Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
85
datasets available to search
ShareScore release 0.9.0
Dataset results
85 results for βLigand Bindingβ
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, ππβππ πβπ¨π (πππ’ππ), as a function of the overall mole fraction of oleylphosphonic acid (= unbound phosphonic acid and bound phosphonate), ππβππ πβπ¨π (π‘ππ‘ππ) , 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>
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>
Do ZnF-UBD ligands bind full length USP5?
<p>A surface plasmon resonance (SPR) assay was used to determine the binding affinities of USP5 zinc finger ubiquitin-binding domain (ZnF-UBD) ligands to full length (FL) USP5<sup>1-835 </sup></p>
The dataset used in the article "A point cloud graph neural network for protein-ligand binding site prediction"
Open the record for dataset details and reuse information.
Simulation and analysis data set for apo-conformational kinetics and gated ligand binding to HIV-1 protease
<p>The data set provided here accompanies a study described in the manuscript:</p> <p>S. Kashif Sadiq, Abraham Muñiz Chicharro, Patrick Friedrich, Rebecca Wade, A multiscale approach for computing gated ligand binding from molecular dynamics and Brownian dynamics simulations. (2021) Preprint available: https://doi.org/10.1101/2021.06.22.449380</p> <p>This study combines molecular dynamics MD simulations and associated conformational analyses and Markov state models (MSMs) with Brownian dynamics (BD) simulations to compute conformation gated ligand association kinetics to HIV-1 protease.</p> <p>To download the data, go to a directory where you would like to download the files. Then for each of the provided tar files enter the following command:</p> <p>tar xvf $X.tar</p> <p>where $X is the name prefix of the corresponding tar file.</p> <p>The unpacked data set creates a ./data sub-directory which itself contains two further sub-directories: MD and BD. Please see README.txt files within these sub directories for further instructions on the software tools and scripts that have been provided therein for using and reproducing the data set. The MD README.txt is found within: data_MD_MSM_analysis.tar, the BD README.txt is found within: data_BD_examples.tar.</p> <p>Please note, the python Jupyter notebook and associated module for further analysis of the MSM from the pre-defined feature set calculated in the study as well as other analyses can also be found at:</p> <p>https://github.com/kashifsadiq/hiv1pr-msm/</p> <p>MD trajectory files are provided for further analysis but are not required to reproduce the MSM and conformational analyses reported in the study. To facilitate overview, MSM analysis has been stored in several object files. To exactly reproduce the reported MD/MSM analyses, untar only the 1) data_MD_MSM_analysis.tar and 2) data_MD_MSMobj.tar files and work through the python Jupyter notebook.</p> <p> </p>
Dataset - Exploring Ligand Binding to Calcitonin Gene-Related Peptide Receptors
<p>Spervised molecular dynamics simulations (SuMD) of:</p> <p>- CGRP binding to CGRPR TMD</p> <p>- telcagepant binding to CGRPR ECD</p> <p>- telcagepant unbinding from CGRPR ECD</p> <p>(Water molecules, POPC and ions removed)</p>
Consensus machine-learning models for protein-ligand binding affinity estimation
<p><strong>Motivation:</strong> In structure-based virtual screening, machine learning based scoring function gained popularity in the last few years as they outperformed classical scoring function. The protein-ligand system can be encoded by a set of orthogonal descriptor spaces, which are then mined by machine learning algorithms to find a relationship with the binding affinity experimental value.</p> <p><strong> </strong></p> <p><strong>Results:</strong> In this work we propose our modelling approach to derive a new scoring function, derived from a combination of multiple descriptor spaces coupled with machine learning algorithms ensembled in consensus. The SF has been trained on the PDBbind v.2019 data and has been extensively internally and externally validated on a large set of complexes. When benchmarked on the PDBbind core set, it achieved better performance than state-of-the-art counterparts, scoring: R<sub>Pearson </sub>= 0.85-0.86 r<sup>2</sup> = 0.70-0.72 and RMSE = 1.15-1.21. As highlights: (i) an applicability domain definition has been implemented to delimit the SF’s application boundaries, and (ii) a mechanistic interpretation is proposed by investigating the contribution of each protein-ligand atom pairs in the prediction of the binding affinity, which could provide a support in the lead-optimization process.</p> <p><strong> </strong></p> <p><strong>Availability and implementation:</strong> Our scoring function is freely available through the webportal: <a href="https://predictor.exscalate.eu/">https://predictor.exscalate.eu/</a></p>
Mechanism of rotenone binding to respiratory complex I depends on ligand flexibility
<p>Snapshots from MD simulations (with umbrella sampling and metadynamics) of<br> respiratory complex I. Only subunits ND1, NDUFS2 and NDUFS7 (with the membrane spanning N-terminus<br> truncated) and rotenone are shown in binding mode (see Fig. 2 of main paper): </p> <p>1) ROT1: configurations/rot1.pdb<br> 2) pre-redox: configurations/rot1.5.pdb<br> 3) ROT2: configurations/rot2.pdb</p> <p>Force-field and topology files in GROMACS format:</p> <p>4) Bonded parameters: parameters/rotenoids_ffBonded.itp<br> 5) Rotenone: parameters/rot.itp<br> 6) Dehydrated derivative: parameters/dehyd_rot.itp</p>
PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening
<p>Training and test datasets of the paper "Improving the versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening".</p>
Exploring the ligand binding and conformational dynamics of the substrate binding domain 1 of the ABC transporter GlnPQ
Open the record for dataset details and reuse information.
GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction
<p>The preprocessed dataset for paper "GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction" with associated code at https://github.com/Mercuryhs/GAABind.</p><p>The dataset files are saved as .pkl file for the convenience of use.</p><p><strong>Paper Abstract</strong>:</p><p>Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket- ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset, the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the SARS-CoV-2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.</p>
Fuzzy Supertertiary Interactions within PSD-95 Enable Ligand Binding
<p>This file details the folder structure for the data files provided corresponding to the experiments and analysis performed in <em>Fuzzy Supertertiary Interactions within PSD-95 Enable Ligand Binding</em>. Correction parameters, results, and methods are detailed in the manuscript. The file “Sample Reference.xlsx” details the naming conventions for different FRET labeling site variants. “RefitDistanceTables.xlsx” provides the distances for all global fits of subsets of samples performed for classification of DMD structures.</p> <p> </p> <p>The .zip file "PDBDev_adtl_Datasets.zip" Contains additional files corresponding to submissions of structures from the corresponding manuscript to wwPDB-Dev.</p> <table> <tbody> <tr> <td>Folder</td> <td> </td> </tr> <tr> <td><strong>1. Single Molecule Experimental Data</strong></td> <td>.ht3 and .ptu files corresponding to time-tagged photon records collected using single-photon detectors with Hydra Harp TCSPC software and hardware. Folders are provided for each sample. Selection criteria and correction parameters are detailed in the manuscript. DO data were taken from bursts with S<sub>PIE</sub><0.1 while AO data were S<sub>PIE</sub>>0.9.</td> </tr> <tr> <td>1.1 BG</td> <td>Background files from which background signals were calculated for each sample. Subfolder for each sample.</td> </tr> <tr> <td>1.2 IRF</td> <td>IRF data files used for generation of IRF curves for fluorescence decay histogram analysis and generation of fFCS filters. Subfolder for each sample.</td> </tr> <tr> <td><strong>Fluorescence Decay Histograms</strong></td> <td>Data files for photon counts in each time bin following donor excitation pulse for Donor-only (DO), FRET (DA), and Instrument Response Function (IRF) curves used in fluorescence decay histogram analysis.</td> </tr> <tr> <td>2.1 Fit Curves</td> <td>Curves fit to decay histograms, in xyxy format (time delay after pulse, fit curve counts, time, residual)</td> </tr> <tr> <td><strong>Filtered FCS Curves and Filter Files</strong></td> <td>Data corresponding to four fFCS curves per sample: low-FRET-high-FRET (lohi), high-FRET-low-FRET (hilo), low-FRET-low-FRET (lolo), and high-FRET-high-FRET (hihi), as well as filter files generated in the Kristine software package using the .ht3 and .ptu data and IRF files from <strong>Single Molecule Experimental Data</strong>.</td> </tr> <tr> <td>3.1 Fits</td> <td>Fit curves and residuals for all samples. .cor files are simple text files in xy format (third column unused). .res, .fit files follow likewise.</td> </tr> <tr> <td><strong>Simulation AV-Derived Distance Values</strong></td> <td>Structures from DMD simulations and AV-derived interdye distances used for structural classification against experimental FRET data. Also includes references files for associating distances correctly with structures and FRET pairs.</td> </tr> <tr> <td>4.1 CoM AV Contours and PCA Data</td> <td>Files contain AV-derived distance information for all simulation structures from DMD, in compact npy format loadable via numpy.load. The text files contain center-of-mass positions in xyz format for the indicated domains.</td> </tr> <tr> <td><strong>5. MFD Histograms</strong></td> <td>Contains text files for MFD contour plot data, as well as FRET line equations for each sample. Text files are in matrix representation, with the first row and column being the x/y axis values and all internal values representing a 2D bin.</td> </tr> <tr> <td><strong>6. PDA Histograms</strong></td> <td>Contains time-binned histograms of smFRET data and modeling outputs from PDA fitting.</td> </tr> </tbody> </table> <p> </p>
Ligand Field Molecular Dynamics Simulation of Pt(II)-Phenanthroline Binding to N-Terminal Fragment of Amyloid-beta Peptide
<p>DL_POLY Classic input and output files for 10 MD simulations: 5 of free peptide, 5 with Pt(phen)</p>
Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction
<p>This Zenodo repository provides comprehensive resources for the paper titled "Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction" published on <a href="https://academic.oup.com/bioinformatics/article/41/8/btaf429/8238154">Bioinformatics</a>. We created a dataset of 63,000 molecular dynamics simulations by performing 10 simulations of 10 ns on 6,300 complexes. Neural networks were developed to learn from this data in order to predict the binding affinities of protein-ligand complexes. The implementation of these neural networks are available on <a href="https://github.com/ICOA-SBC/MD_DL_BA" target="_blank" rel="noopener">github</a>. Our collection includes training/benchmark datasets, trained statistical models, and results on test sets (CSV & PDF files).</p> <p> </p> <p><strong>Training/benchmark datasets:</strong></p> <p>Training, validation and test sets are provided to train and evaluate the following neural networks:</p> <ul> <li>Pafnucy, Proli and Densenucy without MD data augmentation (dataset file names contain "initial")</li> <li>Pafnucy, Proli and Densenucy with MD data augmentation (dataset file names contain "MDDA")</li> <li>Pafnucy with/without MD data augmentation and Proli and Densenucy with MD data augmentation were also evaluated on the fep test set (test set file name contain "fep")</li> <li>Timenucy and Videonucy using spatiotemporal learning methods (dataset file names contain "4D")</li> <li>Pafnucy without MD data augmentation and on a reduced training set (dataset file names contain "reduced")</li> </ul> <p>For each training methodology (MD data augmentation and spatiotemporal learning), we provide the data for the whole complex, only the ligand or only the protein. Additionally for spatiotemporal learning, we provide the data with only the ligand using the tracking mode.</p> <p> </p> <p><strong>Statistical models:</strong></p> <p>We provide the models trained with Pafnucy, Proli, Densenucy, Timenucy and Videonucy. Each models were trained in 10 replicates. </p> <p>For Pafnucy, Proli, Densenucy, we provide the models trained with random and systematic rotations, as well as with or without MD data augmentation.</p> <p>For Proli, Densenucy, Timenucy and Videonucy, we provide the models trained on the whole complex, only the ligand or only the protein.</p> <p>For Pafnucy we also provide the models trained on the reduced set (5932 complexes).</p> <p> </p> <p><strong>Results on test sets (CSV & PDF files):</strong></p> <p>We provide the predictions on the PDBbind v.2016 core set.</p> <ul> <li>For spatiotemporal learning methods (Timenucy and Videonucy), there are predictions for only 83 complexes, as we did not perform simulations on the whole test set.</li> <li>For models trained with MD DA, predictions were carried on the crystallographic structures as well as on the frames extracted from the simulations performed on the test set (augmented test).</li> </ul> <p>Results on the FEP dataset are also provided for Pafnucy, Proli and Densenucy.</p> <p> </p> <p>The Raw MD data (~4.5 To) are stored, and can be visualized/downloaded, on the <a href="https://mdposit.mddbr.eu/#/browse?search=MDBind">MDDB</a>.</p> <p>This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-A0100712496 & 2022-AD011013521) and CRIANN (Grant 2021002).</p>
Dataset S4-6: Leveraging co-evolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms
<h3>Significance statement:</h3> <p>This study presents proof-of-concept for a rapid and inexpensive alternative to classical structure-based approaches for resolving ligand-receptor binding mechanisms. It relies on a multilayered bioinformatic approach that leverages genomic data across diverse species in combination with AI-based structural modeling to identify true ligand and receptor homologues, and subsequently predict their binding mechanisms. <em>In silico </em>findings were validated by multiple experimental approaches, which investigated the effect of amino acid changes in the proposed binding pockets on ligand-binding, complex formation with a co-receptor essential for downstream signaling, and activation of downstream signaling. Our analysis combining evolutionary insights, <em>in silico</em> modeling and functional validation provides a framework for structure-function analysis of other peptide-receptor pairs, which could be easily implemented by most laboratories.</p> <h3><span>Zip file contains:</span></h3> <p><span>Dataset S4:</span><span> </span><strong><span>Plasmid maps of constructs used in this study.</span></strong></p> <p><span>Dataset S5:</span><span> </span><strong><span>AFM and AF3 predicted structures (.pdb) and AFM confidence metrics (.pae)</span></strong></p> <p><span>Dataset S6:</span><span> </span><strong><span>Unedited files (.tiff) of co-IP and western blotting.</span></strong></p>
Enthalpic classification of water molecules in target-ligand binding
<p>This repository contains supporting data for the manuscript entitled: Enthalpic classification of water molecules in target-ligand binding.</p>
Metal Binding to Amyloid Beta 1-42: A Ligand Field Molecular Dynamics Study
<p>DL_POLY Classic inputs and trajectories (PDB format) for 3 x free, Cu and Pt simulations</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>
ScienceDex guides
Understand access before you commit
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.