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13 results for “affinity ligands”
Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction
<p>The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."</p> <p> </p> <p><strong>experiment_results.zip:</strong> Contains generated results that can reproduce the result from the reported paper.</p> <p><strong>benchmark.zip:</strong> Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.</p> <p><strong>checkpoints.zip: </strong>Contains one weight for the Energy and four PoseScore and Affinity models.</p> <p><strong>source_code_1.0.zip:</strong> Contains the initial version of the source code.</p> <p><strong>interformer_train.tar.gz:</strong> Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.</p> <p><strong>baseline_results.tar.gz:</strong> Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the <em>eda/ </em>directory.</p> <p> </p> <p>You can also find the newest version of the source code at <a href="https://github.com/tencent-ailab/Interformer" target="_blank" rel="noopener">https://github.com/tencent-ailab/Interformer</a></p> <p> </p>
Target-ligand binding affinity from single point enthalpy calculation and elemental composition
<p>This repository contains supporting files for the manuscript entitled: Target-ligand binding affinity from single point enthalpy calculation and elemental composition.</p>
Data for "Binding Affinity of Monoalkyl Phosphinic Acid Ligands toward Nanocrystal Surfaces".
<p>Data of the figures in the publication "<strong>Binding Affinity of Monoalkyl Phosphinic Acid Ligands toward Nanocrystal Surfaces</strong>".</p> <p>The <em>.pxp</em> documents contain the experimental data of the figures in the manuscript and they can be opened/edited with the software IGOR Pro 6.3 or higher.</p> <p>Table of contents:</p> <p><strong>Figure 1.</strong> (A) General reaction scheme toward monoclinic HfO<sub>2</sub>/oleate NCs. (B) TEM image and (C) DOSY NMR spectrum in C<sub>6</sub>D<sub>6</sub> of HfO<sub>2</sub>/oleate NCs. (D) General reaction scheme toward zinc blend CdSe/oleate NCs. (E) TEM image and (F) DOSY NMR spectrum in C<sub>6</sub>D<sub>6</sub> of CdSe/oleate NCs.</p> <p><strong>Figure 2.</strong> (Left) Titration of HfO<sub>2</sub>/oleate with 6-(hexyloxy)hexylphosphinic acid. (A) General reaction scheme. (B) <sup>1</sup>H NMR spectra of the titration. (C) <sup>31</sup>P NMR after 0.95 and 1.15 equiv of phosphinic acid is added. (D) Quantification of the different compounds as a function of the added equivalents. (Right) Titration of CdSe/oleate with 6-(hexyloxy)hexylphosphinic acid. (E) General reaction scheme. (F) <sup>1</sup>H NMR spectra of the titration. (G) <sup>31</sup>P NMR after 1.0 and 1.2 equiv of phosphinic acid is added. (H) Quantification of the different compounds as a function of the added equivalents.</p> <p><strong>Figure 3.</strong> (A) <sup>1</sup>H NMR spectrum of oleate-capped HfO<sub>2</sub> NCs in C<sub>6</sub>D<sub>6</sub>. (B) <sup>1</sup>H NMR spectrum of HfO<sub>2</sub> in C<sub>6</sub>D<sub>6</sub> after ligand exchange for 6-(hexyloxy)hexylphosphinic acid and purification. The inset shows the <sup>31</sup>P NMR spectrum.</p> <p><strong>Figure 4.</strong> (Left) Titration of HfO<sub>2</sub>/[6-(hexyloxy)hexyl]phosphinate with oleylphosphonic acid. (A) General reaction scheme. (B) <sup>1</sup>H NMR spectra of the titration. (C) Quantification of the different compounds as a function of the added equivalents. Note that the small amount of residual oleic acid present at the start of the titration in (B) is due to a challenging purification (high solubility of the HfO<sub>2</sub>/[6-(hexyloxy)hexyl]phosphinate NCs). This small signal was integrated and subtracted from the spectra for the quantification in (C). (Right) Titration of CdSe/[6-(hexyloxy)hexyl]phosphinate with oleylphosphonic acid. (D) General reaction scheme. (E) <sup>1</sup>H NMR spectra of the titration. (F) Quantification of the different compounds as a function of the added equivalents.</p> <p><strong>Figure 5.</strong> Mole fraction of bound oleylphosphonate in the ligand shell, χ<sub>phosphon (bound)</sub>, as a function of the overall mole fraction of oleylphosphonic acid (=unbound phosphonic acid and bound phosphonate), χ<sub>phosphon (total)</sub>, during the titrations of HfO<sub>2</sub> (blue), CdSe (red), and ZnS (green) NCs. The full lines represent different calculated equilibrium constants.</p> <p><strong>Figure 6.