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85 results for “Ligand Binding”
[PART 3] A twist of fate: the helix-turn-helix motif in Pseudomonas aeruginosa ExsA can allosterically stabilize the ligand-binding domain
<p>1273- ExsA - Dimer1 without DNA - alphaFold compact - 230ns/day 10x500ns<br>*1274- ExsA - Dimer2 without DNA - xtal extended (like 1026) - 10x1µs (not everything is 1 µs)<br>1275- ExsA - Dimer3 without DNA - made by hand / 150ns/day (reps 1-5 broken, reps 6-10 correct)<br>*1276- ExsA- monomer - Site1-PexoT - 240 ns/day - 10x1µs (some replicas have pieces missing)<br>*1277- ExsA- monomer - Site2-PexoT - 10x1µs<br>*1278- ExsA - Dimer2 with broken DNA - xtal extended (like 1026) - 5x200ns?</p>
[PART 2] A twist of fate: the helix-turn-helix motif in Pseudomonas aeruginosa ExsA can allosterically stabilize the ligand-binding domain
<p>1273- ExsA - Dimer1 without DNA - alphaFold compact <br>1274- ExsA - Dimer2 without DNA - xtal extended <br>1275- ExsA - Dimer3 without DNA - made by hand (reps 1-5 broken, reps 6-10 correct)<br>1276- ExsA- monomer - Site1-PexoT <br>1277- ExsA- monomer - Site2-PexoT<br>1278- ExsA - Dimer2 with broken DNA</p>
Data from: 'Venus trapped, Mars transits': Cu and Fe redox chemistry, cellular topography and in situ ligand binding in terrestrial isopod hepatopancreas
Woodlice efficiently sequester copper (Cu) in 'cuprosomes' within hepatopancreatic 'S' cells. Binuclear 'B' cells in the hepatopancreas form iron (Fe) deposits; these cells apparently undergo an apocrine secretory diurnal cycle linked to nocturnal feeding. Synchrotron-based m-focus X-ray spectroscopy undertaken on thin sections was used to characterize the ligands binding Cu and Fe in S and B cells of Oniscus asellus (Isopoda). Main findings were: (i) morphometry confirmed a diurnal B-cell apocrine cycle; (ii) X-ray fluorescence (XRF) mapping indicated that Cu was co-distributed with sulfur (mainly in S cells), and Fe was co-distributed with phosphate (mainly in B cells); (iii) XRF mapping revealed an intimate morphological relationship between the basal regions of adjacent S and B cells; (iv) molecular modelling and Fourier transform analyses indicated that Cu in the reduced Cuþ state is mainly coordinated to thiol-rich ligands (Cu–S bond length 2.3 A˚ ) in both cell types, while Fe in the oxidized Fe3þ state is predominantly oxygen coordinated (estimated Fe–O bond length of approx. 2 A˚ ), with an outer shell of Fe scatterers at approximately 3.05 A˚ ; and (v) no significant differences occur in Cu or Fe speciation at key nodes in the apocrine cycle. Findings imply that S and B cells form integrated unit-pairs; a functional role for secretions from these cellular units in the digestion of recalcitrant dietary components is hypothesized.
