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2,012 results for “kinase”
Adenylate Kinase Potential of Mean Force
<p>Adenylate kinase (AdK) is a enzyme that undergoes a large hinge-like motion. Because of an abundance of structural and functional data, it has become a standard system to test computational methods for sampling conformational transitions.<sup>1</sup></p> <p>In 2009, we studied the conformational transition between open and closed <em>E. coli</em> AdK without substrate, i.e. “apo AdK”, with a variety of computational methods.<sup>2</sup> As part of the study we also produced a free energy landscape (a <strong>potential of mean force</strong> or <strong>PMF</strong>) as a function of two collective variables, the angles formed by the LID and NMP domains with the CORE domain.<sup>3</sup> We <sup>2</sup> and others<sup>4</sup><sup>,</sup><sup>5</sup> have used this PMF to compare methods that sample transition paths to the underlying free energy landscape.</p> <p><strong>Terms of Use</strong></p> <p>The data are made available under a <strong>Attribution-ShareAlike 4.0 International</strong> licence (include the following when using the data):</p> <p><em>Adenylate Kinase Potential of Mean Force</em> by O Beckstein, EJ Denning, JR Perilla, TB Woolf is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at http://becksteinlab.physics.asu.edu/file_download/11/free_bw2_tol1e-05.dat.</p> <p>When you make use of the data (contained in the file free_bw2_tol1e-05.dat) in published work, <strong>cite</strong> the paper<sup>2</sup></p> <p>O. Beckstein, E. J. Denning, J. R. Perilla, and T. B. Woolf. <em>Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open ? closed transitions</em>. J. Mol. Biol., 394(1):160–176, 2009.</p> <p><strong>Data</strong></p> <p>The file free_bw2_tol1e-05.dat contains the PMF data shown in Fig. 4a of the JMB paper<sup>2</sup>.</p> <p>The image shows the data plotted with cubic spline smoothing.</p> <p>File format</p> <p>free_bw2_tol1e-05.dat is the output from WHAM. The important data columns are</p> <ol> <li>NMP-core angle (degrees)</li> <li>LID-core angle (degrees)</li> <li>free energy in kcal/mol</li> </ol> <p>(Other columns are output from wham and can be ignored.)</p> <p><strong>Methods</strong></p> <p>Conformations of <em>E. coli</em> AdK were umbrella-sampled in the space of the NMP-core and LID-core angles.<sup>3</sup> The protein was modelled in implicit solvent with the ACE2 electrostatics model. The resulting umbrella data were unbiased using Alan Grossfield’s wham code with</p> <ul> <li>bin size 2º</li> <li>tolerance of the self consistency procedure 1e-5 <em>kT</em></li> <li>limits 34º < NMP < 80º and 94º < LID < 156º</li> </ul> <p>The first 2000 frames (200ps) of each window were discarded as equilibration and the remaining 3000 frames were used for the PMF. For further details please see the paper.<sup>2</sup></p> <p><strong>References</strong></p> <ol> <li>S. L. Seyler and O. Beckstein, O. <em>Sampling large conformational transitions: adenylate kinase as a testing ground</em>. Mol. Simul., 40(10–11): 855–877, 2014.</li> <li>O. Beckstein, E. J. Denning, J. R. Perilla, and T. B. Woolf. <em>Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open / closed transitions</em>. J. Mol. Biol., 394(1):160–176, 2009.</li> <li>See the 2009 paper<sup>2</sup> for the definitions and the MDAnalysis tutorial’s Exercise 4 for Python code to calculate the angles.</li> <li>M. Gur, J. D. Madura, and I. Bahar. <em>Global transitions of proteins explored by a multiscale hybrid methodology: Application to adenylate kinase</em> Biophysical Journal, 105(7):1643 – 1652, 2013.</li> <li>A. Uyar, N. Kantarci-Carsibasi, T. Haliloglu, and P. Doruker. <em>Features of large hinge-bending conformational transitions. Prediction of closed structure from open state</em>. Biophysical Journal, 106(12):2656 – 2666, 2014 </li> </ol>
Supplemental Information - Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling
<p>This folder contains</p> <p>1) maps of plasmids</p> <p>2) files of phylogenetic analysis </p> <p>3) Replication information</p> <p>4) Image cropping information</p> <p>5) Gene IDs and protein sequences</p> <p>that are part of the manuscript "Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling"</p>
