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1,204 results for “Enzyme”
Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction
<p>Dataset produced for the Project PRELUDIUM19 2020/37/N/NZ2/00967 entitled: "Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction"</p> <p>The dataset counts with the three families of enzymes used: dehalogenase, aldehyde reductase and nitrilase.</p> <p>For each enzyme, the docked structures, docked parameters and scripts to analyze them further are present. Moreover, the protocol that derives geometric descriptors from docked structures is also present.</p> <p>This work was supported by the National Science Centre, Poland (grant no. 2020/37/N/NZ2/00967)</p>
03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. </p> <p># The forders are organized as:</p> <p>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.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p> <p> </p>
Discovery and characterization of non-canonical ubiquitin conjugating enzymes
<p>Ubiquitin conjugating enzymes (E2s) play a central role in the enzymatic cascade that leads to the attachment of ubiquitin to a substrate. This process, termed ubiquitylation is required to maintain cellular homeostasis and impacts almost all cellular process. By interacting with multiple E3 ligases, E2s dictate the ubiquitylation landscape within the cell. Since its discovery, ubiquitylation has been regarded as a post-translational modification (PTM) that specifically targets lysine side chains (canonical ubiquitylation). We used Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) Mass Spectrometry (MS), to discover and characterize a family of E2s that are instead able to conjugate ubiquitin to serine and/or threonine. We employed structural modelling and prediction tools to identify the key activity determinants that these E2s use to interact with ubiquitin as well as their substrates. Our results identify the missing E2s required for non-canonical ubiquitylation, highlight the versatility of ubiquitin modifications and challenge the view of ubiquitylation as an exclusively lysine specific PTM.</p>
06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, <em>run_adaptiveMD.py</em> : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p>
Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p><strong>00_Caver.tar.gz </strong>- 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). <br>final_clustering<br>├── tunnel_custers # contains caver output files for individual tunnels clusters <br>│ ├── ...<br>├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters <br>│ ├── ...</p> <p><strong>01_CaverDock_Tunnels_Profile.tar.gz</strong> - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained. <br># 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:<br>- <em>calculations/*/caverdock.conf</em> : The config file input for caverdock calculation. <br>-<em> calculations/*/DBE.pdbqt </em>: Input file for the substrate DBE.<br>- <em>calculations/*/stripped_system*.pdbqt </em>: Input file for the Protein/Receptor<br>- c<em>alculations/*/stripped_system*.dsd </em>: Tunnel discretization file. Notes: <br>- <em>calculations/*/stripped_system*.pdb </em>: PDB file for the tunnel. </p> <p><strong>02_Minimization_and_Equilibration.tar.gz</strong> - Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations.</p> <p><strong>03_HTMD_Bulk</strong> - 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. <br><strong>04_HTMD_Cavity</strong> - 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.<br><strong>05_HTMD_Cavity_Bulk</strong> -<strong> </strong>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. <br><strong>06_HTMD_Tunnels</strong> - 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. </p> <p><strong>07_MD-Analysis.tar.gz</strong> - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K. <br># 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:<br>- <em>Epoch_Convergence/epochs_dist_counts.csv</em> : 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. <br>- <em>Distances/*/dist_s_r*.csv</em> : 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:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM <br>- <em>RMSD_RMSF/*/*.csv</em> : 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')<br>- <em>COM_RMSF/.xlsx</em> : 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)<br>- <em>Kinetics/kinetics.txt</em> : 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.<br>- <em>Percentages/.csv</em> : 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).<br>- <em>Tunnels_lengths/.csv</em> : Contains <em>tunnel_lengths.csv</em>, <em>Summary of tunnel lengths.xlsx</em> files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in <em>tunnel_lengths.csv</em> 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 <em>Summary of tunnel lengths.xlsx</em> file. <br>- <em>Sample_proportions/.csv</em> : Contains <em>sample_proportions.csv</em> 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<br> or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path<br> - <em>*.py</em> : Python scripts used to build and analysis Markov state models with use case and distances of ligand.