Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Intrinsically disordered Prosystemin discloses biologically active repeat motifs

Dataset referred to "Intrinsically disordered Prosystemin discloses biologically active repeat motifs". Abstract: The in-depth studies over the years on the defence barriers by tomato plants have shown that the Systemin peptide controls the response to a wealth of environmental stress agents. This multifaceted stress reaction seems to be related to the intrinsic disorder of its precursor protein, Prosystemin (ProSys). Since latest findings show that ProSys has biological functions besides Systemin sequence, here we wanted to assess if this precursor includes peptide motifs able to trigger stress-related pathways. Candidate peptides were identified in silico and synthesized to test their capacity to trigger defence responses in tomato plants against different biotic stressors. Our results demonstrated that ProSys harbours several repeat motifs which triggered plant immune reactions against pathogens and pest insects. Three of these peptides were detected by mass spectrometry in plants expressing ProSys, demonstrating their effective presence in vivo. These experimental data shed light on unrecognized functions of ProSys, mediated by multiple biologically active sequences which may partly account for the capacity of ProSys to induce defense responses to different stress agents.

Exploring relationships between time of day and pollinator activity in the context of pesticide use - data

Pesticide exposure can be harmful to insect pollinators and the ecosystem services they provide. As pesticide guidelines warn against applying such products when pollinators are active, it is important to determine how pollinator activity changes with the time of day and when the most appropriate time is to spray these chemicals. We walked transects from sunrise to sunset in oilseed rape (Brassica napus L.) fields in Ireland to capture the abundance of honeybees, bumblebees, solitary bees, and hoverflies across daylight hours. We also recorded the activity of representative species from the three bee groups at their nests across similar time periods to compare with field observations. Peak pollinator abundance was in the mid-afternoon with fewer individuals in the early morning and late evening for all groups. At the nest, we observed patterns of activity that broadly reflected field abundance but indicated that bees are active earlier and later than those observed on the crop. However, there were differences between pollinator groups. Overall, honeybee and solitary bee abundance and activity were found to peak in the middle of the day, while bumblebee abundance and activity were more consistent throughout daylight hours. Hoverflies were relatively abundant in the morning and increased in number towards the late afternoon and early evening. Our results confirm current recommendations that pesticide application should be avoided in the middle of the day when pollinators are most active. However, the diversity of responses within and between pollinator groups to time of day should be accounted for when shaping guidelines, and clearly defining optimal pesticide application timings for end users is difficult and needs further consideration as it will vary between regions and crops. Further research should also explore how time impacts pesticide efficacy and exposure of pollinators to residues post-application to allow full evaluation of how practical and beneficial timing of application may be when aiming to protect pollinators from pesticide exposure.

opencc-by-4.0Mar 2024View details →

Code & data of the paper "Data sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data"

Code & data of the paper "Data sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data"

Figure2 in The Ecological Significance on Primate Activity in Kimbi-Fungom National Park, Northwest Region, Cameroon

Figure2. Weather condition and Animal activity

Figure7 in The Ecological Significance on Primate Activity in Kimbi-Fungom National Park, Northwest Region, Cameroon

Figure7. The landscape and animal activity

Figure1 in The Ecological Significance on Primate Activity in Kimbi-Fungom National Park, Northwest Region, Cameroon

Figure1. Kimbi Fungom National Park (KFNP) map

Figure6 in The Ecological Significance on Primate Activity in Kimbi-Fungom National Park, Northwest Region, Cameroon

Figure6. The primate activity and the forest vegetation type

Figure5 in The Ecological Significance on Primate Activity in Kimbi-Fungom National Park, Northwest Region, Cameroon

Figure5. The animal activity and the day-period

Figure4 in The Ecological Significance on Primate Activity in Kimbi-Fungom National Park, Northwest Region, Cameroon

Figure4. The vegetation type and seasonality

opencc-by-4.0Mar 2024View details →

Dataset of hydrogen Balmer line indexes for stellar chromospheric activity of solar-like stars based on the LAMOST spectroscopic surveys