</strong> Mole fraction of a bound incoming ligand (= titrating ligand) in the ligand shell, χ<sub>incomingligand (bound)</sub>, as a function of the overall mole fraction of the total incoming ligand (=unbound and bound), χ<sub>incomingligand (total)</sub>, during the titrations of ZnS NCs stabilized with a 50/50 mixture of <em>n</em>-hexyl/<em>n</em>-octadecylphosphinate with oleylphosphonic acid (gray), ZnS NCs stabilized with 6-(hexyloxy)hexylphosphinate with oleylphosphonic acid (green) and ZnS NCs stabilized with oleate with a 50/50 mixture of <em>n</em>-hexyl/<em>n</em>-octadecylcarboxylic acids (orange). The full lines represent the different calculated equilibrium constants.</p> <p><strong>Figure S1.</strong> (A) HfO2/oleate NCs, (B) 1H NMR spectrum in C6D6, and (C) DOSY decay curve of the alkene region.</p> <p><strong>Figure S2.</strong> (A) CdSe/oleate NCs, (B) 1H NMR spectrum in C6D6, and (C) UV-vis absorption spectrum, (D) DOSY decay curve of the alkene region, and (E) DOSY decay curve of the methylene region.</p> <p><strong>Figure S3.</strong> Synthesis of zinc blende ZnS/oleate NCs. (A) General reaction scheme, (B) TEM image, and (C) 1H NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the alkene region, and (F) UV-vis absorption spectrum.</p> <p><strong>Figure S4.</strong> 1H NMR of (top, black line) the supernatant of the HfO2/oleate NCs after titration until 1.0 equivalent 6-(hexyloxy)hexyl phosphinic acid is added, and (bottom grey line) reference spectrum of oleic acid in CDCl3.</p> <p><strong>Figure S5.</strong> 1H NMR of (top, black line) the supernatant of the CdSe/oleate NCs after titration until 1.0 equivalent 6-(hexyloxy)hexyl phosphinic acid is added, and (bottom grey line) reference spectrum of oleic acid in CDCl3</p> <p><strong>Figure S6.</strong> Titration of ZnS/oleate NCs with 6-(hexyloxy)hexylphosphinic acid. (A) General reaction scheme, (B) 1H NMR spectra of the titration, (C) 31P NMR after 1.0 and 1.6 equivalent phosphinic acid is added, and (D) quantification of the different compounds as a function of added equivalents.</p> <p><strong>Figure S7.</strong> 1H and 31P NMR spectra of (top gray line) n-tetradecylphosphinic acid dehydrated<br>with dicyclohexylcarbodiimide (DCC) to form n-tetradecylphosphinic anhydride, and (bottom<br>black line) n-tetradecylphosphinic acid reference, both in C6D6. </p> <p><strong>Figure S8.</strong> (A) 1H and (B) 31P NMR of (top black line) the supernatant of the ZnS/oleate NCs after titration until 1.6 equivalent 6-(hexyloxy)hexylphosphinic acid is added, and reference spectra of (red line) oleic acid and (blue line) 6-(hexyloxy)hexylphosphinic acid in CDCl3.</p> <p><strong>Figure S9.</strong> Purified HfO2/[6-(hexyloxy)hexyl]phosphinate NCs. (A) HfO2/phosphinate NCs. (B) 1H NMR spectrum in C6D6 with zoom inset of the broadened P-H resonance, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the ether region.</p> <p><strong>Figure S10.</strong> Purified CdSe/[6-(hexyloxy)hexyl]phosphinate NCs. (A) CdSe/phosphinate NCs. (B) 1H NMR spectrum in C6D6, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the ether region, (F) DOSY decay curve of the methylene region, and (G) UV-vis absorption spectrum.</p> <p><strong>Figure S11.</strong> Purified ZnS/[6-(hexyloxy)hexyl]phosphinate NCs. (A) ZnS/phosphinate NCs. (B) 1H NMR spectrum in C6D6 with zoom inset of the broadened P-H resonance, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the ether region, and (F) UV-vis absorption spectrum.</p> <p><strong>Figure S12.</strong> (A) 1H and (B) 31P NMR of the supernatant (black line) of the HfO2/phosphinate NCs after titration until 2.0 equivalent oleylphosphonic acid is added, and reference spectra of (green line) oleylphosphonic acid and (blue line) 6-(hexyloxy)hexylphosphinic acid in C6D6</p> <p><strong>Figure S13.</strong> (A) 1H and (B) 31P NMR of the supernatant (black line) of the CdSe/phosphinate NCs after titration until 2.0 equivalent oleylphosphonic acid is added, and reference spectra of (green line) oleylphosphonic acid and (blue line) 6-(hexyloxy)hexylphosphinic acid in C6D6. </p> <p><strong>Figure S14.</strong> Titration of ZnS/[(6-hexyloxy)hexyl]phosphinate with oleylphosphonic acid. (A) General reaction scheme, (B) 1H NMR spectra of the titration, and (C) quantification of the different compounds as a function of added equivalents.</p> <p><strong>Figure S15.