Inactive to active transition of human Thymidine Kinase 1 revealed by Molecular Dynamics simulations
<p>The trajectories and input files for the manuscript <em>Inactive to active transition of human Thymidine</em></p> <p><em>Kinase 1 revealed by Molecular Dynamics simulations</em> (<a href="https://doi.org/10.1021/acs.jcim.1c01157">https://doi.org/10.1021/acs.jcim.1c01157</a>) </p> <p>ABSTRACT</p> <p>Despite its importance for the nucleoside (and nucleoside prodrug) metabolism, the structure<br> of the active conformation of human Thymidine Kinase 1 (hTK1) remains elusive. We perform<br> microsecond molecular dynamics simulations of the inactive enzyme form bound to a<br> bisubstrate inhibitor that was shown experimentally to activate another TK1-like kinase,<br> Thermotoga maritima TK (TmTK). Our results are in excellent agreement with the<br> experimental findings for the TmTK closed-to-open state transition. We show that the inhibitor<br> induces an increase of the enzyme radius of gyration due to the expansion on one of the dimer<br> interfaces; the structural changes observed, including the active site pocket volume increase,<br> decrease in monomer-monomer buried surface area and of the number of hydrogen bonds (as<br> compared to the inactive enzyme control simulation), show that the catalytically competent<br> (open) conformation of hTK1 can be assumed in the presence of an activating ligand.</p>
Human With No Lysine Kinase 3 (WNK3); A Target Enabling Package
<p>Kinases WNK1-4 regulate cation-chloride cotransporters via phosphorylation of SPAK and OSR1 and thereby control salt homeostasis, cell volume and blood pressure. Gain of function mutations in WNK kinases are found in Gordon’s hypertension syndrome suggesting the WNK pathway as a therapeutic target. WNK3 inhibition in particular has also been shown to reduce cerebral injury after Ischemic stroke. Here we present assays and crystal structures that define (i) the molecular basis for disease mutations; (ii) the multiple functional domains of WNK kinases and their protein interactions; (iii) the binding of small molecule kinase inhibitors and a potential allosteric pocket.</p>
Supplemental data for "Intramolecular feedback regulation of the LRRK2 Roc G domain by a LRRK2 kinase dependent mechanism" (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083)
<p><strong>Supportive data for the eLife version of record.</strong></p> <p><strong>(1) Data used for the Michaelis Menten Kinetics.</strong></p> <p><strong>HPLC-based assay.</strong> Steady-state kinetic measurements of LRRK2-mediated GTP hydrolysis were performed as previously described (Ahmadian et al., 1997). Briefly, 0.1 µM of full-length LRRK2 was incubated with different amounts of GTP (0, 25, 75, 150, 250, 500, 1000, 2000, 3000 and 5000 µM) and production of GDP was monitored by reversed phase C18 HPLC. To this end, the samples (10 µl) were directly injected on a reversed-phase C18 column (pre-column: Hypersil Gold, 3µm particle size, 4.6x10mm; main column: Hypersil Gold, 5µm particle size, 4.6x250mm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific, Waltham, MA, USA) in HPLC-buffer containing 50 mM KH<sub>2</sub>PO<sub>4</sub>/K<sub>2</sub>HPO<sub>4</sub> pH 6.0, 10 mM tetrabutylammonium bromide and 10-15% acetonitrile. Subsequently, samples were analyzed using the HPLC integrator (Chromeleon 7.2, Thermo Scientific, Waltham, MA, USA). Initial rates of GDP production were plotted against the GTP concentration using GraFit5 (v.5.0.13, Erithacus Software). The number of experiments is indicated in the graph and data point is the average (±s.e.m.) of indicated repetitions. The Michaelis-Menten equation was fitted to determine K<sub>M</sub> (±s.e.) and k<sub>cat</sub> (±s.e.). Excel sheets used for the calculation of means are provided. No values are reported if the HPLC separation failed (e.g. unstable baseline).