DNA Polymerase and SSB Interaction Analysis

This repository houses a comprehensive tools designed to investigate the interactions between DNA polymerase and Single-Strand Binding (SSB) protein at both single-molecule and bulk assay levels. The repository is structured into three primary modules, each addressing specific aspects of DNA-protein interactions:

1. Analyzing basepair-time traces
2. Analyzing the displacement of SSB by DNA polymerase
3. Analyzing real-time DNA primer extension data

Each module contains a detailed README file explaining the code structure, a Jupyter notebook demonstrating the analysis workflow, and exemple datasets for reproducibility purposes. For specific version requirements, Python dependencies, and quick-start guides, please refer to the README file within each module.

Table of Contents

1. Quick Start
2. Project Overview
   • 1. Change-point Detection using Single-Molecule Basepair-Time Traces
   • 2. Real-time Visualization of DNA Polymerase Displacing SSB
   • 3. Real-time DNA Primer Extension Assay: Analyzing SSB's Effect on DNA Polymerase
3. Roadmap
4. Contributing
5. Support and Contact
6. Citation
7. License
8. Acknowledgments

Quick Start

To quickly get started with this codebase, please follow the steps below to set up your environment and install all necessary dependencies. It is important to use Python 3.9 and a specific version of lumicks.pylake (0.8.1) to ensure compatibility.

Prerequisites

• Python 3.9: Make sure you have Python version 3.9 installed on your system.
• Git: For cloning the repository.
• Poetry (recommended): A tool for dependency management in Python projects. Install Poetry if you don't have it.

Installation Steps

1. Clone the Repository

   Open your terminal and run:

   git clone https://github.com/longfuxu/Interplay_Between_DNAPol_and_SSB.git
   cd Interplay_Between_DNAPol_and_SSB

2. Ensure Python 3.9 is Installed

   Verify that Python 3.9 is installed:

   python3.9 --version

   If not installed, download it from the official Python website or use a version manager like pyenv:

   # Install pyenv if not already installed
   curl https://pyenv.run | bash
   
   # Install Python 3.9.16
   pyenv install 3.9.16
   
   # Set local Python version to 3.9.16
   pyenv local 3.9.16

3. Set Up the Virtual Environment with Poetry

   Use Poetry to create a virtual environment using Python 3.9:

   # Ensure Poetry uses Python 3.9
   poetry env use python3.9
   
   # Install dependencies
   poetry install

   This will create a virtual environment and install all dependencies specified in pyproject.toml, including lumicks.pylake version 0.8.1.

4. Activate the Virtual Environment

   Activate the virtual environment created by Poetry:

   poetry shell

5. Verify the Installation

   Check that the correct versions of Python and lumicks.pylake are installed:

   python --version
   # Expected output: Python 3.9.x
   
   python -c "import lumicks.pylake; print(lumicks.pylake.__version__)"
   # Expected output: 0.8.1

6. Launch Jupyter Lab

   Navigate to the module you're interested in and start Jupyter Lab:

   jupyter lab

   Open the desired Jupyter Notebook and follow the instructions provided within.

Notes

• Python Version: It is crucial to use Python 3.9 as some dependencies may not be compatible with newer versions of Python.
• lumicks.pylake Version: The codebase requires lumicks.pylake version 0.8.1. Using a different version may lead to compatibility issues.
• Dependencies: All other dependencies are managed by Poetry and are specified in the pyproject.toml file.
• Operating System Compatibility: The code has been primarily tested on macOS and Linux. Windows users may need to adjust some commands accordingly.

Project Overview

1. Change-point Detection using Single-Molecule Basepair-Time Traces

This Jupyter notebook presents a streamlined yet robust pipeline for processing and interpreting DNA force-extension experiments conducted using optical tweezers. Specifically designed to handle raw experimental data in TDMS format, this notebook employs polymer physics models (WLC for dsDNA, and FJC for ssDNA) to extract crucial biophysical parameters. The analysis pipeline encompasses several key components, including visualization of force-extension curves, quantification of ssDNA/dsDNA percentages catalysis by DNAp, and identification and characterization of discrete steps in DNAp processing events.

2. Real-time Visualization of DNA Polymerase Displacing SSB

This module comprises three interconnected Jupyter notebooks, each addressing a specific aspect of the dynamic interplay between DNA polymerase and Single-Strand Binding (SSB) proteins. The first notebook, Processing of force measurement data, focuses on the analysis and interpretation of force measurements obtained from optical tweezers experiments. The second notebook, Observing DNA polymerase displacement of SSB in real time, correlates force measurements with fluorescence imaging data to visualize the real-time displacement of SSB by DNA polymerase. An optional third notebook, Image processing for specific datasets, provides advanced image processing techniques for specific datasets, enhancing the resolution and clarity of the DNA polymerase-SSB interaction visualization. All notebooks are designed to process data in .tdms format, ensuring compatibility and ease of use. When utilized in conjunction, these analytical tools offer comprehensive insights into the dynamics of DNA polymerase activity and its interaction with SSB. Each notebook includes a detailed walkthrough of the analysis process for reproducibility purpose.

3. Real-time DNA Primer Extension Assay: Analyzing SSB's Effect on DNA Polymerase

This Jupyter Notebook is dedicated to the quantitative analysis of DNA polymerase (Pol) activity and its associated exonuclease (exo) function within a real-time DNA primer extension assay. The primary objective is to elucidate the modulatory effects of Single-Strand Binding protein (SSB) on both the polymerization and exonuclease activities of the enzyme. The approach involves processing fluorescence intensity data and applying linear regression models to distinct segments of the fluorescence traces. This method enables the extraction of enzyme activity rates, expressed in Relative Fluorescence Units per second (RFU/s), and their correlation with SSB concentration.

Roadmap

• User Interface Development: We aim to develop a user-friendly interface to make these analytical tools accessible to researchers without coding experience.
• Methodological Enhancements: We are exploring the adoption of more advanced Python-centric methodologies for data analysis, such as the Bayesian changepoint detection for single-molecule analysis.
• High-Throughput Analysis: We are developing scripts to efficiently handle and process large-scale datasets.

Contributing

We welcome contributions to enhance and expand this project. Please fork the repository, make your changes, and submit a pull request. For contribution you can also contact Longfu Xu or Prof. Gijs Wuite

Support and Contact

Please note that the code in this repository is custom written for internal lab use and still may contain bugs. For questions, support, or feedback, please contact Dr. Longfu Xu at longfu2.xu[at]gmail.com.

Citation

Xu, L. (2023). Grab, manipulate and watch single DNA molecule replication. [PhD-Thesis - Research and graduation internal, Vrije Universiteit Amsterdam]. https://doi.org/10.5463/thesis.424

License

This project is licensed under MPL-2.0 license. See LICENSE file for more details.

Acknowledgments

All codes listed in this repository are developed by Dr. Longfu Xu (longfuxu.com) during the PhD work in Gijs Wuite Lab. Special thanks to all contributors and supporters of this project.