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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>
Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models
<p>Ligand datasets used to train and evaluate the models studied in <em>"Ligand Identification using Deep Learning</em><em>"</em> by Karolczak, J. <em>et al.</em></p> <p>The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.</p>
Simulated complex structures of the h-FBP21 tandem WW domain with proline-rich ligand extracted from SmB/B' core-splicing protein
<p>The tandem WW domain of the human formin-binding protein 21 (h-FBP21 tWW) consists of two WW domains separated by a flexible linker. It can bind target sequences in two different orientations and the flexibility of the linker additionally allows the two WW domains to adopt various relative orientations to each other. As consequence, the elucidation of possible complex structures for the h-FBP21 tWW is very challenging.</p> <p>Here, we present two complex structures for the h-FBP21 tWW and a proline-rich sequence from its natural binding partner, the core-splicing protein SmB/B’. Showing parallel (‘6’) and antiparallel (’14’) binding orientation, the two structures also differ in the relative positioning of the WW domains.</p> <p>For further instructions regarding the files, please refer to ‘README’.</p>
Data for: Actions of Parathyroid hormone ligand analogs in humanized PTH1R knock-in mice
<p>Rodent models are commonly used to evaluate parathyroid hormone (PTH) and PTH-related protein (PTHrP) ligands and analogs for their pharmacologic activities and potential therapeutic utility towards diseases of bone and mineral ion metabolism. Divergence, however, in the amino acid sequences of rodent and human PTH receptors (rat and mouse PTH1Rs are 91% identical to the human PTH1R) can lead to differences in receptor-binding and signaling potencies for such ligands when assessed on rodent versus human PTH1Rs, as shown by cell-based assays in vitro. This introduces an element of uncertainty in the accuracy of rodent models for performing such pre-clinical evaluations. To overcome this potential uncertainty, we used a homologous recombination-based knock-in (KI) approach to generate a mouse (in host strain C57Bl/6N) in which cDNA encoding the human PTH1R replaces a segment (Exon 4) of the murine PTH1R gene such that the human and not the mouse PTH1R protein is expressed. Expression is directed by the endogenous mouse promoter and hence occurs in all biologically relevant cells and tissues and at appropriate levels. The resulting homozygous hPTH1R-KI (humanized) mice were healthy over at least ten generations and showed functional responses to injected PTH analog peptides that are consistent with a fully functional human PTH1R in target bone and kidney cells. The initial evaluation of these mice and their potential utility for predicting behavior of PTH analogs in humans is reported here. </p>
Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target – an fMRI study
<p>Background: Monoclonal antibodies (mAbs) against calcitonin gene-related peptides (CGRP) are novel treatments for migraine prevention. Based on a previous functional imaging study which investigated the CGRP receptor mAb (erenumab), we hypothesized that (i) the CGRP ligand mAb galcanezumab would alter central trigeminal pain processing; (ii) responders to galcanezumab treatment would show specific hypothalamic modulation in contrast to non-responders; and (iii) the ligand and the receptor antibody differ in brain responses.</p> <p>Methods: Using an established trigeminal nociceptive functional magnetic imaging paradigm, 26 migraine patients were subsequently scanned twice: before and 2–3 weeks after administration of galcanezumab.