</p> <p><strong>Charcoal GTP hydrolysis assay. </strong>The [γ-32P]GTP charcoal assay was performed as previously described (Bollag and McCormick, 1995). Briefly, 0.1 µM full-length LRRK2 or 0.5 µM 6xHIS-MBP-RocCOR was incubated with different GTP concentrations, ranging from 75 µM to 8 mM, in the presence of [γ-<sup>32</sup>P] GTP in GTPase assay buffer (30 mM Tris pH 8, 150 mM NaCl, 10 mM MgCl<sub>2</sub>, 5% (v/v) Glycerol and 3 mM DTT). Samples were taken at different time-points and immediately quenched with 5% activated charcoal in 20 mM phosphoric acid. All non-hydrolyzed GTP and proteins were stripped by the activated charcoal and sedimented by centrifugation. The radioactivity of the isolated inorganic phosphates was then measured by scintillation counting. The initial rates of γ-phosphate release and the Michaelis-Menten kinetics were calculated as described above.</p> <p><strong>(2) Profile plots (Raw data) obtained for the Mass photometry analysis for T1343A vs WT LRRK2.</strong></p> <p>MP was performed as described in (Guaitoli et al., 2023).<strong> </strong>Briefly, the dimer ratio of LRRK2 was determined on a Refeyn Two MP instrument (Refeyn). Prior to the experiment, a standard curve relating particle contrasts to molecular weight was established using a Native molecular weight standard (Invitrogen, 1:200 dilution in HEPES-based elution buffer: 50 mM HEPES [pH 8.0], 150 mM NaCl supplemented with 200 µM desthiobiotin). Prior to mass photometry, the proteins, either WT or T1343A LRRK2, were incubated with 0.5 mM ATP or buffer (control) for 30 min at 30 ℃. The LRRK2 protein was diluted to 2x of the final concentration (end concentrations: 75 nM and 100 nM) in elution buffer. The optical setup was focused in 10 μl elution buffer before adding 10 µl of the adjusted protein sample. Depending on the obtained count numbers, acquisition times were chosen between 20 s to 1 min. The dimer ratio in each measurement was normalize according to the equation. The measurement was perfomed in triplicates.</p> <p><strong>(3) AlphaFold3 model of LRRK2-pT1343 either bound to GDP/Mg or GTP/Mg.</strong></p> <p>Using AlphaFold3 (Abramson et al., 2024), we modeled and compared the GDP vs the GTP-state of phospho-T1343 LRRK2. Interestingly, the AlphaFold3 model suggests, that the phosphate group of the pT1343 residue is orientated inwards thereby substituting the gamma phosphate of the GTP in the GDP-bound state of LRRK2. This finding is in well agreement with MD simulations published recently (Stormer et al., 2023).</p> <p><strong>(4) Western blot RAW files for the cell-based phospho Rab asssay (RAW data for Figure 6 supplement 2/ Supplemental Figure 4 in the preprint version, Gilsbach et al, 2024)</strong></p> <p>Cell-based LRRK2 activity assays were performed as previously described (Singh et al., 2022). Briefly,<strong> </strong>HEK293T cells were cultured in DMEM (supplemented with 10% Fetal Bovine Serum and 0.5% Pen/Strep). For the assay, the cells were seeded onto six-well plates and transfected at a confluency of 50-70% with SF-tagged LRRK2 variants using PEI-based lipofection. After 48 hours cells were lysed in lysis buffer [30 mM Tris-HCl (pH7.4), 150 mM NaCl, 1% NonidentP-40 substitute, complete protease inhibitor cocktail, PhosStop phosphatase inhibitors (Roche)]. Lysates were cleared by centrifugation at 10,000 x g and adjusted to a protein concentration of 1 µg/µl in 1x Laemmli Buffer. Samples were subsequently subjected to SDS PAGE and Western Blot analysis to determine LRRK2 pS935 and Rab10 T73 phosphorylation levels, as described below. Total LRRK2 and Rab10 levels were determined as a reference for normalization. For Western blot analysis, protein samples were separated by SDS–PAGE using NuPAGE 10% Bis-Tris gels (Invitrogen) and transferred onto PVDF membranes (Thermo Fisher). To allow simultaneous probing for LRRK2 on the one hand and Rab10 on the other hand, membranes were cut horizontally at the 140 kDa MW marker band. After blocking non-specific binding sites with 5% non-fat dry milk in TBST (1 h, RT) (25 mM Tris, pH 7.4, 150 mM NaCl, 0.1% Tween-20), membranes were incubated overnight at 4°C with primary antibodies at dilutions specified below. Phospho-specific antibodies were