This dataset consists of three catalogs of the data of the hydrogen Balmer line indexes of solar-like stars for stellar chromospheric activity based on the LAMOST Low-Resolution Spectroscopic Survey (LRS) and Medium-Resolution Spectroscopic Survey (MRS): (1) LRS spectral catalog, (2) MRS spectral catalog, and (3) stellar source catalog. These catalogs are stored as three CSV-format data files. The columns contained in the catalogs are described below. The observational and spectroscopic parameters of the spectral samples in the dataset are from LAMOST DR9 v2.0. Unavailable values in the dataset are marked as −9999.0.   (1) LRS spectral catalog (Balmer_line_indexes_LRS_spectral_catalog_LAMOST_DR9.csv) obsid - unique LAMOST observation identifier of LRS obsdate - date of LRS observation (UTC) uid - unique LAMOST source identifier ra - right ascension of stellar source in LRS catalog (unit: degree) dec - declination of stellar source in LRS catalog (unit: degree) sn_g - LRS g-band signal-to-noise ratio sn_r - LRS r-band signal-to-noise ratio teff - stellar effective temperature determined from LRS data (unit: K) teff_err - uncertainty of teff (unit: K) logg - stellar surface gravity determined from LRS data (unit: dex) logg_err - uncertainty of logg (unit: dex) feh - stellar metallicity determined from LRS data (unit: dex) feh_err - uncertainty of feh (unit: dex) rv - radial velocity determined from LRS data (unit: km/s) rv_err - uncertainty of rv (unit: km/s) L_halpha - Hα activity index of LRS data L_halpha_err - uncertainty of L_halpha L_hbeta - Hβ activity index of LRS data L_hbeta_err - uncertainty of L_hbeta L_hgamma - Hγ activity index of LRS data L_hgamma_err - uncertainty of L_hgamma L_hdelta - Hδ activity index of LRS data L_hdelta_err - uncertainty of L_hdelta   (2) MRS spectral catalog (Balmer_line_indexes_MRS_spectral_catalog_LAMOST_DR9.csv) obsid - unique LAMOST observation identifier of MRS obsdate - date of MRS observation (UTC) uid - unique LAMOST source identifier ra - right ascension of stellar source in MRS catalog (unit: degree) dec - declination of stellar source in MRS catalog (unit: degree) sn_B - MRS blue-band signal-to-noise ratio sn_R - MRS red-band signal-to-noise ratio teff - stellar effective temperature determined from MRS data (unit: K) teff_err - uncertainty of teff (unit: K) logg - stellar surface gravity determined from MRS data (unit: dex) logg_err - uncertainty of logg (unit: dex) feh - stellar metallicity determined from MRS data (unit: dex) feh_err - uncertainty of feh (unit: dex) rv_r0 - radial velocity determined from the red band data of MRS (unit: km/s) rv_r0_err - uncertainty of rv_r0 (unit: km/s) M_halpha – Hα activity index of MRS data M_halpha_err - uncertainty of M_halpha   (3) stellar source catalog (Balmer_line_indexes_stellar_source_catalog_LAMOST_DR9.csv) uid - unique LAMOST source identifier ra - right ascension of stellar source from LRS catalog (unit: degree) dec - declination of stellar source from LRS catalog (unit: degree) teff - stellar effective temperature from LRS catalog (unit: K) teff_err - uncertainty of teff (unit: K) logg - stellar surface gravity from LRS catalog (unit: dex) logg_err - uncertainty of logg (unit: dex) feh - stellar metallicity from LRS catalog (unit: dex) feh_err - uncertainty of feh (unit: dex) L_halpha - Hα activity index of LRS data L_halpha_err - uncertainty of L_halpha L_hbeta - Hβ activity index of LRS data L_hbeta_err - uncertainty of L_hbeta L_hgamma - Hγ activity index of LRS data L_hgamma_err - uncertainty of L_hgamma L_hdelta - Hδ activity index of LRS data L_hdelta_err - uncertainty of L_hdelta M_halpha - Hα activity index of MRS data M_halpha_err - uncertainty of M_halpha l_halpha - Hα absolute flux index of LRS data (unit: 1/Å) l_halpha_err - uncertainty of l_halpha (unit: 1/Å) l_hbeta - Hβ absolute flux index of LRS data (unit: 1/Å) l_hbeta_err - uncertainty of l_hbeta (unit: 1/Å) l_hgamma - Hγ absolute flux index of LRS data (unit: 1/Å) l_hgamma_err - uncertainty of l_hgamma (unit: 1/Å) l_hdelta - Hδ absolute flux index of LRS data (unit: 1/Å) l_hdelta_err - uncertainty of l_hdelta (unit: 1/Å) m_halpha - Hα absolute flux index of MRS data (unit: 1/Å) m_halpha_err - uncertainty of m_halpha (unit: 1/Å)

Code from: Effect of fluid elasticity on the emergence of oscillations in an active elastic filament

Many microorganisms propel through complex media by deformations of their flagella. The beat is thought to emerge from interactions between forces of the surrounding fluid, passive elastic response from deformations of the flagellum, and active forces from internal molecular motors. The beat varies in response to changes in the fluid rheology, including elasticity, but there is limited data on how systematic changes in elasticity alter the beat. This work analyzes a related problem with fixed-strength driving force: the emergence of the beating of an elastic planar filament driven by a follower force at the tip in a viscoelastic fluid. This analysis examines how the onset of oscillations depends on the strength of the force and viscoelastic parameters. Compared to a Newtonian fluid, it takes more force to induce instability in viscoelastic fluids, and the frequency of the oscillation is higher. The linear analysis predicts that the frequency increases with the fluid relaxation time. Using numerical simulations, the model predictions are compared with experimental data on frequency changes in bi-flagellated alga Chlamydomonas reinhardtii. The model shows the same trends in response to changes in both fluid viscosity and Deborah number and thus provides a possible mechanistic understanding of the experimental observations.

