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 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 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.

DNA结合蛋白（DBPs）调控与修复基因，因而探究其动态特性至关重要。DBPs通过结合三维扩散与一维DNA扫描定位其靶位点。本研究利用粗粒度分子动力学模拟，对二聚体乳糖阻遏蛋白（LacI）的一维扩散和DNA结合进行探讨，并将模拟结果与实验数据相对比。研究表明，沿DNA的线性扩散结合了紧密旋转耦合的槽道跟踪与旋转解耦的跳跃，其中蛋白在仅几个碱基对处短暂解离与重新结合。紧密的槽道跟踪对靶位点识别至关重要，而跳跃则加速了整体搜索过程。本研究揭示了LacI的铰链区域灵活性如何确保其在DNA上的敏捷性与槽道跟踪的忠实性。基于对不同遭遇复合物的进一步研究，我们认为LacI与DNA的构象变化是同步发生的。

数据库内容可分为起始结构、原始轨迹、处理数据、可视化数据、三维空间中的电影（例如，用于pymol）和代码。

起始结构包含所有原子模型的.pdb文件和粗粒度模型的.dat文件。

轨迹数据可在以diffusion_开头的文件夹中找到，包括单体、二聚体和全长LacI的轨迹，以及具有直线和弯曲DNA以及两种蛋白构象的不同遭遇复合物的轨迹。

处理数据包含蛋白质识别区域质心相对于DNA的位置。数据分为我们研究的不同系统：全长蛋白、搜索和识别构象的二聚体和单体以及具有A型和B型DNA的遭遇复合物。所有这些系统均在不同的盐浓度下进行了研究。

CG-analysis-rackham代码包含用于绘制出版物中图示的数据绘图代码，该代码于2022年11月22日从github下载。此代码包含分析处理数据并生成出版物中图示的jupyter notebooks，以及pipeline_trajectory_analysis，该部分生成从轨迹中获得的处理数据。

处理数据包含蛋白相对于DNA的位置（沿DNA和绕DNA的位置以及与DNA的距离），可使用pipeline_trajectory_analysis文件夹中包含的Spiral包和CG_analysis-rackham中的Ex_spiral1.py脚本来获得。

预处理轨迹数据可使用plotting_CG_sim.ipynb笔记本进行绘图（论文中的图2）。

扩散分析可用msd_diffusion_coefficient.ipynb进行（论文中的图3）。

轨迹数据还可以使用analysis_sliding_and_hopping.ipynb（论文中的图4）分为一维和三维扩散，以及DNA上的槽道跟踪/滑动运动。

使用interaction_profiles.ipynb（图5A）可以绘制蛋白在DNA上的相互作用图谱。

最后，使用Ex_Bind_Occ.py脚本和CG_energies_analysis.ipynb（图5 C和D）可以分析模拟中获得的不同能量以及不同构象的蛋白与DNA形成的键。

每个zip归档均包含README文件，其中进一步描述了子文件夹结构和包含的文件。对于代码也是如此。