diluted in TBST/ 5% BSA (Roth GmbH). Non-phospho-specific antibodies were diluted in TBST/ 5% non-fat dry milk powder (BioRad). Phospho-Rab10 levels were determined by the site-specific rabbit monoclonal antibody anti-pRAB10(pT73) (Abcam, ab230261) and LRRK2 pS935 was determined by the site-specific rabbit monoclonal antibody UDD2 (Abcam, ab133450), both at a dilution of 1:2,000. Total LRRK2 levels were determined by the in-house rat monoclonal antibody anti-pan-LRRK2 (clone 24D8; 1:10,000) (Carrion et al., 2017). Total Rab10 levels were determined by the rabbit monoclonal antibody anti-RAB10/ERP13424 (Abcam, ab181367) at a dilution of 1:5,000. For detection, goat anti-rat IgG or anti-rabbit IgG HRP-coupled secondary antibodies (Jackson ImmunoResearch) were used at a dilution of 1:15,000 in TBST/ 5% non-fat dry milk powder. Antibody–antigen complexes were visualized using the ECL plus chemiluminescence detection system (GE Healthcare) using the Stella imaging system (Raytest) for detection and quantification.</p> <p><strong>Figure 6 Source Data 1:</strong> <span>Images generated by the Stella system are shown which were used for quantification. The annotation file equals Figure6-figure supplement 2 (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083). The lines corresponding to </span>LRRK2 pS935, total LRRK2, Rab10 pT73 and total Rab10 were <span>used for the quantification shown in Figure 6.</span></p>
IV-KAPhE kinase-substrate assignments for the entire human phosphoproteome
<p>This data set includes the full, all-vs-all kinase-substrate assignments by the IV-KAPhE method for the entire human phosphoproteome (union of the PhosphoSitePlus human phosphosite database and the Ochoa et al. 2020 high-confidence human phosphoproteome). This is an unfiltered version of Supplemental Table S1 from Invergo BM (2022) "Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data".</p> <p>The data set also includes files to facilitate scoring new human phosphosites, particularly the in vitro half of the IV-KAPhE model. "naive-bayes-plus-model.tar.gz" is an archive of HDF5 files comprising the "Naive Bayes+" multi-label, in vitro kinase-substrate assignment model used in the IV-KAPhE model, as described in the manuscript. These files are to be used with the motif-kit software package and can be used to score new sites. "kinase-int-domains-sig.tsv" and "kinase-sub-domains-sig.tsv" contain Pfam domains enriched among each kinase's interacting partners or substrates, respectively. Finally, "human-kinase-interactions.tsv" and "human-kinase-2nd-interactions.tsv" contain physical interactions and indirect ("2 hop") interactions between human protein kinases and other proteins, as described in the manuscript.</p>
Dataset for the Casein kinase II subunit alpha antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing ten antibodies for Casein kinase II subunit alpha protein. The study is available on Zenodo (<a href="https://doi.org/10.5281/zenodo.10818214">https://doi.org/10.5281/zenodo.10818214</a>). </em></p> <p><em>The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).</em></p>
Interactions of the EphA2 Kinase Domain with a PIP2 containing membrane
<p>Last frames of atomistic simulations revealing the interactions of the transmembrane, juxtamembrane (JM), and kinase domains with the membrane. The structures highlight how the kinase domain is oriented relative to the membrane and how the JM region can modulate this interaction. These structures highlight the role of phosphatidylinositol phosphates (PIPs) in mediating the interaction of the kinase domain with the membrane and, conversely, how positively charged patches at the kinase surface and in the JM region induce the formation of nanoclusters of PIP molecules in the membrane.</p> <p>Analysis of the orientation of the kinase domain when bound to the PIP<sub>2</sub>-containing membrane suggests that there are two main modes of interaction. The predominant binding mode (inter1.pdb) involves the N-terminal lobe of the kinase domain. In this interaction mode, the activation loop of the kinase is accessible to phosphorylation. In the secondary mode (inter2.pdb), the interaction with the bilayer involves both the N- and C-terminal lobes of the kinase and thus the activation loop less accessible. </p>
