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55 results for “Binding Energy”
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Data from: Effective In vivo binding energy landscape illustrates kinetic stability of RBPJ-DNA binding
Open the record for dataset details and reuse information.
Data for: Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression
Open the record for dataset details and reuse information.
DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design
<p>This record provides the training and evaluation dataset for DSMBind</p> <p>Paper link: https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1.abstract</p> <p>Github repo: https://github.com/wengong-jin/DSMBind</p>
Molecular docking analysis was performed to assess the affinity of onalespib for their targets LOX, elucidating binding poses, protein interactions, and associated binding energies.
<p>To analyze the binding affinities and interaction modes between the drug candidates and their targets, we employed the Autodock Vina software [21]. Molecular structures of the candidate drugs and targets of hub genes were retrieved from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and Protein Data Bank database (http://www.rcsb.org/), respectively. <span>In the analysis of docking, the files for all proteins and molecules were converted to PDBQT format. Water molecules were removed and polar hydrogen atoms were added. The grid box was positioned at the center to encompass the protein domain, allowing for unrestricted movement of molecules.</span></p>
Data for "Thermal Desorption Kinetics, Binding Energies, and Entrapment of Methyl Mercaptan Ices"
<p>This data includes infrared and mass spectra for thermal desorption experiments of methyl mercaptan and methanol ices. There are also Gaussian 16 log files that were used for complementary analysis of the data (i.e. single/dimer molecule optimization+frequency and harmonic/anharmonic band strength calculations) using the M062X/cc-pVDZ or M062X/cc-pVQZ or B3LYP/cc-pVDZ levels of theory. </p>
Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-guest Binding: I. Standard Procedure
<p>All the initial structures as well as the Autodock docked ligand poses used and the computational results.</p>
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
<p>Dataset relative to Molecular dynamics simulation performed for the work "Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction".<br><br>The dataset contains data of 42 alchemical simulations and is subdivided in 4 zip files.<br><br>Folders are named following the scheme: system_configuration_forcefield.<br>Zip file C1 contains .mdp files used for all the simulations.<br><br>Folders corresponding to simulations performed with the fit5_AC ff contains:<br>- topology files (topol.top, topol_RNA_chain_A.itp, topol_RNA_chain_B.itp)<br>- index files needed to reconstruct the demuxed trajectories (replica_index.xvg , replica_index.xvg)<br>- 16 folders, one for each replica (lam0 ... lam15), containing:<br> - final configuration (confout.gro)<br> - log file (md.log)</p> <p> - input file for md run (md.tpr)<br> - energies for the concatenated trajectories recomputed for the realtive replica hamiltonian (ener_trj_conc.edr)<br><br>Folders corresponding to simlations performed with fit_A parametrization only contains .edr files corresponding to energies for the concatenated trajectory computed for 14 set of DeQs drawn from a gaussian distribution, with the relative topologies.<br><br>Supplementary materials relative to simlations performed with fit_A parametrizationcan be found in: https://zenodo.org/records/6498021</p>
Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method
<p>This data includes the HF-optimized orbitals, coupled cluster amplitudes (T1, T2), and EOM-CCSD left and right eigenvectors at the CC-PCVDZ basis set. It can be used to reproduce the data for "Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method."</p>
Dataset 3 for paper: "Estimation of free energy of ligand binding using Multi-eGO"
<p>Dataset 3 contains Abeta42 dataset:</p> <ul> <li>APO: reference and multi-eGO simulations</li> <li>HOLO: reference and multi-eGO simualtions</li> <li>Titration at multiple concentrations with two different multi-eGO parameters</li> </ul>
Dataset 2 for paper "Esimation of free energy of ligand binding using Multi-eGO"
<p>Dataset2 contains Kinase dataset and part of Lysozyme-Benzene dataset:</p> <p>LYZ-BNZ:</p> <ul> <li>Unbiased binding simulations</li> </ul> <p>Kinase:</p> <ul> <li>APO trainng, reference, and multi-eGO simulations</li> <li>HOLO training, reference and multi-eGO simulations of both Dasatinib and PP1</li> <li>Thermodynamic integration of both Dasatinib and PP1</li> </ul> <p> </p> <p> </p>
Dataset 1 for paper: "Estimation of free energy of ligand binding using Multi-eGO"
<p>The dataset 1 contains Lysozyme-Benzene dataset:</p> <ul> <li>APO training, reference and multi-eGO simulations</li> <li>HOLO training, reference and multi-eGO simulations</li> <li> Thermodynamic integration calculations, Volume based metadynamics</li> </ul>
Biexciton binding energy and line width of single quantum dots at room temperature
<p>Data and vector images for main-text figures of S.J.W. Vonk, Bart A.J. Heemskerk, Robert C. Keitel, Stijn O.M. Hinterding, Jaco J. Geuchies, Arjan J. Houtepen & Freddy T. Rabouw, Biexciton binding energy and line width of single quantum dots at room temperature, <em>Nano Lett.</em> <strong>21</strong>, 5760–5766 (2021).</p>
Acetaldehyde binding energies: a coupled experimental and theoretical study
<p>CRYSTAL17 output files of the atomic structures used in the related publication</p>
A computational workflow for binding free energies in Python
<p>Dataset of distances between a host and six different ligands. The host was beta-cyclodextrin (bCD), while the ligands were phenol, benzene, aspirin, toluene, chlorobenzene and 1,3-dichlorobenzene. No bonds were frozen. </p> <p>The ligand were set to move with a step of 0.25 angstrom from -26 to 26 relative to the bCD (a total of 208 distances). At each distance, a energy biasing potential <span class="math-tex">\(E_{bias}\)</span> was applied the keep two molecules in place. </p> <p><span class="math-tex">\(E_{bias} = \frac{1}{2}\cdot K \cdot (R - R_0)^2\)</span></p> <p>The parameters of the ligands were taken from OpenFF while GLYCAM were used for the host bCD. All of it were applied in Python and the OpenMM framework. Starting parameters, pdb-, and sdf-files can be found in the start folder.</p>
Supporting Information for "Broadening the scope of binding free energy calculations using a Separated Topologies approach"
<p>Supporting Information for the publication "Broadening the scope of binding free energy calculations using a Separated Topologies approach" including input files for the datasets used in that study.</p>
Figure 5 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 5 Docking interaction and binding mode of palmatine and alpha-amylase enzyme. The π-π staked interactions are presented in magenta dotted lines.
Figure 8 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 8 Docking interaction and binding mode of sitagliptin and DPP-IV enzyme. The hydrogen bonds are presented in green dotted lines, the π-π staked interactions are presented in magenta dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Figure 4 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 4 Docking interaction and binding mode of acarbose and alpha-amylase enzyme. The hydrogen bonds are presented in green dotted lines.
Figure 7 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 7 Docking interaction and binding mode of palmatine and alpha-glucosidase enzyme. The hydrogen bonds are presented in green dotted lines and the π-alkyl interactions are presented in pink dotted lines.
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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.