</p> <p>Results: We found that galcanezumab decreases hypothalamic activation in all patients and that the reduction was stronger in responders than in non-responders. Contrasting erenumab and galcanezumab showed that both antibodies activate a distinct network. We also found that pre-treatment activity of the spinal trigeminal nucleus (STN) and coupling between the STN and the hypothalamus covariates with the response to galcanezumab.</p> <p>Conclusions: These data suggest that despite relative impermeability of the blood-brain barrier for CGRP mAb, mAb treatment induces certain and highly specific brain effects which may be part of the mechanism of their efficacy in migraine treatment.</p> <p>Funding: This work was supported by the German Ministry of Education and Research (BMBF) of ERA-Net Neuron under the project code BIOMIGA (01EW2002 to AM) and by the German Research Foundation (SFB936-178316478-A5 to AM). The funding sources did not influence study conduction in any way. </p> <p>Clinical trial number: The basic science study was preregistered in the Open Science Framework (https://osf.io/m2rc6).</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data
<p>This dataset contains the raw image data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data - calibration images
<p>This dataset contains the images used for channel calibration for the single molecule data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
PanDDA files from a ligand screen against the NSP3 macrodomain of SARS-CoV-2 - ligands from fragment merging/linking and virtual screening
<p>This deposition contains the X-ray diffraction data used to run PanDDA in the ligand screen against the NSP3 macrodomain of SARS-CoV-2 described in Gahbauer et al. 2022 (doi: https://doi.org/10.1101/2022.06.27.497816).</p> <p>mac1_pandda.zip contains the structure factor intensities, PanDDA input/ouput and refined models/maps. A description of the files can be found in the README file. </p> <p>mac1_ligand-bound_states.zip contains the ligand-bound states extracted from the multi-state PDB files. </p>
Raw Data for 'Efficient Access of Phenyl-Spaced 5,5´-Bridged Dinuclear Ruthenium Metal Complexes and the Effect of Dynamic Ligand Exchange on Catalysis'
<p>Herein, we provide the raw data for all figures being part of either the mansucript or the supporting information of the publication 'Efficient Access of Phenyl-Spaced 5,5’-Bridged Dinuclear Ruthenium Metal Complexes and the Effect of Dynamic Ligand Exchange on Catalysis'.</p>
Mass spectrometric data for chaperone-ligand interactions
<p><span>The assembly of proteins and peptides into amyloid fibrils is causally linked to serious disorders such as Alzheimer's Disease. Multiple proteins have been shown to prevent amyloid formation <em>in vitro</em> and <em>in vivo</em>, ranging from highly specific chaperone-client pairs to completely non-specific binding of aggregation-prone peptides. The underlying interactions remain elusive. Here, we turn to the machine learning-based structure prediction algorithm AlphaFold2 (AF2) to obtain models for the non-specific interactions of </span><span>b</span><span>-lactoglobulin (</span><span>b</span><span>LG), transthyretin (TTR), or Thioredoxin 80 (T80) with the model amyloid peptide Amyloid </span><span>b</span><span> (A</span><span>b</span><span>), and the highly specific complex between the BRICHOS chaperone domain of lung surfactant protein C (CTC) and its polyvaline target. Using a combination of native mass spectrometry (MS) and ion mobility MS, we show that non-specific chaperoning is driven predominantly by hydrophobic interactions of A</span><span>b</span><span> with hydrophobic surfaces in </span><span>b</span><span>LG, TTR, and T80, and in part regulated by oligomer stability. For CTC, native MS and hydrogen-deuterium exchange MS reveal that a disordered region recognizes the polyvaline target by forming a complementary </span><span>b</span><span>-strand. Hence, we show that AF2 and MS can yield atomistic models of hard-to-capture protein interactions that reveal different chaperoning mechanisms based on separate ligand properties and may provide possible clues for specific therapeutic intervention.</span></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>