Metabolome and proteome dataset from yeast kinase knock-outs
<p>The dataset comprised of processed data produced in Zelezniak at al, Cell Systems 2018 study, please see README.txt for the detailed description of files. </p> <p> </p> <p> </p>
Promiscuity cliffs (PCs), promiscuity cliff pathways (PCPs), and promiscuity hubs (PHs) formed by inhibitors of human kinases
<p>The PC, PCP, and PH data structures have been introduced for the analysis of compound promiscuity [1-3]. A comprehensive collection of PCs, PCPs, and PHs formed by kinase inhibitors covering more than 80% of the human kinome is made available. See readme.txt for more information regarding the provided files.</p> <p>References:</p> <ol> <li>Dimova, D.; Gilberg, E.; Bajorath, J. Identification and Analysis of Promiscuity Cliffs Formed by Bioactive Compounds and Experimental Implications. RSC Adv. 2017, 7, 58–66.</li> <li>Miljković, F.; Bajorath, J. Computational Analysis of Kinase Inhibitors Identifies Promiscuity Cliffs across the Human Kinome. ACS Omega 2018, 3, 17295–17308.</li> <li>Miljković, F; Vogt, M; Bajorath, J. Systematic Computational Identification of Promiscuity Cliff Pathways Formed by Inhibitors of the Human Kinome. J. Comput. Aided Mol. Des. 2019, in press, doi: doi.org/10.1007/s10822-019-00198-9</li> </ol>
Cyclin-Dependent Kinase-Like 5 (CDKL5); A Target Enabling Package
<p>The protein kinase CDKL5 is best known for harbouring loss of function mutations that cause a variant of Rett syndrome, CDKL5 deficiency disorder, that predisposes to seizures and mental retardation. Recent kinome-wide screening has identified CDKL5 as a therapeutic target to ameliorate acute kidney injury, which is a common complication of hospitalisation that can lead to chronic kidney disease. There are currently no proven treatments. We prepared recombinant proteins for the kinase domains of CDKL1, CDKL2, CDKL3 and CDKL5 and solved the structures of these kinases in complex with identified inhibitors at resolutions from 1.5 to 2.4 Å. Overall, the structures capture the kinases with both active and inactive conformations and provide a model to explain the effects of CDKL5 mutation. An <em>in vitro</em> kinase assay showed the importance of a C-terminal αJ helix for the activity of CDKL2 and CDKL3, but not CDKL1 and CDKL5. Functional analyses of the single orthologue in C. elegans CDKL-1 also suggested that CDKL proteins can limit cilia length, which could potentially contribute to the neurological defects in CDKL5 deficiency syndrome. AST-487 and ASC67 present inhibitors of CDKL5 that could be developed for treating acute kidney injury. However, future work is needed to improve selectivity in this drug development.</p>
Genome-wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase
<p>Primary data associated with the figure 1 of the manuscript "<strong>Genome wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase" </strong>(Herschel S. Dhekne, Francesca Tonelli, Wondwossen M. Yeshaw, Claire Y. Chiang, Charles Limouse, Ebsy Jaimon, Elena Purlyte, Dario Alessi, and Suzanne Pfeffer). </p> <p>These include </p> <p>- data (annotated .tiff exports) from Metamorph acquired spinning disk confocal microscope</p> <p>- sequencing data as fastq files .gz files from Hiseq or Miseq next generation sequencing, </p> <p>- graphs made using GraphPad Prism (.pzf files).</p> <p>- flow cytometry .fcs files and workspace files</p> <p>- <a href="https://zenodo.org/api/files/de4d1da1-785a-4f55-a542-876647a2a5ad/Figure%201-%20file%20names%20legend.xlsx">Figure 1- file names legend.xlsx</a> excel sheet explaining the details</p>
Classifying protein kinase conformations with machine learning: data
