DOI: 10.5061/dryad.4b8gthtmf
title: Data from: Binding position dependent modulation of smoothened activity by Cyclopamine

description: Cyclopamine is a natural alkaloid that is known to act as an agonist when
 it binds to the Cysteine Rich Domain (CRD) of the Smoothened receptor and
 as an antagonist when it binds to the Transmembrane Domain (TMD). To study
 the effect of cyclopamine binding to each binding site experimentally,
 mutations in the other site are required. Hence, simulations are critical
 for understanding the WT activity due to binding at different sites.
 Additionally, there is a possibility that cyclopamine could bind to both
 sites simultaneously especially at high concentration, the implications of
 which remain unknown. We performed three independent sets of simulations
 to observe the receptor activation with cyclopamine bound to each site
 independently (CRD, TMD) and bound to both sites simultaneously. Using
 multi-milliseconds long aggregate MD simulations combined with Markov
 state models and machine learning, we explored the dynamic behavior of
 cyclopamine's interactions with different domains of WT SMO. A higher
 population of the active state at equilibrium, a lower activation free
 energy barrier of ~ 2 kcal/mol, and expansion of the hydrophobic tunnel to
 facilitate cholesterol transport agrees with the cyclopamine's
 agonistic behavior when bound to the CRD of SMO. A higher population of
 the inactive state at equilibrium, a higher free energy barrier of ~ 4
 kcal/mol and restricted the hydrophobic tunnel to impede cholesterol
 transport showed cyclopamine's antagonistic behavior when bound to
 TMD. With cyclopamine bound to both sites, there was a slightly larger
 inactive population at equilibrium and an increased free energy barrier (~
 3.5 kcal/mol). The tunnel was slightly larger than when solely bound to
 TMD, and showed a balance between agonism and antagonism with respect to
 residue movements exhibiting an overall weak antagonistic effect.
description: Cyclopamine (CYC), a steroidal molecule, acts as an agonist or an
 antagonist for the human Smoothened protein (SMO), depending on where it
 binds. Here, we investigate the mechanism behind this process, and try to
 explain the behavior of CYC bound to different sites in
 SMO. <br><br>Data was collected for each bound pose:
 Cyclopamine bound to the orthesteric site present in
 the Cysteine Rich Domain (CRD) of SMO. (CRD-CYC)
 Cyclopamine bound to the allosteric site present in the
 Transmembrane Domain (TMD) of SMO. (TMD-CYC)
 Cyclopamine bound to both the orthosteric and allosteric sites
 (CRD and TMD) of SMO. (Dual-CYC) For each
 system, two starting points were set up as initial systems - corresponding
 to Active and Inactive SMO. In total, 6 systems were simulated for a total
 aggregate time of 3 ms of unbiased all-atom simulations. On this Dryad
 repository, we are uploading the collective variable pickle files
 calculated from the simulated trajectories, which were then used to
 analyse the dynamics of CYC-bound SMO. The stripped MD trajectories are
 available publicly, via Box (https://uofi.app.box.com/s/4g3xmumfmesb68y7tb0fn8wvhvycylrf). The codes used to process the trajectories and the calculated observables are available on Github (https://github.com/ShuklaGroup/SMO_CYC).<br>Here, we are also providing additional numpy files that were calculated for the MSM construction - the trajectory weights used to reweigh the entire ensemble. <br><br>Molecular Simulations were performed using OpenMM 7.7, and the CHARMM36m force field was used for the simulations.<br><br>This work has been performed by Kihong (Max) Kim, Prateek Bansal & Diwakar Shukla at the University of Illinois Urbana-Champaign.
