DOI: 10.5061/dryad.76hdr7t4w
title: Multiple modes of cholesterol translocation in the human Smoothened receptor

description: Smoothened (SMO), a member of the G Protein-Coupled Receptor superfamily,
 mediates Hedgehog signaling and is linked to cancer and birth defects. SMO
 responds to accessible cholesterol in the ciliary membrane, translocating
 it via a longitudinal tunnel to its extracellular domain. Reaching a
 complete mechanistic understanding of the cholesterol translocation
 process would help in the development of cancer therapies. Competing
 hypotheses based on available structures support entry of cholesterol from
 outer and inner membrane leaflets, but the exact mechanism of
 translocation remains unclear. Using atomistic molecular dynamics
 simulations (∼2 millisecond simulations) and biochemical assays of SMO
 mutants, we assess the energetic feasibilities of proposed hypotheses. We
 show that the energetic barriers for cholesterol translocation from either
 leaflets are comparable. Mutagenesis experiments and complementary
 simulations of SMO mutants validate the role of critical amino acid
 residues along the translocation pathways. Our data suggests that
 cholesterol can take either pathway to enter SMO, thus explaining
 contradictory experimental observations in literature. Thus, our results
 illuminate the energetics and provide a first molecular description of
 cholesterol translocation in SMO.
description: Dataset was collected using Long-Timescale Molecular Dynamics
 Simulations (MD). Simulations were started from four starting points that
 consisted of Smoothened (SMO) protein embedded in a membrane. Each
 starting point contained the cholesterol at a different site in SMO.
 Simulations were performed in an adaptive manner, consisting of multiple
 rounds of sampling with hundreds of trajectories run in parallel for each
 round. After every round of sampling, we gathered the entire dataset until
 that point, clustered the trajectory frames according to metrics that
 would differentiate between the different stages of cholesterol transport.
 Then simulations were seeded from clusters with the least population, and
 the next round of simulations was run. In total, the entire dataset
 consists of 2 ms of unbiased simulations. The files deposited in this
 repository contain the .npy files computed from each MD trajectory, which
 were used to plot the free energy landscapes in the manuscript.
 Simulations were run using AMBER and OpenMM MD simulation
 engines. Another set of biased simulations were run
 with different SMO mutants to describe the overall transport process for
 cholesterol, and compared to WT SMO. The .pmf files generated by NAMD are
 submitted to this repository for easy replication. 
 Python was extensively used to analyse the dataset generated.
 This work has been performed by Prateek Bansal and Diwakar Shukla at the
 University of Illinois Urbana-Champaign. This Dryad
 dataset has been made available in compliance with the "minimum
 dataset"/source data requirement per the Data Availability guidelines
 by Nature Communications.
description: # Multiple modes of cholesterol translocation in the human Smoothened
 receptor
 [https://doi.org/10.5061/dryad.76hdr7t4w](https://doi.org/10.5061/dryad.76hdr7t4w) ## Description of the data and file structure #### The following is an explanation of the overall dataset submitted to this repository: The repository contains all the data (.npy files) that were used to plot the figures in the manuscript: [Energetics of cholesterol perception and translocation in the human Smoothened receptor](https://www.biorxiv.org/content/10.1101/2024.11.25.625241). The data presented here is being shared to increase reproducibility. ### Accessing Data within Files The data within any `.pkl` file presented in the dataset can be accessed using the `pickle` package, a part of the standard python library. For example: ``` import pickle all_distances = pickle.load(open('./totdist_2ms.pkl', 'rb')) #Imports all the distances used to make MSM ``` The data within any `.npy` file presented in the dataset can be accessed using the `numpy` package, a part of the standard python library. For example: ``` import numpy as np trajectory_distance = np.load('./p14960_run0_clone104_strip.npy') #Imports a particular .npy associated with an MD trajectory ``` The code can be edited to access the data within any `.pkl` or `.npy` file associated with this repository. ### Files and variables * Fig7_dryad.tar.gz (3.14 MB): Compressed .tar.gz with the data used to construct Figure 7 of the manuscript. The data was computed by sampling 10000 frames belonging to each minima (along the translocation pathway) from the MSM, using the microstates probabilities. The code used plotting the data is available [here](https://github.com/ShuklaGroup/Bansal_et_al_Cholesterol_Smoothened_2024/tree/main/Fig7). Here is a detailed explanation of the files and folders inside the .tar.gz: * `entered_protein_holeframes_to_trajframes.pkl` contains the trajectories and frames used to construct the hole plot for Fig 7a in the manuscript. * `entered_protein_x.npy` contains the x-coordinate data for the average plot radius for Fig 7a. * `entered_protein_y.npy` contains the y-coordinate data for the average plot radius for Fig 7a. * `bottom_holeframes_to_trajframes.pkl` contains the trajectories and frames used to construct the hole plot for Fig 7c in the manuscript. * `bottom_x.npy` contains the x-coordinate