Conformational Change of Transcription Factors from Search to Specific Binding: A lac Repressor Case Study
DNA-binding proteins (DBPs) regulate and repair genes. It is therefore important to understand their dynamics. DBPs find their target sites by combining three-dimensional diffusion and one-dimensional scanning of the DNA. Here, we study the one-dimensional diffusion and DNA binding of the dimeric lac repressor (LacI) using coarse-grained molecular dynamic simulations and compare the results to experimental data. This study supports that linear diffusion along DNA combines tight rotation-coupled groove tracking and rotation-decoupled hopping, where the protein briefly dissociates and re-associates just a few base-pairs away. Tight groove tracking is crucial for target-site recognition, while hopping speeds up the overall search process. We show how the flexibility of LacI’s hinge regions ensures agility on DNA as well as faithful groove tracking. Based on our additional study of different encounter complexes, we argue that the conformational change in LacI and DNA occur simultaneously. The content of the database can be split into Starting structures, original trajectories, processed data, data for visualization, movies in 3D space (to be used in e.g. pymol) and code. The Starting structures contain .pdb files with all-atom models and .dat files with coarse-grained models. The trajectories can be found in the folders starting with diffusion_ for monomer, dimer and full-length LacI. Additionally there are trajectories of the different encounter complexes with straight and bent DNA and the two protein conformations. The processed data contains the position of the center of mass of the proteins recognition region relative to the DNA. The data is split into the different systems we studied: the full-length proteins, dimers and monomers of the search and recognition conformations as well as encounter complexes with A- and B-forms DNA. All these systems have been studied at different salt concentrations. The code CG-analysis-rackham contains the code that was used for plotting the data for the figure in the publication as it was downloaded from github on November 22 2022. This code contains jupyter notebooks that analyse the processed data and produce the figures in the publication. It also contains pipeline_trajectory_analysis which produces the processed data from the trajectories. The processed data contains the position of the protein relative to the DNA (position along and around the DNA and distance from the DNA), which can be obtained from the trajectory using the Spiral package contained in the pipeline_trajectory_analysis folder and the Ex_spiral1.py (https://github.com/mallu2/CG_analysis/blob/rackham/pipeline_trajectory_analysis/Ex_spiral1.py) script of CG_analysis-rackham. The preprosessed trajetcory data can the be plotted with the notebook plotting_CG_sim.ipynb (Figure 2 of the paper). The diffusion can be analysed and plotted with msd_diffusion_coefficient.ipynb (Figure 3 of the paper). The trajectory data can also be split into 1D and 3D diffusion and into groove tracking/sliding motions on the DNA with analysis_sliding_and_hopping.ipynb (Figure 4 of the paper). Interaction profiles of the protein on DNA can be plotted using interaction_profiles.ipynb (Fig. 5A). Finally different energies obtained from the simulation and bonds formed between protein and DNA of different conformations can be analysed using the script Ex_Bind_Occ.py (https://github.com/mallu2/CG_analysis/blob/rackham/pipeline_trajectory_analysis/Ex_Bind_Occ.py) and CG_energies_analysis.ipynb (Fig. 5 C and D). Each zip archive contains a README with further descriptions of the subfolder structure and the files contained within. The same goes for the code.
SNIC 2.0: Swedish National Infrastructure for Computing
The physics of genetic information processing
A genome wide approach to replication initiation
Structural biology (incl. macromolecular modelling)