path: Specify the path to the output directory (e.g., /home/user/af_output).
    • 📂 Model Parameters Directory: Provide the path to the model parameters directory.
    • 📂 Databases Directory: Provide the path to the databases directory.

  • ⚙️ Execution Options:

    • 🏗️ Run Data Pipeline: Choose whether to run the data pipeline (CPU-intensive).
    • 💻 Run Inference: Choose whether to run inference (requires GPU).

8. Run AlphaFold 3

  • 💾 Save JSON File: Click the "Save JSON File" button to save the generated JSON to the specified input path.
  • ▶️ Run AlphaFold 3 Now: Click the "Run AlphaFold 3 Now ▶️" button to execute the AlphaFold 3 prediction using the Docker command.
    • 🔧 Docker Command: The exact Docker command used is displayed for your reference.
    • 📊 Command Output: Execution output is displayed within the app for monitoring.

Documentation

  • Full Documentation in here

ToDo

  • 📄 Bulid Documentation: Tutorial for using the AFusion API in Python scripts for batch predictions.
  • ♻️ Refactor Code and Publish to PyPI: Refactor the project code for improved modularity and maintainability, and publish the latest version to PyPI for easy installation.
  • 🔗 Develop AlphaFold result analysis system: Design and implement a comprehensive analysis pipeline for AlphaFold prediction results.
    • Update the system.
  • ⚛️ Preset Common Small Molecules & Metal Ions: Add a dedicated section for quick access to commonly used small molecules and metal ions.
  • 🛠️ New Tool for Chemical Small Molecules: Develop a new tool to handle and model chemical small molecules, supporting seamless integration into the prediction pipeline.
  • 🖥️ Add Console Output: Implement a backend console for output to track processes and debug more effectively.
  • 🧩 Create API for Batch Predictions: Develop a standalone function API to allow users to perform batch predictions with afusion in Python scripts.
  • 🧭 Create Guided Installation GUI: To simplify the installation process.
  • 🎨 Enhanced Visualization Features: Implement advanced visualization capabilities with multiple styles and export options.

Screenshots

Visualization GUI Interface

Click to view screenshot image

Installation GUI Interface

Click to view screenshot

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CLI Output

Click to view screenshot

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License

This project is licensed under the GPL3 License - see the LICENSE file for details.

Acknowledgements

  • Original Project: Created by Hanziwww
  • Visualization Enhancements: This fork includes additional visualization features while maintaining the core functionality of the original project
  • Enhanced Version: Maintained by Shivp1413
  • AlphaFold 3: This GUI is designed to work with AlphaFold 3 by DeepMind
  • Streamlit: AFusion is built using Streamlit
  • Contributors: Thanks to all contributors to the original project!

If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.

Happy Folding! 🧬