<p>This data collection accompanies the manuscript "Classifying protein kinase conformations with machine learning".</p> <p>It is created using the <a href="https://github.com/edikedik/kinactive">kinactive</a> v0.1 tool written in pure Python v3.10. <strong>Note that the data are provided for the reference and reproducibility purposes and will not be compatible with later versions of `kinactive` built upon <a href="https://github.com/edikedik/lXtractor">lXtractor</a> > 0.1.1.</strong> Refer to the <a href="https://kinactive.readthedocs.io/en/latest/index.html">kinactive documentation</a> for instructions on how to obtain an actualized version of the structural kinome collection.</p> <p>File descriptions:</p> <ul> <li>db_v3.tar.gz -- a structural kinome collection archive. One can unpack it and inspect the contents or load it into the Python interpreter using `kinactive` or `lXtractor` tools.</li> <li>db_af2.tar.gz -- an AlphaFold2 kinome collection for Swiss-Prot sequences.</li> <li>default_*_vs.tsv -- structure/sequence variables calculated with lXtractor and used in an interpretable ML pipeline.</li> <li>*_features.tsv -- lists of ranked features selected by the <a href="https://github.com/edikedik/eBoruta">eBoruta</a> tool for each classifier.</li> <li>Supplement_labels.tsv -- ML model predictions for each PK domain structure found in db_v3.</li> <li>predictions_af2.csv -- Active/Inactive and DFG labels predicted for domains in db_af2.</li> </ul> <p> </p>
Multiple sequence alignments of sensor histidine kinases and response regulators
<p>The two FASTA files contain multiple sequence alignments of sensor histidine kinase and response regulator sequences. The source sequences were obtained by BLAST, clustered with usearch and aligned with muscle. More details to be found in Multamäki et al. 2021.</p>
Type-II kinase inhibitors that target Parkinson's Disease-associated LRRK2
<p>This dataset includes all of the tabular data used in the tables and figures in the article. Additionally, we provide a table with key resources for data aquisition.</p> <p>Aberrant increases in kinase activity of leucine-rich repeat kinase 2 (LRRK2) are associated with Parkinson’s disease (PD). Numerous LRRK2-selective type-I kinase inhibitors have been developed and some have entered clinical trials. In this study, we present the first LRRK2-selective type-II kinase inhibitors. Targeting the inactive conformation of LRRK2 is functionally distinct from targeting the active-like conformation using type-I inhibitors. We designed these inhibitors using a combinatorial chemistry approach fusing selective LRRK2 type-I and promiscuous type-II inhibitors by iterative cycles of synthesis supported by structural biology and activity testing. Our current lead structures are selective and potent LRRK2 inhibitors. Through cellular assays, cryo-electron microscopy structural analysis, and in vitro motility assays, we show that our inhibitors stabilize the open, inactive kinase conformation. These new conformation-specific compounds will be invaluable as tools to study LRRK2’s function and regulation, and expand the potential therapeutic options for PD.</p>
Inhibition of Parkinson's Disease-related LRRK2 by type-I and type-II kinase inhibitors: activity and structures
<p>Mutations in Leucine Rich Repeat Kinase 2 (LRRK2) are a common cause of familial Parkinson’s Disease (PD), and a risk factor for the sporadic form. Increased kinase activity has been shown in both familial and sporadic PD patients. This has made LRRK2 kinase inhibitors a major focus of drug development efforts in PD. Although significant progress has been made in understanding the structural biology of LRRK2, there are no available structures of LRRK2 inhibitor complexes. To this end, we solved cryo-EM structures of LRRK2, wild-type and PD-linked mutants, bound to the LRRK2-specific type-I inhibitor MLi-2 and the broad-spectrum type-II inhibitor GZD-824. Our structures revealed LRRK2’s kinase in the active-like state, stabilized by type-I inhibitor interactions, and an inactive DYG-out type-II inhibitor complex. The structures also showed how inhibitor-induced conformational changes are affected by the N-terminal half of LRRK2. The structural models provide a template for the rational development of LRRK2 kinase inhibitors covering both canonical inhibitor binding modes.</p>
Kinodata-3D: an in silico kinase-ligand complex dataset for kinase-focused machine learning.
