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AI Structure Prediction for Protein Complexes

This repository contains the code and documentation for an MSc-level bioinformatics project focused on predicting the structure of protein complexes using advanced computational tools like AlphaFold2. The project aims to accurately model protein-ligand complexes, specifically targeting the p300 protein and its interactions with various transcription factors.

Project Overview

Protein-ligand complexes are crucial for cellular functions, and understanding their structures is key to deciphering biological processes, drug discovery, and treating various diseases. This project leverages computational predictions, experimental data, and collaborative approaches to improve the accuracy of these models. Understanding how these interactions, particularly involving intrinsically disordered regions, drive gene expression remains a significant challenge in structural biology. This study utilises advanced artificial intelligence-driven tools, including AlphaFold2 and AlphaFold3, to explore the structural dynamics of p300, focusing on its interactions with 1,639 human transcription factors. We hypothesised that conformational changes in the TAZ2 domain of p300 upon transcription factor binding could allosterically activate its histone acetyltransferase activity.

Objectives

  1. Conduct an initial screen of the 1,639 human TFs and determine which TF IDRs contain such ADs
  2. Predict TFs structure in complex with different domains of p300
  3. Determine which TFs contain ADs that directly interact with the TAZ2 domain of p300 to allosterically activate the HAT enzyme by TAZ2 domain remodelling.

Tools and Technologies

  • Python
  • Bash Shell Scripting
  • High-Performance Computing

Method

  1. Data Collection
  2. Data Pre-pocessing
  3. AlphaFold2 Structural Predictions
  4. Identification of Novel ADs

Getting Started

Prerequisites

  • Linux Mint OS
  • Python 3.x
  • Necessary computational tools (AlphaFold2)
  • High-performance computing resources
  1. Ensure you have access to computational tools:
    • Follow the installation instructions for AlphaFold2.

Acknowledgements

Thanks to the Professor Daniel Panne (University of Leicester) for supporting this research.

Contact

For any questions or inquiries, please contact Bismah Ghafoor at [email protected].