zenodoopen

Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

00_Caver.tar.gz - Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA).  final_clustering ├── tunnel_custers # contains caver output files for individual tunnels clusters  │   ├── ... ├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters  │   ├── ... 01_CaverDock_Tunnels_Profile.tar.gz - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained.  # Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are: - calculations/*/caverdock.conf :  The config file input for caverdock calculation.  - calculations/*/DBE.pdbqt : Input file for the substrate DBE. - calculations/*/stripped_system*.pdbqt : Input file for the Protein/Receptor - calculations/*/stripped_system*.dsd : Tunnel discretization file. Notes:  - calculations/*/stripped_system*.pdb : PDB file for the tunnel.  02_Minimization_and_Equilibration.tar.gz -  Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations. 03_HTMD_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  04_HTMD_Cavity - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. 05_HTMD_Cavity_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  06_HTMD_Tunnels - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  07_MD-Analysis.tar.gz - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K.  # Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are: - Epoch_Convergence/epochs_dist_counts.csv :  Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs.  -  Distances/*/dist_s_r*.csv : Contains .csv file for the  frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  - RMSD_RMSF/*/*.csv : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program.             Example input: pytraj.rmsd(traj, mask='1-295@CA')             Example input: pytraj.rmsf(traj, mask=':1-295', options='byres') - COM_RMSF/.xlsx : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213) - Kinetics/kinetics.txt : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates. - Percentages/.csv : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å). - Tunnels_lengths/.csv : Contains tunnel_lengths.csv, Summary of tunnel lengths.xlsx files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in tunnel_lengths.csv and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the Summary of tunnel lengths.xlsx file.  - Sample_proportions/.csv : Contains sample_proportions.csv file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html  or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path  - *.py : Python scripts used to build and analysis Markov state models with use case and distances of ligand. - Generated_models/ : Contains models_rep[].dat files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below: ├── Cavity │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat ├── Cavity_Bulk │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat └── Tunnels     ├── model_rep1.dat     ├── model_rep2.dat     └── model_rep3.dat  08_TransportTools.tar.gz - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... - event.csv file contains the aggregated summary of events inferred from the 4-filtered_events_statistics.txt files of each results of respective schemes 09_MSM_states.tar.gz - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below:  ├── Bulk │   ├── rep1 # MSM states for replicate 1 │   ├── rep2 # MSM states for replicate 2 │   ├── rep3 # MSM states for replicate 3 ├── Cavity │   ├── rep1  │   ├── rep2  │   ├── rep3  ├── Cavity&Bulk │   ├── rep1  │   ├── rep2 │   ├── rep3 ├── Tunnels │   ├── rep1  │   ├── rep2 │   ├── rep3 10_MSM_fingerprints.tar.gz - Contains the Markov state models (MSMs) distances generated from repository dir 09_MSM_states consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package. - MSM_Distances/*/rep*/*.csv : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  11_ULS_clustering_and_transition_assignments.tar.gz - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files: 1. for the transition detection of DBE and the classification in  Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and  2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown. # the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels: ├── average_tunnel_utilization_per_scheme.png ├── average_tunnel_utilization.png ├── Bulk │   ├── Bulk_run_htmd_0_combined_df.csv │   ├── Bulk_run_htmd_0_transitions_counts.csv │   ├── Bulk_run_htmd_1_combined_df.csv │   ├── Bulk_run_htmd_1_transitions_counts.csv │   ├── Bulk_run_htmd_2_combined_df.csv │   └── Bulk_run_htmd_2_transitions_counts.csv ├── Bulk&Cavity │   ├── Cavity&Bulk_run_htmd_0_combined_df.csv │   ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_1_combined_df.csv │   ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_2_combined_df.csv │   └── Cavity&Bulk_run_htmd_2_transitions_counts.csv ├── Cavity │   ├── Cavity_run_htmd_0_combined_df.csv │   ├── Cavity_run_htmd_0_transitions_counts.csv │   ├── Cavity_run_htmd_1_combined_df.csv │   ├── Cavity_run_htmd_1_transitions_counts.csv │   ├── Cavity_run_htmd_2_combined_df.csv │   └── Cavity_run_htmd_2_transitions_counts.csv ├── parse_distances_msm.py ├── schemes_comparison_piechart_per_scheme.png └── Tunnels     ├── Tunnels_run_htmd_0_combined_df.csv     ├── Tunnels_run_htmd_0_transitions_counts.csv     ├── Tunnels_run_htmd_1_combined_df.csv     ├── Tunnels_run_htmd_1_transitions_counts.csv     ├── Tunnels_run_htmd_2_combined_df.csv     └── Tunnels_run_htmd_2_transitions_counts.csv

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 16
Reuse readiness: 8
Engagement: 0

Topics

Adaptive sampling kinetics ligand transport Molecular dynamics simulations protein-ligand seeding tunnels