SC-XRD diffraction images of stereo-defined pyrrolidine ligand
<p>Structures of a piperazine in the publication: "Direct and Stereospecific [3+2] Synthesis of Pyrrolidines from Simple Unactivated Alkenes" Angew. Chem. Int. Ed. 2017, DOI:10.1002/anie.201706682</p> <p>Structure solutions were deposited in the CCDC: 1528080<br> https://www.ccdc.cam.ac.uk/structures-beta/Search?id=doi:10.1002/anie.201706682</p>
Ligands detected by CheckMyBlob in the Protein Data Bank
<p>A dataset containing numerical descriptions all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017. It is the "master" data set containing all ligands queried and detected as described in the Kowiel et al. paper "Automatic recognition of ligands in electron density by machine learning methods".</p> <p>The file is compressed using 7zip to allow for faster downloads. The compressed file weighs around 1.1 GB, whereas the uncompressed CSV will take close to 3.0 GB of disk space. The all_summary.csv file can be used to reproduce the filtered ligand data sets (CMB, TAMC, CL) described in the Kowiel et al. paper. Additionally, the data set can be used as a source to create data sets based on other filtering criteria (e.g. ligand subsets of your choice), or on its own a as source of knowledge about all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017.</p> <p>For machine learning applications, please read the code at https://github.com/dabrze/CheckMyBlob, to see examples describing how to preprocess the data. In particular, the following attributes should not be used during model training or testing: "pdb_code", "res_id", "chain_id", "local_res_atom_count", "local_res_atom_non_h_count", "local_res_atom_non_h_occupancy_sum", "local_res_atom_non_h_electron_sum", "local_res_atom_non_h_electron_occupancy_sum", "local_res_atom_C_count", "local_res_atom_N_count", "local_res_atom_O_count", "local_res_atom_S_count", "dict_atom_non_h_count", "dict_atom_non_h_electron_sum", "dict_atom_C_count", "dict_atom_N_count", "dict_atom_O_count", "dict_atom_S_count", "fo_col", "fc_col", "weight_col", "grid_space", "solvent_radius", "solvent_opening_radius", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step", "local_volume", "res_coverage", "blob_coverage", "blob_volume_coverage", "blob_volume_coverage_second", "res_volume_coverage", "res_volume_coverage_second", "skeleton_data", "resolution_max_limit", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step".</p> <p>The target attribute for classification is: <strong>res_name</strong>.</p>
Data for "Conformational control of Pd2L4 assemblies with unsymmetrical ligands"
<div>In the following subdirectories are the input and output of Gaussian calculations for this publication:</div> <div> </div> <div>James E. M. Lewis, et.al., Conformational Control of Pd2L4 Assemblies with Unsymmetrical Ligands, Chem. Sci. DOI: 10.1039/C9SC05534G </div> <div> </div> <div>Previously uploaded in <span>10.5281/zenodo.8432296 and </span><a href="https://github.com/andrewtarzia/citable_data" rel="noopener noreferrer"><span>https://github.com/andrewtarzia/citable_data</span></a></div> <div> </div> <div>sub-directories:</div> <div> <ul> <li>b3lyp_calculations/<br> <ul> <li>Gaussian16 input and output for DFT calculations on the cages in this publication.</li> <li>See the README in that directory.</li> </ul> </li> <li>pywindow_analysis/<br> <ul> <li>python code to run pywindow analysis on reported CIFs and calculated XYZ structures.</li> <li>See the README in that directory.</li> </ul> </li> </ul> </div> <p> </p>
Data for "Binding Affinity of Monoalkyl Phosphinic Acid Ligands toward Nanocrystal Surfaces".
