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ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Water migration through enzyme tunnels is sensitive to the choice of explicit water model (AldO + CYP2D6)

Authors: Szleper, Katarzyna; Tanriver, Gamze; Góra, Artur;

Water migration through enzyme tunnels is sensitive to the choice of explicit water model (AldO + CYP2D6)

Abstract

This repository contains data for the alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6). Data underpinning analyses of haloalkane dehalogenase DhaA are available from the related repository: 10.5281/zenodo.11489893. Content: AldO.tar.gz: tt_conda.yml -> conda environment used for the calculations. Usage : conda env create -f tt_conda.yml conda activate tt_conda.yml 01_MD_simulations -> the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs). ├── 01_MD_simulations│ ├── 01_inputs│ │ ├── opc│ │ │ ├── TCG_d1.0_o1.6│ │ │ └── TCG_d2.0_o1.9│ │ ├── scripts│ │ │ ├── 00_prepare_model.sh│ │ │ ├── 01_minimization.sh│ │ │ ├── 02_equilibration.sh│ │ │ └── 03_production.sh│ │ ├── tip3p│ │ │ ├── TCG_d1.0_o1.6│ │ │ └── TCG_d2.0_o1.9│ │ └── tip4pew│ │ ├── TCG_d1.0_o1.6│ │ └── TCG_d2.0_o1.9│ ├── 02_outputs│ │ ├── opc│ │ │ ├── TCG_d1.0_o1.6│ │ │ └── TCG_d2.0_o1.9│ │ ├── tip3p│ │ │ ├── TCG_d1.0_o1.6│ │ │ └── TCG_d2.0_o1.9│ │ └── tip4pew│ │ ├── TCG_d1.0_o1.6│ │ └── TCG_d2.0_o1.9│ ├── 03_analysis│ │ ├── rmsd_opc.csv│ │ ├── rmsd_tip3p.csv│ │ ├── rmsd_tip4pew.csv│ │ ├── rmsf_opc.csv│ │ ├── rmsf_tip3p.csv│ │ └── rmsf_tip4pew.csv│ └── readme.txt02_caver -> results of CAVER calculations, organized by models and TCGs. ├── 02_caver│ ├── config_files│ │ ├── calculate_tunnels.txt│ │ └── clustering.txt│ ├── opc│ │ ├── TCG_d1.0_o1.6│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ ├── readme.txt│ ├── tip3p│ │ ├── TCG_d1.0_o1.6│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── tip4pew│ ├── TCG_d1.0_o1.6│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── TCG_d2.0_o1.9│ ├── 1│ ├── 2│ ├── 3│ ├── 4│ └── 5 03_aquaduct -> results of AQUA-DUCT calculations, organized by models and TCGs. ├── 03_aquaduct│ ├── opc│ │ ├── TCG_d1.0_o1.6│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ ├── readme.txt│ ├── tip3p│ │ ├── TCG_d1.0_o1.6│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── tip4pew│ ├── TCG_d1.0_o1.6│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── TCG_d2.0_o1.9│ ├── 1│ ├── 2│ ├── 3│ ├── 4│ └── 5 04_transport_tools -> the results of TransportTools and analysis done from TransportTools results. ├── 04_transport_tools│ ├── overall_results│ │ ├── config.in│ │ ├── data│ │ │ ├── exact_matching_analysis│ │ │ └── super_clusters│ │ ├── statistics│ │ │ ├── 1-initial_tunnels_statistics_bottleneck_residues.txt│ │ │ ├── 1-initial_tunnels_statistics.txt│ │ │ ├── 2-filtered_tunnels_statistics_bottleneck_residues.txt│ │ │ ├── 2-filtered_tunnels_statistics.txt│ │ │ ├── 3-initial_events_statistics_bottleneck_residues.txt│ │ │ ├── 3-initial_events_statistics.txt│ │ │ ├── 4-filtered_events_statistics_bottleneck_residues.txt│ │ │ ├── 4-filtered_events_statistics.txt│ │ │ └── comparative_analysis│ │ ├── transport_tools.log│ │ └── visualization│ │ ├── 1-visualize_initial_tunnels.py│ │ ├── 2-visualize_filtered_tunnels.py│ │ ├── 3-visualize_initial_events.py│ │ ├── 4-visualize_filtered_events.py│ │ ├── comparative_analysis│ │ └── sources│ └── scripts│ ├── bottleneck_residues│ │ ├── 5_bottleneck_residues.py│ │ └── T1.png│ ├── presence_of_tunnels│ │ ├── 6_tunnels_before_assignment.py│ │ ├── before.png│ │ └── number_frames.pkl│ └── water_transport_analysis│ ├── 8_main_figure.py│ ├── figure2.png│ ├── output│ ├── percent_frames_events_AldO.png│ ├── transit_time_median.png│ ├── tt_events.png│ └── water_per_frame_AldO.png CYP2D6.tar.gz: tt_conda.yml -> conda environment used for the calculations. Usage : conda env create -f tt_conda.yml conda activate tt_conda.yml 01_MD_simulations -> the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs). ├── 01_MD_simulations│ ├── 01_inputs│ │ ├── opc│ │ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ ├── scripts│ │ │ ├── 01_minimization_heating_CPU_ARES_prep.sh│ │ │ ├── 02_equilibration_GPU_ARES_prep.sh│ │ │ ├── 03_production_ARES.sh│ │ │ └── prepare_model_3tbg.sh│ │ ├── tip3p│ │ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ └── tip4pew│ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ ├── 02_outputs│ │ ├── opc│ │ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ ├── tip3p│ │ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ └── tip4pew│ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ ├── 03_analysis│ │ ├── rmsd_opc.csv│ │ ├── rmsd_tip3p.csv│ │ ├── rmsd_tip4pew.csv│ │ ├── rmsf_opc.csv│ │ ├── rmsf_tip3p.csv│ │ └── rmsf_tip4pew.csv│ └── readme.txt 02_caver -> results of CAVER calculations, organized by models and TCGs. ├── 02_caver│ ├── config_files│ ├── opc│ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ ├── readme.txt│ ├── tip3p│ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── tip4pew│ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ ├── 1│ ├── 2│ ├── 3│ ├── 4│ └── 5 03_aquaduct -> results of AQUA-DUCT calculations, organized by models and TCGs. ├── 03_aquaduct│ ├── opc│ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ ├── readme.txt│ ├── tip3p│ │ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ │ ├── 1│ │ │ ├── 2│ │ │ ├── 3│ │ │ ├── 4│ │ │ └── 5│ │ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── tip4pew│ ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1│ │ ├── 1│ │ ├── 2│ │ ├── 3│ │ ├── 4│ │ └── 5│ └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1│ ├── 1│ ├── 2│ ├── 3│ ├── 4│ └── 5 04_transport_tools -> the results of TransportTools and analysis done from TransportTools results. ├── 04_transport_tools│ ├── overall_results│ │ ├── config.in│ │ ├── data│ │ │ ├── exact_matching_analysis│ │ │ └── super_clusters│ │ ├── statistics│ │ │ ├── 1-initial_tunnels_statistics_bottleneck_residues.txt│ │ │ ├── 1-initial_tunnels_statistics.txt│ │ │ ├── 2-filtered_tunnels_statistics_bottleneck_residues.txt│ │ │ ├── 2-filtered_tunnels_statistics.txt│ │ │ ├── 3-initial_events_statistics_bottleneck_residues.txt│ │ │ ├── 3-initial_events_statistics.txt│ │ │ ├── 4-filtered_events_statistics_bottleneck_residues.txt│ │ │ ├── 4-filtered_events_statistics.txt│ │ │ └── comparative_analysis│ │ ├── transport_tools.log│ │ └── visualization│ │ ├── 1-visualize_initial_tunnels.py│ │ ├── 2-visualize_filtered_tunnels.py│ │ ├── 3-visualize_initial_events.py│ │ ├── 4.pse│ │ ├── 4-visualize_filtered_events.py│ │ ├── comparative_analysis│ │ └── sources│ └── scripts│ ├── bottleneck_analyses│ │ ├── 5_bottleneck_residues.py│ │ ├── Ch2B-F.png│ │ ├── Ch2C.png│ │ └── ChS.png│ ├── presence_of_tunnels│ │ ├── 6_tunnels_before_assignment.py│ │ ├── before.png│ │ └── number_frames.pkl│ └── water_transport_analysis│ ├── 8_main_figure.py│ ├── figure2_Ch2B-Ch2F.png│ ├── figure2_Ch2C.png│ ├── figure2_Ch2S.png│ ├── output│ ├── percent_frames_events_Ch2B-Ch2F.png│ ├── percent_frames_events_Ch2C.png│ ├── percent_frames_events_ChS.png│ ├── transit_time_median_Ch2B-Ch2F.png│ ├── transit_time_median_Ch2C.png│ ├── transit_time_median_ChS.png│ ├── tt_events_Ch2B-Ch2F.png│ ├── tt_events_Ch2C.png│ ├── tt_events_Ch2S.png│ ├── water_per_frame_Ch2B-Ch2F.png│ ├── water_per_frame_Ch2C.png│ └── water_per_frame_ChS.png

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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