The impact of the cross-docked poses on the performance of machine learning classifier for protein-ligand binding pose prediction
<p>Datasets, features, and some representative scripts utilized in the paper "The impact of the cross-docked poses on the performance of machine learning classifier for protein-ligand binding pose prediction".</p>
Ligand binding remodels protein side chain conformational heterogeneity
<p>While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.</p>
Fragment screening using biolayer interferometry reveals ligands targeting the SHP-motif binding site of the AAA+ ATPase p97
<p>Biosensor techniques have become increasingly important for fragment-based drug discovery during the last years. The AAA+ ATPase p97 is an essential protein with key roles in protein homeostasis and a possible target for cancer chemotherapy. Currently available p97 inhibitors address its ATPase activity and globally impair p97-mediated processes. In contrast, inhibition of cofactor binding to the N-domain by a protein-protein-interaction inhibitor would enable the selective targeting of specific p97 functions. Here, we describe a biolayer interferometry-based fragment screen targeting the N-domain of p97 and demonstrate that a region known as SHP-motif binding site can be targeted with small molecules. Guided by molecular dynamics simulations, the binding sites of selected screening hits were postulated and experimentally validated using protein- and ligand-based NMR techniques, as well as X-ray crystallography, ultimately resulting in the first structure of a small molecule in complex with the N-domain of p97. The identified fragments provide insights into how this region could be targeted and present first chemical starting points for the development of a protein-protein interaction inhibitor preventing the binding of selected cofactors to p97.</p> <p><br> Here we publish the primary data of the biolayer-interferometry (BLI), STD-NMR, HSQC-NMR and mixed-solvent MD simulation results.<br> For BLI, the sensorgrams of 10 binding fragments identified in the screening are given. Measurements were conducted with the N-domain and the ND1-construct of p97. Additionaly, the measurements with ADP as positive control (n=6) are provided.<br> Four of the 10 fragments were selected as most promising hits for further investigation: TROLL2, TROLL7, TROLL8 and TROLL12. For these fragemnts, the STD-NMR build-up data, the HSQC-NMR spectra and the representative poses from the mixed solvent MD simulations used for CORCEMA predictions are provided. For TROLL2 the anomalous map of the bromine atom of the crystal structure (PDB entry: 7PUX) is given.</p>
Data snapshot from the Protein Ligand Binding Database (PLBD)
<p>Data snapshot from the Protein Ligand Binding Database (PLBD)<br> =============================================================</p> <p>This data set contains tabular data snapshots from the PLBD. Data are provided in the time-stamped JSON [1] files.</p> <p>The time stamp in all files (the first line of each data file) records the moment when the data tables were retrieved from the PLBD server. Since the PLBD is a constantly evolving database, it is recommended that the latest data from the PLBD server [2] are downloaded and used, unless historic data are needed. The provided Makefile illustrates a possible download procedure. The tables here are provided solely to comply with the editorial policies of the "Scientific Data" journal.</p> <p>The ".json" files in the "outputs/main" directory contain the data; the ".nrec" files contain the number of records in the PLBD tables at the moment of download (a single decimal integer recorded on the second line of each ".nrec" file) and were used to request the the appropriate number of rows when downloading the "outputs/main/*.json" files.</p> <p>The meaning and the data type descriptions of the JSON file attributes are provided in the database description file "main-database-description.xml". This file is deposited as a separate digital object in Zenodo [3]. The layout of the JSON files follows ideas described in the JSON:API schema [4].</p> <p>Layout of the archived file tree<br> --------------------------------</p> <p> .<br> ├── Makefile<br> ├── README<br> ├── README.pdf<br> ├── inputs<br> │ ├── tables.lst<br> │ ├── tables.lst.history<br> │ └── tables.lst.log<br> ├── makefiles<br> │ ├── available<br> │ │ ├── Makelocal-download-json<br> │ │ └── Makelocal-download-nrec<br> │ └── enabled<br> │ ├── Makelocal-download-json<br> │ └── Makelocal-download-nrec<br> └── outputs<br> └── main [52 entries exceeds filelimit, not opening dir]</p> <p>References:</p> <p>1. ECMA Standard. The JSON data interchange syntax. 2017, 1-16, URL: https://www.ecma-international.org/publications/files/ECMA-ST/ECMA-404.pdf</p> <p>2. The Protein Ligand Binding Database (PLBD) server. URL: https://plbd.org/db/main [accessed: 2022-12-21T09:29+02:00]</p> <p>3. Lingė, D.; Gedgaudas, M.; Merkys, A.; Petrauskas, V.; Vaitkus, A.; Grybauskas, A.; Paketurytė, V.; Zubrienė, A.; Zakšauskas, A.; Mickevičiūtė, A.; Smirnovienė, J.; Baranauskienė, L.; Čapkauskaitė, E.; Dudutienė, V.; Urniežius, E.; Konovalovas, A.; Kazlauskas, E.; Gražulis, S. & Matulis, D. PLBD (Protein Ligand Binding Database) table description XML file. Zenodo, 2022, DOI: https://doi.org/10.5281/ZENODO.7482008</p> <p>4. Katz, Y.; Gebhardt, D.; Sullice, G.; Hanschke, J.; Kellen, T.; Klabnik, S. & Resnick, E. JSON:API version 1.1. 2022 URL: https://jsonapi.org/format/1.1/ [accessed: 2022-12-25T16:04+02:00]</p>