<p>Data of the figures in the publication "<strong>Binding Affinity of Monoalkyl Phosphinic Acid Ligands toward Nanocrystal Surfaces</strong>".</p> <p>The <em>.pxp</em> documents contain the experimental data of the figures in the manuscript and they can be opened/edited with the software IGOR Pro 6.3 or higher.</p> <p>Table of contents:</p> <p><strong>Figure 1.</strong> (A) General reaction scheme toward monoclinic HfO<sub>2</sub>/oleate NCs. (B) TEM image and (C) DOSY NMR spectrum in C<sub>6</sub>D<sub>6</sub> of HfO<sub>2</sub>/oleate NCs. (D) General reaction scheme toward zinc blend CdSe/oleate NCs. (E) TEM image and (F) DOSY NMR spectrum in C<sub>6</sub>D<sub>6</sub> of CdSe/oleate NCs.</p> <p><strong>Figure 2.</strong> (Left) Titration of HfO<sub>2</sub>/oleate with 6-(hexyloxy)hexylphosphinic acid. (A) General reaction scheme. (B) <sup>1</sup>H NMR spectra of the titration. (C) <sup>31</sup>P NMR after 0.95 and 1.15 equiv of phosphinic acid is added. (D) Quantification of the different compounds as a function of the added equivalents. (Right) Titration of CdSe/oleate with 6-(hexyloxy)hexylphosphinic acid. (E) General reaction scheme. (F) <sup>1</sup>H NMR spectra of the titration. (G) <sup>31</sup>P NMR after 1.0 and 1.2 equiv of phosphinic acid is added. (H) Quantification of the different compounds as a function of the added equivalents.</p> <p><strong>Figure 3.</strong> (A) <sup>1</sup>H NMR spectrum of oleate-capped HfO<sub>2</sub> NCs in C<sub>6</sub>D<sub>6</sub>. (B) <sup>1</sup>H NMR spectrum of HfO<sub>2</sub> in C<sub>6</sub>D<sub>6</sub> after ligand exchange for 6-(hexyloxy)hexylphosphinic acid and purification. The inset shows the <sup>31</sup>P NMR spectrum.</p> <p><strong>Figure 4.</strong> (Left) Titration of HfO<sub>2</sub>/[6-(hexyloxy)hexyl]phosphinate with oleylphosphonic acid. (A) General reaction scheme. (B) <sup>1</sup>H NMR spectra of the titration. (C) Quantification of the different compounds as a function of the added equivalents. Note that the small amount of residual oleic acid present at the start of the titration in (B) is due to a challenging purification (high solubility of the HfO<sub>2</sub>/[6-(hexyloxy)hexyl]phosphinate NCs). This small signal was integrated and subtracted from the spectra for the quantification in (C). (Right) Titration of CdSe/[6-(hexyloxy)hexyl]phosphinate with oleylphosphonic acid. (D) General reaction scheme. (E) <sup>1</sup>H NMR spectra of the titration. (F) Quantification of the different compounds as a function of the added equivalents.</p> <p><strong>Figure 5.</strong> Mole fraction of bound oleylphosphonate in the ligand shell, χ<sub>phosphon (bound)</sub>, as a function of the overall mole fraction of oleylphosphonic acid (=unbound phosphonic acid and bound phosphonate), χ<sub>phosphon (total)</sub>, during the titrations of HfO<sub>2</sub> (blue), CdSe (red), and ZnS (green) NCs. The full lines represent different calculated equilibrium constants.</p> <p><strong>Figure 6.</strong> Mole fraction of a bound incoming ligand (= titrating ligand) in the ligand shell, χ<sub>incomingligand (bound)</sub>, as a function of the overall mole fraction of the total incoming ligand (=unbound and bound), χ<sub>incomingligand (total)</sub>, during the titrations of ZnS NCs stabilized with a 50/50 mixture of <em>n</em>-hexyl/<em>n</em>-octadecylphosphinate with oleylphosphonic acid (gray), ZnS NCs stabilized with 6-(hexyloxy)hexylphosphinate with oleylphosphonic acid (green) and ZnS NCs stabilized with oleate with a 50/50 mixture of <em>n</em>-hexyl/<em>n</em>-octadecylcarboxylic acids (orange). The full lines represent the different calculated equilibrium constants.</p> <p><strong>Figure S1.