<br>- <em>Generated_models/</em> : Contains <em>models_rep[].dat</em> files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below:<br>├── Cavity<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>├── Cavity_Bulk<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>└── Tunnels<br> ├── model_rep1.dat<br> ├── model_rep2.dat<br> └── model_rep3.dat </p> <p><br><strong>08_TransportTools.tar.gz</strong> - 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<br>results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>- <em>event.csv</em> file contains the aggregated summary of events inferred from the <em>4-filtered_events_statistics.txt</em> files of each results of respective schemes</p> <p><strong>09_MSM_states.tar.gz</strong> - 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: <br>├── Bulk<br>│ ├── rep1 # MSM states for replicate 1<br>│ ├── rep2 # MSM states for replicate 2<br>│ ├── rep3 # MSM states for replicate 3<br>├── Cavity<br>│ ├── rep1 <br>│ ├── rep2 <br>│ ├── rep3 <br>├── Cavity&Bulk<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3<br>├── Tunnels<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3</p> <p><br><strong>10_MSM_fingerprints.tar.gz</strong> - Contains the Markov state models (MSMs) distances generated from repository dir <strong>09_MSM_states</strong> consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package.<br>- <em>MSM_Distances/*/rep*/*.csv</em> : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM </p> <p><br><strong>11_ULS_clustering_and_transition_assignments.tar.gz</strong> - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files:<br>1. for the transition detection of DBE and the classification in Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and <br>2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown.<br># the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels:<br>├── average_tunnel_utilization_per_scheme.png<br>├── average_tunnel_utilization.png<br>├── Bulk<br>│ ├── Bulk_run_htmd_0_combined_df.csv<br>│ ├── Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Bulk_run_htmd_1_combined_df.csv<br>│ ├── Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Bulk_run_htmd_2_combined_df.csv<br>│ └── Bulk_run_htmd_2_transitions_counts.csv<br>├── Bulk&Cavity<br>│ ├── Cavity&Bulk_run_htmd_0_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_1_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_2_combined_df.csv<br>│ └── Cavity&Bulk_run_htmd_2_transitions_counts.csv<br>├── Cavity<br>│ ├── Cavity_run_htmd_0_combined_df.csv<br>│ ├── Cavity_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity_run_htmd_1_combined_df.csv<br>│ ├── Cavity_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity_run_htmd_2_combined_df.csv<br>│ └── Cavity_run_htmd_2_transitions_counts.csv<br>├── parse_distances_msm.py<br>├── schemes_comparison_piechart_per_scheme.png<br>└── Tunnels<br> ├── Tunnels_run_htmd_0_combined_df.csv<br> ├── Tunnels_run_htmd_0_transitions_counts.csv<br> ├── Tunnels_run_htmd_1_combined_df.csv<br> ├── Tunnels_run_htmd_1_transitions_counts.csv<br> ├── Tunnels_run_htmd_2_combined_df.csv<br> └── Tunnels_run_htmd_2_transitions_counts.csv</p> <p> </p>
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
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>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.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. </p> <p># The forders are organized as:</p> <p>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.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Machine learning prediction of enzyme optimal pH
<p>- Enzyme catalytic optimum pH dataset (pHopt, 9855 proteins)</p> <p>- Secreted bacterial optimum environment pH dataset (pHenv, 1.9 million proteins)</p> <p>- Model for predicting pHopt of enzymes (EpHod)</p> <p>- Code for using the EpHod model are in <a href="https://github.com/beckham-lab/EpHod">GitHub</a>. Paper in <a href="https://doi.org/10.1101/2023.06.22.544776">BioRxiv</a></p> <p> </p>
Supplementary dataset for Correlational networking guides the discovery of cryptic natural product biosynthetic enzymes
<p>Supplementary files for the paper: Correlational networking guides the discovery of cryptic natural product biosynthetic enzymes</p> <p>23777967_protease.fasta.xz:<br> A fasta file (compressed by xz) containing 23777967 protease sequences obtained from 161954 bacterial genomes<br> 23777967_protease_cluster.csv.xz:<br> A csv file (compressed by xz) containing MMseqs2 cluster information of 23777967 proteases<br> This csv file has 5 columns: rep (representative sequence name), mem (member sequence name), number of members in cluster, cluster No.<br> Fig1C_cytoscape.zip:<br> Cytoscape file corresponding to Fig.1C, as well as its node and edge tables<br> Fig2C_cytoscape.zip:<br> Cytoscape file corresponding to Fig.2C, as well as its node and edge tables<br> FigS2_cytoscape.zip:<br> Cytoscape file corresponding to Supplementary Fig.2, as well as its node and edge tables. Size was differently scaled for very large nodes containing more than 1000 precursors/proteases</p>