<p><strong>Project Description</strong></p> <p>Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity. <br>We provide kinodata-3D, a dataset of <strong>~138 000</strong> docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see <a href="https://github.com/volkamerlab/kinodata-3D-affinity-prediction">github.com/volkamerlab/kinodata-3D-affinity-prediction</a>).</p> <h2>Dataset</h2> <h3>1. Data</h3> <p>This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 33. In particular, we use kinase activity data as curated through the <a href="https://github.com/openkinome/kinodata">OpenKinome kinodata</a> project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a <a href="https://github.com/openkinome/kinase-docking-benchmark">kinase-docking benchmark</a> data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see <a href="https://github.com/volkamerlab/kinodata-3D" target="_blank" rel="noopener">kinodata-3D repository</a>).</p> <p>The final data set contains in total <strong>138 286</strong> deduplicated kinase-ligand pairs, covering <strong>~98 000</strong> distinct compounds and ~<strong>271</strong> distinct kinase structures.</p> <h3>2. File structure</h3> <p>The archive <strong>kinodata_3d.zip </strong>uses the following file structure</p> <blockquote> <p>data/raw<br> | kinodata_docked_with_rmsd.sdf.gz<br> | pocket_sequences.csv<br> | mol2/pocket<br> | 1_pocket.mol2<br> | ...</p> </blockquote> <p>The file <strong>kinodata_docked_with_rmsd.sdf.gz</strong> contains the docked ligand poses and the information on the protein-ligand pair inherited from <em>kinodata</em>. The protein pockets located in <strong>mol2/pocket</strong> are stored according to the MOL2 file format.</p> <p>The pocket structures were sourced from KLIFS (<a href="https://klifs.net" target="_blank" rel="noopener">klifs.net)</a> and complete the poses in the aforementioned SDF file. The files are named <strong>{klifs_structure_id}_pocket.mol2</strong>. The structure ID is given in the SDF file along with the ligand poses.</p> <p>The file <strong>pocket_sequences.csv </strong>contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.</p> <h3>3. Related code</h3> <p>The code used to create the poses can be found in the <a href="https://github.com/volkamerlab/kinodata-3D" target="_blank" rel="noopener">kinodata-3D repository</a>. The docking pipeline makes heavy use of the <a href="https://github.com/openkinome/kinoml" target="_blank" rel="noopener">kinoml</a> framework, which in turn uses <a href="https://www.eyesopen.com" target="_blank" rel="noopener">OpenEye's</a> Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by <a href="https://www.biorxiv.org/content/10.1101/2023.09.11.557138v1">Schaller et al. (<strong>2023</strong>). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. <em>bioRxiv</em>.</a></p>
ADP Glo Kinase assay for PI3KC3-C1
<p>ADP Glo kinase assay data from associated publication</p>
Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?
<p>Datasets related to publication: </p> <p>Shevchenko E, Pantsar T: Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?. <em><em>Biochem Soc Trans</em></em> 28 February 2022; 50 (1): 633–648</p> <p>https://doi.org/10.1042/bst20210837</p> <p> </p> <p> </p>
Candidate compounds from the design of covalent Bruton's tyrosine kinase (BTK) inhibitors via focused deep generative modeling
<pre>A total of 1491 candidate inhibitors for covalent inhibition of BTK are deposited as SMILES strings together with 34 known covalent BTK inhibitors used to guide generative computational design. The study will be reported in a publication in Molecules under the authors' names.</pre>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.