data for the average plot radius for Fig 7c. * `bottom_y.npy` contains the y-coordinate data for the average plot radius for Fig 7c. * `membrane_holeframes_to_trajframes.pkl` contains the trajectories and frames used to construct the hole plot for Fig 7e in the manuscript. * `membrane_x.npy` contains the x-coordinate data for the average plot radius for Fig 7e. * `membrane_y.npy` contains the y-coordinate data for the average plot radius for Fig 7e. * `left_holeframes_to_trajframes.pkl` contains the trajectories and frames used to construct the hole plot the case where the cholesterol is in the membrane. * `left_x.npy` contains the x-coordinate data for the average plot radius for the case where the cholesterol is in the membrane. * `left_y.npy` contains the y-coordinate data for the average plot radius for the case where the cholesterol is in the membrane. * `distI_refined_1p4ms_final.pkl` contains the names of the trajectories that were used to make fig. 7 (a, c, e) (Common Pathway) * `msmweights_200_95.pkl` MSM weights used to reweigh each Free Energy plot in Fig 7. * Fig2_dryad.tar.gz (12.15 GB): Compressed .tar.gz with the data used to construct Figure 2 of the manuscript. The plotting code is available [here](https://github.com/ShuklaGroup/Bansal_et_al_Cholesterol_Smoothened_2024/tree/main/Fig2). Here is a detailed explanation of the files and folders inside the .tar.gz: * The dataset contains all the .npy files used to plot Fig 2a and 2b from the manuscript. * `distI_2ms.pkl` contains the file names of all the trajectories simulated. * `totdist_2ms.pkl` contains the corresponding .npy files of the 89 distances used to construct the Markov State Model. The first distance (index 0) is the negative of the z-coordinate of the cholesterol being transported. * `weights_msm_200_95_300_23.pkl` contains the weights used to construct the final MSM weighted free energy plot. * `pkl_Fig2a_angle` contains the 11663 .npy files of the x- and y-coordinates of the cholesterol which undergoes translocation. * `pkl_Fig2b` contains the 11663 .npy files containing the angle of the cholesterol with the x-y plane (-180 to 180). * Fig4_dryad.tar.gz (12.55 GB): Compressed .tar.gz with the data used to construct Figure 4 of the manuscript. The plotting code is available [here](https://github.com/ShuklaGroup/Bansal_et_al_Cholesterol_Smoothened_2024/tree/main/Fig4). Here is a detailed explanation of the files and folders inside the .tar.gz: * The dataset contains all the .npy files used to plot Fig 4a and 4b from the manuscript. * `distI_2ms.pkl` contains the file names of all the trajectories simulated. * `totdist_2ms.pkl` contains the corresponding .npy files of the 89 distances used to construct the Markov State Model. The first distance (index 0) is the negative of the z-coordinate of the cholesterol being transported. * `weights_msm_400_80_300.pkl` contains the weights used to construct the final MSM weighted free energy plot. * `pkl_Fig4a_angle` contains the 12513 .npy files of the x- and y-coordinates of the cholesterol which undergoes translocation. * `pkl_Fig4b` contains the 12513 .npy files containing the angle of the cholesterol with the x-y plane (-180 to 180). - Smo_mutants_QPCR_data_dryad_formatted.xlsx (24.77 KB): Excel file containing the QPCR data plotted in Fig 3 and Fig 5 in the manuscript. There are three sheets in the file, each corresponding to the different pathways explored in the manuscript (Pathway 1, Pathway 2, Common Pathway). The following applies to all 3 sheets, `CRD pathway`, `TMDPathway1` and `TMDPathway2`. Below is a detailed explanation of each column in the excel sheet: * Column A (`Mutant`) represents the mutants tested. * Column B (`Condition`) represents the Condition at which each mutant was tested (unt = No Shh added, low shh = Low amount of Shh added, high shh = Saturating amount of Shh added). * Columns C-F (`Gli1, Gli2, Gli3, Gli4`): Raw quantitative PCR (qPCR) measurements of **Gli1** transcript levels from four replicates. Gli1 is a readout of Hedgehog pathway activity, higher values indicate greater pathway activation. * Columns G-J (`Gap1, Gap2, Gap3, Gap4`): qPCR measurements of **Gapdh** transcript levels from four replicates, serving as a normalization control for Gli1 expression. * Column K (`average gap`) : the mean of `gap1`, `gap2`, `gap3`, and `gap4`. * Column L (values): raw Gli1 expression values, adjusted against **Gapdh**. * Columns M-P (`dct1, dct2, dct3, dct4`) These represent the ΔCt values for **Gli1** relative to **Gapdh** for each replicate. * Columns Q-T (`abundance1, abun2, abun3, abun4`) The normalized **Gli1** expression levels calculated based on the ΔCt values, expressed as fold changes. * Column U (`average`) mean of the normalized **Gli1** expression levels. * Column V (`average untreated`) mean normalized **Gli1** expression level in untreated conditions. * Columns W-Z (`fc1, fc2, fc3, fc4`), highlighted in green: Fold changes (FC) in **Gli1** expression for each replicate under specific conditions (unt, low or high SHH) compared to untreated conditions. These are the values plotted in the manuscript. ## Code/software Code used to plot the figures in the paper is available [Github](https://github.com/ShuklaGroup/Bansal_et_al_Cholesterol_Smoothened_2024). ## Access information Other publicly accessible locations of the data: * All trajectories simulated for this manuscript (&gt; 2 TB) are available on [Box](https://uofi.box.com/s/zldrqparb39ax72c637amcxi43hv2yaq).