Aplication of eco-enzyme from nutmeg, clove, and eucalyptus plant waste in inhibiting the growth of E. coli and S. aureus
<p>That different plant wastes' eco-enzymes also had distinctive colors, where DP and DK were brown, BP was reddish-brown, while DC appeared blackish-brown and clear. These differences occur due to variations in the chemical composition of each material used. Furthermore, the acidic aroma from each eco-enzyme was derived from the decomposition of alcohol compounds into acetic acid during aerobic respiration. The aroma was distinctively different depending on the type of plant waste used. Eco-enzymes and commercial antiseptics also have different abilities to inhibit <em>E. coli</em> and <em>S. aureus </em>growth with the highest inhibition found in eco-enzymes made from eucalyptus leaf waste</p>
Data from: Protein Conformational Space at the Edge of Allostery: Turning a Non-allosteric Malate Dehydrogenase into an "Allosterized" Enzyme using Evolution Guided Punctual Mutations
<p>This data accompanies the paper entitled <em>Protein Conformational Space at the Edge of Allostery: Turning a Non-allosteric Malate Dehydrogenase into an “Allosterized” Enzyme using Evolution Guided Punctual Mutations</em></p> <p>The zip archive contains the results of molecular dynamics simulations of the 4 systems investigated in the paper: wt of A. ful MalDH and three mutants. Each system has been simulated at two temperatures, 300 K and 340 K. Starting configurations of the proteins after equilibration are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Computational models from: Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer
<p>Computational models, compressed molecular dynamics (MD) simulation trajectories, and sample input files for "Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer". An early version of this manuscript is available as a preprint here: https://www.biorxiv.org/content/10.1101/2022.03.16.484661v1</p>
Reaction Mechanism of the PET Degrading Enzyme PETase Studied with DFT/MM Molecular Dynamics Simulations
<p>Raw simulations of the acylation step by PETase on a PET dimer model substrate, ran with CP2K 6.1 software at the PBE:AMBER level. Details can be found in the original manuscript (<a href="https://doi.org/10.1021/acscatal.1c03700">https://doi.org/10.1021/acscatal.1c03700</a>): Molecular topology in AMBER Parameter Topology format and Trajectories in CHARMM binary coordinate format DCD.</p> <p>RESIDUE LIST:<br> GLY57<br> TYR58<br> SER131<br> MET132<br> TRP156<br> ASP177<br> SER178<br> ILE179<br> ALA180<br> HID208<br> MOL262</p> <p>VMD selection:<br> (name CA C O HA2 HA3 and resname GLY and resid 57) or (name N CA CB H HA HB2 HB3 and resname TYR and resid 58) or (name CA C O OG CB HA HB2 HB3 HG and resname SER and resid 131) or (name N CA SD CE CB CG H HA HB2 HB3 HG2 HG3 HE1 HE2 HE3 and resname MET and resid 132) or (name CB CG CD1 CD2 CE2 CE3 NE1 CZ2 CZ3 CH2 HB2 HB3 HD1 HE1 HE3 HZ2 HZ3 HH2 and resname TRP and resid 156) or (name CG OD1 OD2 CB HB2 HB3 and resname ASP and resid 177) or (name C O and resname SER and resid 178) or (name N CA C O CG2 CD1 CB CG1 H HA HB HG12 HG13 HG21 HG22 HG23 HD11 HD12 HD13 and resname ILE and resid 179) or (name N CA H HA and resname ALA and resid 180) or (name CB CG CD2 ND1 CE1 NE2 HB2 HB3 HD1 HD2 HE1 and resname HID and resid 208) or (name C1 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C2 C20 C3 C4 C5 C6 C7 C8 C9 H1 H10 H11 H12 H13 H14 H15 H16 H17 H2 H3 H4 H5 H6 H7 H8 H9 O1 O2 O3 O4 O5 O6 O7 O8 O9 and resname MOL and resid 262)</p> <p>PYMOL selection:<br> (name CA+C+O+HA2+HA3 & resn GLY & resi 57) | (name N+CA+CB+H+HA+HB2+HB3 & resn TYR & resi 58) | (name CA+C+O+OG+CB+HA+HB2+HB3+HG & resn SER & resi 131) | (name N+CA+SD+CE+CB+CG+H+HA+HB2+HB3+HG2+HG3+HE1+HE2+HE3 & resn MET & resi 132) | (name CB+CG+CD1+CD2+CE2+CE3+NE1+CZ2+CZ3+CH2+HB2+HB3+HD1+HE1+HE3+HZ2+HZ3+HH2 & resn TRP & resi 156) | (name CG+OD1+OD2+CB+HB2+HB3 & resn ASP & resi 177) | (name C+O & resn SER & resi 178) | (name N+CA+C+O+CG2+CD1+CB+CG1+H+HA+HB+HG12+HG13+HG21+HG22+HG23+HD11+HD12+HD13 & resn ILE & resi 179) | (name N+CA+H+HA & resn ALA & resi 180) | (name CB+CG+CD2+ND1+CE1+NE2+HB2+HB3+HD1+HD2+HE1 & resn HID & resi 208) | (name C1+C10+C11+C12+C13+C14+C15+C16+C17+C18+C19+C2+C20+C3+C4+C5+C6+C7+C8+C9+H1+H10+H11+H12+H13+H14+H15+H16+H17+H2+H3+H4+H5+H6+H7+H8+H9+O1+O2+O3+O4+O5+O6+O7+O8+O9 & resn MOL & resi 262)</p>