description: # Binding Position Dependent Modulation of Smoothened Activity by
 Cyclopamine
 [https://doi.org/10.5061/dryad.4b8gthtmf](https://doi.org/10.5061/dryad.4b8gthtmf) The following is an explanation of the overall dataset submitted to this repository: 1. Stripped_Parm.tar.gz (2.69 MB): parameter files used for the simulations. Files are in AMBER22 format. 2. Dtraj_Weights.tar.gz (15.57 MB): Trajectory weights obtained after MSM construction. These were then used to reweigh the ensembles shown in Figures 3, 4, 5, 6. * To construct the MSM, a set of 57 distances was chosen (described in the manuscript). time-lagged Independent Component Analysis (tICA) was performed on these set of distances, which uses a linear combination of the 57 distances to reduce the dimensions, where the first dimension represented the slowest kinetic process (activation) observed in the system. Then, k-means clustering was performed on the tIC dimensions and an MSM was constructed by optimizing the VAMP2 score after choosing the optimal number of clusters, number of tICs and MSM lagtime. * Code used to construct the MSM and get the weights is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Supplementary/msm.py). 3. PDB_files.tar.gz (10.35 MB): The starting points used for the simulations. 4. PDB_Distances_57.tar.gz (1.66 KB): The 57 distances used to construct the MSM were calculated for the starting points to refer to them in Figures 3, 4, 5. 5. WGM_plot_CRD.tar.gz (1.41 GB): .npy files used to plot Fig. 3B in the manuscript. These files contain data of collective variables that were used to explain the function of the WGM motif in SMO activation, when CYC is bound to the CRD of SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure3/distance_calculation_57.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure3/WGM_plot.py). 6. WGM_plot_Daul.tar.gz (2.28 GB): .npy files used to plot Fig. 3D in the manuscript. These files contain data of collective variables that were used to explain the function of the WGM motif in SMO activation, when CYC is bound to both sites - the CRD and the TMD of SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure3/distance_calculation_57.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure3/WGM_plot.py). 7. WGM_plot_TMD.tar.gz (2.26 GB): .npy files used to plot Fig. 3F in the manuscript. These files contain data of collective variables that were used to explain the function of the WGM motif in SMO activation, when CYC is bound to the TMD of SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure3/distance_calculation_57.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure3/WGM_plot.py). 8. DREnpy_CRD.tar.gz (98.79 MB): .npy files used to plot Fig. 4B in the manuscript. These files contain data of collective variables that were used to explain the function of the DRE lock in SMO activation, when CYC is bound to the CRD of SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure4/distance_calculation_DRE.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure4/DRE_plot.py). 9. DREnpy_Dual.tar.gz (160.05 MB): .npy files used to plot Fig. 4D in the manuscript. These files contain data of collective variables that were used to explain the function of the DRE lock in SMO activation, when CYC is bound to both sites - the CRD and the TMD of SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure4/distance_calculation_DRE.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure4/DRE_plot.py). 10. DREnpy_TMD.tar.gz (157.60 MB): .npy files used to plot Fig. 4F in the manuscript. These files contain data of collective variables that were used to explain the function of the DRE lock in SMO activation, when CYC is bound to the TMD of SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure4/distance_calculation_DRE.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure4/DRE_plot.py). 11. extracted_frames.tar.gz (9.60 GB): .pdb files representing sampled from the MSM for each ensemble. These PDBs are representative of the Active, Inactive and Intermediate states observed during the simulations for each ensemble. These were then used to compute the tunnel profiles shown in Fig. 6 of the manuscript. 12. Tunnel_Files.tar.gz (104.95 MB): .npy files containing the tunnel profiles shown in Fig. 6 of the manuscript. These files contain data that enables us to differentiate the tunnel radii when cyclopamine is bound to different binding sites in SMO. * The collective variables were calculated using the script available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure6/make_input_files.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure6/tunnel_plot.py). 13. Random_Forest_Files.tar.gz (930.36 MB): .npy files used to train the Random Forest Classifier used to distinguish between ensembles. Methodology explained in the manuscript. * The model training code is available [here.](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure7/Random_Forest.py) 14. Angle_Files.tar.gz (1.13 MB) : List of .npy files used to plot the kernel density plot for Figure 7 of the paper. These files contain data of collective variables that distinguish the three ensembles described in the paper. * The code to calculate these metrics is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure7/compute_angle.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure7/plot_angle.py). 15. fig2c_data.pkl (0.8kb) and alchemical_plot_data_fig2b.pkl (0.4kb) : List of files used to make Fig 2b and Fig 2c for the paper. These files contain data of the alchemical free energies and the equilibrium populations. * The code to calculate these metrics is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure7/compute_angle.py). The code used to plot these .npy files is available [here](https://github.com/ShuklaGroup/SMO_CYC/blob/main/Figure7/plot_angle.py). 16. alchemical_data.tar.gz: (1.95 GB): This contains the 5 datasets used to compute the alchemical free energies presented in Fig. 2b of the study. The tarball contains 5 folders, each containing the dHdl.xvg files used to compute the alchemical free energies.  * The code to compute the free energies from these files is available [here](https://github.com/MobleyLab/alchemical-analysis). Links to other publicly accessible locations of the data (Box link, total size &gt; 800 GB): * [https://uofi.box.com/s/4g3xmumfmesb68y7tb0fn8wvhvycylr](https://uofi.box.com/s/4g3xmumfmesb68y7tb0fn8wvhvycylr) ## Code/Software Code used to analyze the trajectories and construct the Markov Model is openly available at [https://github.com/ShuklaGroup/SMO_CYC/](https://github.com/ShuklaGroup/SMO_CYC/)