</strong> (A) HfO2/oleate NCs, (B) 1H NMR spectrum in C6D6, and (C) DOSY decay curve of the alkene region.</p> <p><strong>Figure S2.</strong> (A) CdSe/oleate NCs, (B) 1H NMR spectrum in C6D6, and (C) UV-vis absorption spectrum, (D) DOSY decay curve of the alkene region, and (E) DOSY decay curve of the methylene region.</p> <p><strong>Figure S3.</strong> Synthesis of zinc blende ZnS/oleate NCs. (A) General reaction scheme, (B) TEM image, and (C) 1H NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the alkene region, and (F) UV-vis absorption spectrum.</p> <p><strong>Figure S4.</strong> 1H NMR of (top, black line) the supernatant of the HfO2/oleate NCs after titration until 1.0 equivalent 6-(hexyloxy)hexyl phosphinic acid is added, and (bottom grey line) reference spectrum of oleic acid in CDCl3.</p> <p><strong>Figure S5.</strong> 1H NMR of (top, black line) the supernatant of the CdSe/oleate NCs after titration until 1.0 equivalent 6-(hexyloxy)hexyl phosphinic acid is added, and (bottom grey line) reference spectrum of oleic acid in CDCl3</p> <p><strong>Figure S6.</strong> Titration of ZnS/oleate NCs with 6-(hexyloxy)hexylphosphinic acid. (A) General reaction scheme, (B) 1H NMR spectra of the titration, (C) 31P NMR after 1.0 and 1.6 equivalent phosphinic acid is added, and (D) quantification of the different compounds as a function of added equivalents.</p> <p><strong>Figure S7.</strong> 1H and 31P NMR spectra of (top gray line) n-tetradecylphosphinic acid dehydrated<br>with dicyclohexylcarbodiimide (DCC) to form n-tetradecylphosphinic anhydride, and (bottom<br>black line) n-tetradecylphosphinic acid reference, both in C6D6. </p> <p><strong>Figure S8.</strong> (A) 1H and (B) 31P NMR of (top black line) the supernatant of the ZnS/oleate NCs after titration until 1.6 equivalent 6-(hexyloxy)hexylphosphinic acid is added, and reference spectra of (red line) oleic acid and (blue line) 6-(hexyloxy)hexylphosphinic acid in CDCl3.</p> <p><strong>Figure S9.</strong> Purified HfO2/[6-(hexyloxy)hexyl]phosphinate NCs. (A) HfO2/phosphinate NCs. (B) 1H NMR spectrum in C6D6 with zoom inset of the broadened P-H resonance, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the ether region.</p> <p><strong>Figure S10.</strong> Purified CdSe/[6-(hexyloxy)hexyl]phosphinate NCs. (A) CdSe/phosphinate NCs. (B) 1H NMR spectrum in C6D6, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the ether region, (F) DOSY decay curve of the methylene region, and (G) UV-vis absorption spectrum.</p> <p><strong>Figure S11.</strong> Purified ZnS/[6-(hexyloxy)hexyl]phosphinate NCs. (A) ZnS/phosphinate NCs. (B) 1H NMR spectrum in C6D6 with zoom inset of the broadened P-H resonance, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the ether region, and (F) UV-vis absorption spectrum.</p> <p><strong>Figure S12.</strong> (A) 1H and (B) 31P NMR of the supernatant (black line) of the HfO2/phosphinate NCs after titration until 2.0 equivalent oleylphosphonic acid is added, and reference spectra of (green line) oleylphosphonic acid and (blue line) 6-(hexyloxy)hexylphosphinic acid in C6D6</p> <p><strong>Figure S13.</strong> (A) 1H and (B) 31P NMR of the supernatant (black line) of the CdSe/phosphinate NCs after titration until 2.0 equivalent oleylphosphonic acid is added, and reference spectra of (green line) oleylphosphonic acid and (blue line) 6-(hexyloxy)hexylphosphinic acid in C6D6. </p> <p><strong>Figure S14.</strong> Titration of ZnS/[(6-hexyloxy)hexyl]phosphinate with oleylphosphonic acid. (A) General reaction scheme, (B) 1H NMR spectra of the titration, and (C) quantification of the different compounds as a function of added equivalents.</p> <p><strong>Figure S15.