Large excess capacity of glycolytic enzymes in Saccharomyces cerevisiae under glucose-limited conditions
<p>Computational models and figure data for the publication "Large excess capacity of glycolytic enzymes in <em>Saccharomyces cerevisiae </em>under glucose-limited conditions" (to be submitted). Data put together by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p> </p> <p><em>Abstract</em></p> <p>In Nature, microbes live in very nutrient-dynamic environments. Rapid scavenging and consumption of newly introduced nutrients therefore offer a way to outcompete competitors. This may explain the observation that many microorganisms, including the budding yeast <em>Saccharomyces cerevisiae,</em> appear to keep “excess” glycolytic proteins at low growth rates, i.e. the maximal capacity of glycolytic enzymes (largely) exceeds the actual flux through the enzymes. However, such a strategy requires investment into preparatory protein expression that may come at the cost of current fitness. Moreover, at low nutrient levels, enzymes cannot operate at high saturation, and overcapacity is poorly defined without taking enzyme kinetics into account.</p> <p>Here we use computational modeling to suggest that in yeast the overcapacity of the glycolytic enzymes at low specific growth rates is a genuine excess, rather than the optimal enzyme demand dictated by enzyme kinetics. We found that the observed expression of the glycolytic enzymes did match the predicted optimal expression when <em>S. cerevisiae</em> exhibits mixed respiro-fermentative growth, while the expression of tricarboxylic acid cycle enzymes always follows the demand. Moreover, we compared the predicted metabolite concentrations with the experimental measurements and found the best agreement in glucose-excess conditions. We argue that the excess capacity of glycolytic proteins in glucose-scarce conditions is an adaptation of <em>S. cerevisiae</em> to fluctuations of nutrient availability in the environment.</p>
Leaf enzyme plays a more important role in leaf nitrogen resorption efficiency than soil properties along an elevation gradient
<p>1. Nitrogen (N) resorption is a strategy for plant N conservation through which plants withdraw N from senescing leaves prior to litterfall and its underlying mechanisms are important for better understanding of N cycling. However, most current studies focused on the impacts of soil and leaf nutrients on leaf N resorption efficiency (NRE), and plant physiological regulation that is species-dependent is still unclear.</p> <p>2. Here, we conducted a field experiment to investigate the variations of leaf NRE along an altitudinal gradient in a temperate forest of Northeastern China.</p> <p>3. Results showed that leaf NRE of Q. mongolica and F. mandshurica increased with altitude, while leaf NRE of T. amurensis, A. mono and A. pseudosieboldianum exhibited an opposite trend, although the relationships were not significant for F. mandshurica and A. mono. The inconsistent responses of leaf NRE of different species to increasing altitude were primarily due to the effect of leaf Glutamate dehydrogenase (GDH), an enzyme responsible for N translocation. Leaf GDH activity in senescing leaves explained the variation of NRE more than soil and climate factors did, suggesting that different plant species had different physiological regulation strategies for their N conservation under similar environment.</p> <p>4. Synthesis. Our study highlights the role of leaf enzyme as a pivotal regulator of leaf NRE and helps us better understand and predict N cycling under climate change in forest ecosystems.</p>
Data and metadata of soil microbial community structure, enzyme activities, functional genes and earthworms derived from H2020 Diverfarming project
<p>Soil data and metadata of soil microbial community structure, enzyme activities (dehydrogenase, β-glucosidase, leucine-aminopeptidase, alkaline phosphatase and arylsusfatase activities), N functional genes and earthworms from the different cases studies and long terms from WP4 "Impact of crop diversification on biodiversity", derived from H2020 Diverfarming project. The main objective of workpackage is to provide a scientific understanding of the link between diversified cropping systems, above- and belowground biodiversity, and the resulting ecosystem services provided by soil microorganisms, soil invertebrates and vegetation in agro-ecosystems. Soil organisms contribute to all biogeochemical cycles, Soil organic matter mineralization and stabilization, shape soil structure and have associations with plant species promoting growth and development. http://www.diverfarming.eu.</p>
Raw data: Association and functional analysis of angiotensin-converting enzyme 2 gene genetic variants with the pathogenesis of pre-eclampsia