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
<p><span>Cancer cells bypass cell death by changing the expression of the BCL-2 family of proteins, which are apoptotic pathway regulators. Upregulation of pro-survival BCL-2 proteins or downregulation of cell death effectors BAX and BAK interferes with the initiation of the intrinsic apoptotic pathway. In normal cells, apoptosis can occur through pro-apoptotic BH3-only proteins interacting and inhibiting pro-survival BCL-2 proteins. When cancer cells over-express pro-survival BCL-2 proteins, a potential remedy is the sequestration of these pro-survival proteins through a class of anti-cancer drugs called BH3 mimetics that bind in the hydrophobic groove of pro-survival BCL-2 proteins. To improve the design of these BH3 mimetics, the packing interface between BH3 domain ligands and pro-survival BCL-2 proteins was analyzed using the Knob-Socket model to identify the amino acid residues responsible for interaction affinity and specificity. A Knob-Socket analysis organizes all the residues in a binding interface into simple 4 residue units: 3-residue sockets defining surfaces on a protein that pack a 4th residue knob from the other protein. In this way, the position and composition of the knobs packing into sockets across the BH3/BCL-2 interface can be classified. A Knob-Socket analysis of 19 BCL-2 protein and BH3 helix co-crystals reveal multiple conserved binding patterns across protein paralogs. Conserved knob residues such as a Gly, Leu, Ala and Glu most likely define binding specificity in the BH3/BCL-2 interface, whereas other residues such as Asp, Asn, and Val are important for forming surface sockets that bind these knobs. These findings can be used to inform the design of BH3 mimetics that are specific to pro-survival BCL-2 proteins for cancer therapeutics.</span></p>
DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein–Ligand Binding Affinity Prediction
<p><strong>structures.zip:</strong> including the coordinates of all optimized proteinligand complex structure obtained by DOX_BDW calculation. (compressed PDB file). These pdb files could also be used as input for the binding energy calculation,as illustrated in SI section 8. </p> <p><strong>mdinput.zip:</strong> Including the input files,parameter files, topology files needed to run MD simulation for water mapping, as illustrated in SI section 8. Note that all of the parameter files and topology files would be automatically generated using the RUNMD program we uploaded with the example file. </p> <p><strong>example.zip:</strong> The programs and input files needed to run an example, as illustrated in SI section 9. And all the output files except MD trajectories are in there,too.</p>
Fig. 4 in Benzofuran and coumarin derivatives from the root of Angelica dahurica and their PPAR-γ ligand-binding activity
Fig. 4. PPAR-γ ligand binding activity of 1–21. Pioglitazone (PG): 50 (■) and 5.0 (□) μM, 1–21: 500 and 50 μM. Data are represented as the mean ± S.E.M. (n = 3). Dunnett's test was used for statistical analysis. ***p <0.001, **p <0.01, *p <0.05.
Dataset to Carbon nanotube field-effect transistor for resolving single-molecule aptamer-ligand binding kinetics
<p>Raw data of single molecular field effect transistor data from recordings of aptamer serotonin interactions.</p>
A Phase I Study of a DNA Vaccine Encoding Androgen Receptor Ligand-Binding Domain (AR LBD) +/-GMCSF
ClinicalTrials.gov study NCT02411786. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
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Data from: ‘Venus trapped, Mars transits’: Cu and Fe redox chemistry, cellular topography and in situ ligand binding in terrestrial isopod hepatopancreas
Open the record for dataset details and reuse information.
Molecular dynamics simulation data of designed cyclic peptide (ligand-binding)
<p>Trajectories of ligand binding simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. <br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
A Folding–Docking–Affinity framework for protein–ligand binding affinity prediction
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Biophysical characterization of calcium-binding and modulatory-domain dynamics in a pentameric ligand-gated ion channel
<p><strong>Molecular dynamics simulations</strong></p> <p>Trajectories and related files.</p>
Supplementary Information: Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors
<p>Supplementary Information: Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors</p>
Fig. 3 in Benzofuran and coumarin derivatives from the root of Angelica dahurica and their PPAR-γ ligand-binding activity
Fig. 3. Key HMBC correlations and ORTEP drawing of the crystal structure of 7.
Fig. 2 in Benzofuran and coumarin derivatives from the root of Angelica dahurica and their PPAR-γ ligand-binding activity
Fig. 2. Key HMBC correlations of 1.
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OpenNeuro
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