</strong> (A) 1H and (B) 31P NMR of the supernatant (black line) of the ZnS/phosphinate NCs after titration until 2.0 equivalent oleylphosphonic acid is added, and reference spectra of (green line) oleylphosphonic acid and (blue line) 6-(hexyloxy)hexyl phosphinic acid in C6D6.</p> <p><strong>Figure S16.</strong> Titration of HfO2 NCs stabilized with a mixed ligand shell consistent of 6-(hexyloxy)hexylphosphinate and oleylphosphonate. (A) General reaction scheme, (B) 1H NMR spectrum of the purified NCs prior to titration (at 0.0 added equivalents of 6-(hexyloxy)hexylphosphinic acid), (C) 1H NMR spectra of the titration, and (D) quantification of the different compounds as a function of added equivalents 6-(hexyloxy)hexylphosphinic acid.</p> <p><strong>Figure S17.</strong> Titration of CdSe NCs stabilized with a mixed ligand shell consistent of 6-(hexyloxy)hexylphosphinate and oleylphosphonate. (A) General reaction scheme, (B) 1H NMR spectrum of the purified NCs prior to titration (at 0.0 added equivalents of 6-(hexyloxy)hexylphosphinic acid), (C) 1H NMR spectra of the titration, and (D) quantification of the different compounds as a function of added equivalents 6-(hexyloxy)hexylphosphinic acid.</p> <p><strong>Figure S18.</strong> Titration of ZnS NCs stabilized with a mixed ligand shell consistent of 6-(hexyloxy)hexylphosphinate acid and oleylphosphonate. (A) General reaction scheme, (B) 1H NMR spectrum of the purified NCs prior to titration (at 0.0 added equivalents of 6-(hexyloxy)hexylphosphinic acid) (C) 1H NMR spectra of the titration, and (D) quantification of the different compounds as a function of added equivalents 6-(hexyloxy)hexylphosphinic acid.</p> <p><strong>Figure S19.</strong> The mole fraction of bound oleylphosphonate in the ligand shell, 𝜒𝑝ℎ𝑜𝑠𝑝ℎ𝐨𝑛 (𝑏𝑜𝑢𝑛𝑑), as a function of the overall mole fraction of oleylphosphonic acid (= unbound phosphonic acid and bound phosphonate), 𝜒𝑝ℎ𝑜𝑠𝑝ℎ𝐨𝑛 (𝑡𝑜𝑡𝑎𝑙) , during the titrations of (A) HfO2 (blue), (B) CdSe (red), and (C) ZnS (green) NCs. The full lines represent different calculated equilibrium constants.</p> <p><strong>Figure S20.</strong> Changes in chemical shift during the ligand exchange of phosphinate for phosphonate for HfO2, CdSe, and ZnS NCs</p> <p><strong>Figure S21.</strong> Purified ZnS/n-alkylphosphinate NCs with a 50/50 mixture of n-hexyl/noctadecylphosphinate. (A) ZnS/phosphinate NCs. (B) 1H NMR spectrum in C6D6 with zoom inset of the broadened P-H resonance, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the alkane region, and (F) UV-vis absorption spectrum.</p> <p><strong>Figure S22.</strong> Titration of ZnS/n-alkylphosphinate with a 50/50 mixture of n-hexyl/noctadecylphosphinate with oleylphosphonic acid. (A) General reaction scheme, (B) 1H NMR spectra of the titration (zoom of the alkene resonance and the adjacent methylene groups), and (C) quantification of the different compounds as a function of added equivalents.</p> <p><strong>Figure S23.</strong> Comparative ligand exchange experiments where oleate capped ZnS NCs are titrated with a 50/50 mixture of n-hexyl/n-octadecylcarboxylic, -phosphinic, or -phosphonic acids. (A) General reaction scheme for the 3 separate titrations with carboxylic, phosphinic, or phosphonic acids. (B) Bound fraction of oleate on ZnS NCs as a function of the added equivalents of the titrating acid mixture, including the theoretical expected quantitative and random exchange development.</p> <p><strong>Figure S24.</strong> Titration of ZnS/oleate NCs with a 50/50 mixture of n-hexyl, and n-octadecyl carboxylic, phosphinic, and phosphonic acids in C6D6. (A) General reaction scheme. (B) 1H NMR spectra of the titration with carboxylic acids. (C) 1H NMR spectra of the titration with phosphinic acids. (D) 1H NMR spectra of the titration with phosphonic acids (added from a concentrated solution in THF-d8).</p> <p> </p> <p> </p> <p> </p>