<p class="MsoNormal"><span>These data were generated to investigate the association and functional analysis of angiotensin-converting enzyme 2 genetic variants with the pathogenesis of pre-eclampsia(PE). This study conducted a case-control study involving 327 PE patients and 591 healthy pregnant women to explore the associations between candidate variants in the ACE2 gene variants and the pathogenesis of PE.This study collected clinical samples and data, and used logistic regression, false positive report rate, multi factor dimension reduction, functional analysis and other analysis methods to process the research data. </span>Potential functional ACE2 gene variants (rs2106809 A>G, rs6632677 G>C, and rs2074192 C>T) were selected and genotyped using kompetitive allele-specific PCR. The strength of the associations between the studied genetic variants and the risk of PE were evaluated using odds ratios (ORs) and corresponding 95% confidence intervals (CIs).<span> Finally,it showed that the rs2106809 A>Gis significantly associated with the risk of PE via individual locus effects and/or complex gene-gene and gene-environment interactions.</span><span> </span></p>
Biochar and nitrogen fertilizer promote rice yield by altering soil enzyme activity and microbial community structure
<p><span>Biochar can significantly change soil properties and improve soil quality.</span> <span>However, the effects of long-term combined application of biochar (B) and nitrogen (N) fertilizer on relationships between soil enzyme activity, microbial community structure and crop yield are still obscure. We characterized these relationships in a long-term (8 years) field experiment with rice, two biochar rates of 0 and 13.5 t ha<sup>-1</sup> year<sup>-1</sup> (B0 and B) and two N fertilizer rates of 0 and 300 kg N ha<sup>-1</sup> year<sup>-1</sup> (N0 and N).</span><span> The repeated, long-term combined applications of biochar and N fertilizer significantly increased microbial biomass carbon and nitrogen (MBC and MBN), but biochar decreased the abundance of total bacteria, fungi, actinomycetes, Gram-positive and Gram-negative bacteria as well as the amount of total phospholipid fatty acids. </span><span>The activity of leucine aminopeptidase (LAP) </span><span>decreased significantly in the biochar-amended and N fertilized treatment, but</span><span> the LAP activity either remained unchanged or increased with biochar amendment at N0. The relative abundance of bacterial phylum <em>Chloroflexi</em> was increased in the combined biochar and N fertilizer treatment. The changes in soil organic matter and the activity of α-1,4-xylosidase were the major properties influencing soil bacterial community composition, whereas the structure of fungal community was governed by MBC, MBN and LAP activity. In addition, long-term biochar and N fertilizer applied together significantly increased rice yield (more than biochar and nitrogen fertilizer applied alone). Yield</span> <span>was significantly positively correlated with LAP activity, but significantly negatively correlated with the relative abundance of Chloroflexi. In conclusion, long-term biochar and nitrogen fertilizer applications increased rice yield, which was associated with altered soil microbial community and enhanced activity of some enzymes.</span></p>
Heterogeneity of RNA editing in mesothelioma and how RNA editing enzyme ADAR2 affects mesothelioma cell growth, response to chemotherapy and tumor microenvironment
<p>Raw data supporting the manuscript</p>
Molecular dynamics simulations data of Caspase-3 enzyme with pentapeptide ligand DEVDG and its chiral mutant DEVdG having D-Asp at fourth position
<p>Amino acids in proteins are maintained in one specific L chiral form in the body. D-amino acids are not normally incorporated into proteins and their accumulation has been associated with several conditions including schizophrenia, amyotrophic lateral sclerosis, and other age-related disorders. However, the mechanisms by which the accumulation of D-amino-acids in proteins may lead to pathophysiological consequences remain poorly understood. In this work, we studied a model protease system, caspase-3 that specifically hydrolyses the 4’–5’ peptide bond of the pentapeptide substrate DEVDG. Through extensive molecular dynamics simulations, free energy calculations and distance maps, we reveal that caspase-3 naturally rejects the pentapeptide containing D-Asp substrate, DEVdG and prevents catalytic activity by caspase. The importance of this chiral discriminating capacity is evident from chiral-selective in vivo experimental assays to detect caspase-bound D-Asp in Drosophila where altering the chiral balance created impaired caspase activity and impaired apoptosis, increased tumour formation, and premature death. The modelling data reveals the molecular level charge balancing that enforces the chiral recognition necessary to maintain homeostasis across the cell, tissue, and organ level.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.