opencc-by-4.0Apr 2024View details →

Daily fraction of photosynthetically active radiation (fPAR), edaphic, and weather conditions from 20220410 to 20230409 in Kulen, Cambodia

<ol><li>Sub-title 1: Daily mean and standard deviation of Soil water content, soil temperature, soil saturation extracted electrical conductivity of evergreen forests, regrowth forests, and cashew plantations in Kulen, Cambodia. (File name: Edaphic_fPAR_Pub.txt)</li><li>Sub-title 2: Daily weather condition data from 20220410 to 20230409 in Kulen, Cambodia. (File name: Weather_Pub.txt)</li><li>Sub-title 3: Photosynthetically active radiation (PAR) data from 20220410 to 20230409 in Kulen weather station, Cambodia. (File name: PARa_Pub.txt)</li></ol>

Data from: Evidence for socially influenced and potentially actively coordinated cooperation by bumblebees

Cooperation is common in animals, yet the specific mechanisms driving collaborative behaviour in different species remain unclear. We investigated the proximate mechanisms underlying the cooperative behaviour of bumblebees in two different tasks, where bees had to simultaneously push a block in an arena or a door at the end of a tunnel for access to reward. In both tasks, when their partner's entry into the arena/tunnel was delayed, bees took longer to first push the block/door, compared to control bees that learned to push alone. In the tunnel task, just before gaining access to reward, bees were more likely to face towards their partner than expected by chance or compared to controls. These results show that bumblebees' cooperative behaviour is not simply a by-product of individual efforts but is socially influenced. We discuss how bees' turning behaviours, e.g. turning around before first reaching the door when their partner was delayed and turning back towards the door in response to seeing their partner heading towards the door, suggest active coordination at its simplest level – i.e. bumblebees may have a basic understanding of their partner's role in cooperative tasks. Future studies are needed to help determine the complexity and limitations of this understanding.

Blood lead levels in an endangered vulture decline following changes in hunting activity

Lead ammunition stands out as one of the most pervasive pollutants affecting wildlife. Its impact on bird populations has spurred efforts for the phase-out of leaded gunshots in several countries, although with varying scopes and applications. Ongoing and future policy changes require data to assess the effectiveness of adopted measures, particularly in the current context of biodiversity loss. Here, we assessed the long-term changes in blood lead (Pb) levels of Egyptian vultures from the Canary Islands, Spain, which have been severely affected by Pb poisoning over the past two decades. During this period, the reduction in hunting pressure and changes in legislation regarding firearms usage for small game hunting likely contributed to a decrease in environmental Pb availability. As anticipated, our results show a reduction in Pb levels, especially after the ban on wild rabbit hunting with shotguns since 2010. This effect was stronger in the preadult fraction of the vulture population. However, we still observed elevated blood Pb levels above the background and clinical thresholds in 5.6% and 1.5% of individuals, respectively. Our results highlight the positive impact of reducing the availability of Pb from ammunition sources on individual health. Nonetheless, the continued use of Pb gunshot remains an important source of poisoning, even lethal, mainly affecting adult individuals. This poses particular concerns for long-lived birds, compounding by potential chronic effects associated with Pb bioaccumulation. Our findings align with recent studies indicating insufficient reductions in Pb levels among European birds of prey, attributed to limited policy changes and their uneven implementation. We anticipated further reductions in Pb levels among Egyptian vultures with expanded restrictions on hunting practices, including a blanket ban on Pb shot usage across all small game species.

03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

opencc-by-4.0Dec 2023View details →

Design and high-throughput implementation of MALDI-TOF/MS-based assays for Parkin E3 ligase activity

Summary Parkinson’s disease (PD) is a progressive neurological disorder that manifests clinically as alterations in movement as well as multiple non-motor symptoms including but not limited to cognitive and autonomic abnormalities. Loss-of-function mutations in the gene encoding the ubiquitin E3 ligase Parkin are causal for familial and juvenile PD. Among several therapeutic approaches being explored to treat or improve PD patient’s prognosis, the use of small molecules able to reinstate or boost Parkin activity represents a potential pharmacological treatment strategy. A major barrier is the lack of high throughput platforms for the robust and accurate quantification of Parkin activity in vitro. Here we present two different and complementary Matrix Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF/MS) based approaches for the quantification of Parkin E3 ligase activity in vitro. Both approaches are scalable for high-throughput primary screening to facilitate the identification of Parkin modulators.

06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, run_adaptiveMD.py : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

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

05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...