Gene and protein sequence features augment HLA class I ligand predictions
<p>Dataset and analyses supporting the manuscript "Gene and protein sequence features augment HLA class I ligand predictions".</p> <p>The "peptides" files contain the mass-spec detected peptides obtained from HLA ligandomics performed on the indicated tumor lines. </p> <p>The "protein data" files contain the RNAseq data (TPM) and Ribosome profiling data (ribosome occupancy) per protein, for each tumor line. </p> <p>The "source data" zip archive contains the source data underlying the figures of the manuscript.</p> <p>The "HLA ligandome analyses" zip archive contains the R scripts used for all data analysis in the manuscript, including all data and output files. These analyses can also be found at https://github.com/kasbress/HLA_Ligandome_Analyses/</p> <p> </p> <p> </p>
Application of CoLD-CoP to Detecting Competitively and Cooperatively Binding Ligands
<p>CompetitiveCooperative_CoLD_CoP_for_archiving.zip will unzip into a directory with two sub-directories. The directory LysozymeNAG has directories with raw data (and NMRPipe format spectra processed in the direct dimension) for DOSYs acquired with a ligands only sample, a ligands + lysozyme sample and a ligands + lysozyme + NAG (N-Acetyl-Glucosamine) sample. This directory also has the key scripts used for DOSY processing and analysis of this dataset. The directory TyrosinaseHCCA similarly has directories with raw data (and NMRPipe format spectra processed in the direct dimension) for DOSYs acquired with a ligands only sample, a ligands + (mushroom) tyrosinase sample and a ligands + tyrosinase + HCCA (4-Hydroxy-a-Cyano-Cinnamic Acid) sample. The TyrosinaseHCCA directory has not only the key scripts used for DOSY processing and analysis of this dataset, but it also has 1D proton spectra acquired on each solution used in this arm of the project. </p> <p>The file das_dosy_nm.m is a customized version of the DOSY processing file used in DOSY processing instead of the dosy_nm.m file used in the DOSY Toolbox. Use of the scripts found in the directories described in the above paragraph requires das_dosy_nm.m as well as NMRPipe, the DOSY Toolbox (https://nmr.chemistry.manchester.ac.uk/?q=node/8), CoLD-CoP toolbox and Covariance Toolbox (both available via MATLAB Central File Exchange).</p> <p>The file lysozyme_complexes_for_docking.zip contains lysozyme structures as minimized in various complexes as well as the ligands from those complexes. These can be used for rescoring or further re-docking, e.g., in FastDRH. DockingAndEnergyMinimization.zip contains some of the files used for docking and energy minimization of the Lysozyme-NAG, Lysozyme-Tris and Lysozyme-GlcNAC complexes. </p>
Titration of ligands for the stimulation of MDA-MB-231 BRE and CAGA reporter cell lines
<p>Considerable amount of ligands will be consumed in the dual luciferase assay (DLA) which will be used for the screening of ALK2 inhibitors and off-target inhibition of ALK5 routinely. Ligands are costly to procure. In order to minimise wastage, it is important to determine the least amount of ligands needed to achieve optimal stimulation of ALK2 and ALK5. In these experiments, the activation of ALK2 and ALK5 by different concentrations of ligands in MDA-MB-231 reporter cells was analysed in Western Blot and DLA.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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International Brain Laboratory public data
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