</strong> (A) 1H and (B) 31P NMR of the supernatant (black line) of the ZnS/phosphinate NCs after titration until 2.0 equivalent oleylphosphonic acid is added, and reference spectra of (green line) oleylphosphonic acid and (blue line) 6-(hexyloxy)hexyl phosphinic acid in C6D6.</p> <p><strong>Figure S16.</strong> Titration of HfO2 NCs stabilized with a mixed ligand shell consistent of 6-(hexyloxy)hexylphosphinate and oleylphosphonate. (A) General reaction scheme, (B) 1H NMR spectrum of the purified NCs prior to titration (at 0.0 added equivalents of 6-(hexyloxy)hexylphosphinic acid), (C) 1H NMR spectra of the titration, and (D) quantification of the different compounds as a function of added equivalents 6-(hexyloxy)hexylphosphinic acid.</p> <p><strong>Figure S17.</strong> Titration of CdSe NCs stabilized with a mixed ligand shell consistent of 6-(hexyloxy)hexylphosphinate and oleylphosphonate. (A) General reaction scheme, (B) 1H NMR spectrum of the purified NCs prior to titration (at 0.0 added equivalents of 6-(hexyloxy)hexylphosphinic acid), (C) 1H NMR spectra of the titration, and (D) quantification of the different compounds as a function of added equivalents 6-(hexyloxy)hexylphosphinic acid.</p> <p><strong>Figure S18.</strong> Titration of ZnS NCs stabilized with a mixed ligand shell consistent of 6-(hexyloxy)hexylphosphinate acid and oleylphosphonate. (A) General reaction scheme, (B) 1H NMR spectrum of the purified NCs prior to titration (at 0.0 added equivalents of 6-(hexyloxy)hexylphosphinic acid) (C) 1H NMR spectra of the titration, and (D) quantification of the different compounds as a function of added equivalents 6-(hexyloxy)hexylphosphinic acid.</p> <p><strong>Figure S19.</strong> The mole fraction of bound oleylphosphonate in the ligand shell, 𝜒𝑝ℎ𝑜𝑠𝑝ℎ𝐨𝑛 (𝑏𝑜𝑢𝑛𝑑), as a function of the overall mole fraction of oleylphosphonic acid (= unbound phosphonic acid and bound phosphonate), 𝜒𝑝ℎ𝑜𝑠𝑝ℎ𝐨𝑛 (𝑡𝑜𝑡𝑎𝑙) , during the titrations of (A) HfO2 (blue), (B) CdSe (red), and (C) ZnS (green) NCs. The full lines represent different calculated equilibrium constants.</p> <p><strong>Figure S20.</strong> Changes in chemical shift during the ligand exchange of phosphinate for phosphonate for HfO2, CdSe, and ZnS NCs</p> <p><strong>Figure S21.</strong> Purified ZnS/n-alkylphosphinate NCs with a 50/50 mixture of n-hexyl/noctadecylphosphinate. (A) ZnS/phosphinate NCs. (B) 1H NMR spectrum in C6D6 with zoom inset of the broadened P-H resonance, (C) 31P NMR spectrum in C6D6, (D) DOSY NMR spectrum in C6D6, (E) DOSY decay curve of the alkane region, and (F) UV-vis absorption spectrum.</p> <p><strong>Figure S22.</strong> Titration of ZnS/n-alkylphosphinate with a 50/50 mixture of n-hexyl/noctadecylphosphinate with oleylphosphonic acid. (A) General reaction scheme, (B) 1H NMR spectra of the titration (zoom of the alkene resonance and the adjacent methylene groups), and (C) quantification of the different compounds as a function of added equivalents.</p> <p><strong>Figure S23.</strong> Comparative ligand exchange experiments where oleate capped ZnS NCs are titrated with a 50/50 mixture of n-hexyl/n-octadecylcarboxylic, -phosphinic, or -phosphonic acids. (A) General reaction scheme for the 3 separate titrations with carboxylic, phosphinic, or phosphonic acids. (B) Bound fraction of oleate on ZnS NCs as a function of the added equivalents of the titrating acid mixture, including the theoretical expected quantitative and random exchange development.</p> <p><strong>Figure S24.</strong> Titration of ZnS/oleate NCs with a 50/50 mixture of n-hexyl, and n-octadecyl carboxylic, phosphinic, and phosphonic acids in C6D6. (A) General reaction scheme. (B) 1H NMR spectra of the titration with carboxylic acids. (C) 1H NMR spectra of the titration with phosphinic acids. (D) 1H NMR spectra of the titration with phosphonic acids (added from a concentrated solution in THF-d8).</p> <p> </p> <p> </p> <p> </p>
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>
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>
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>
Dataset : Micromolar concentration affinity study on a benchtop NMR spectrometer with secondary 13C labeled hyperpolarized ligands
<p><strong>Data of the dDNP decays for 13C drug screening</strong></p> <p><strong>Drug discovery Data recap</strong></p> <p> </p> <p><strong>Samples</strong></p> <p>Sample 2 : 44mM of Ac-L30 (N-Acetyl [1-<sup>13</sup>C]-6 amino-2-naphthoic acid) in 60/30/10 DMSO/D2O/H2O with 25 mM Tempol</p> <p>Sample 4 (in fact sample 3 in Topspin and according to OC figures) : 44mM of Ac-L08 (N-Acetyl [1-<sup>13</sup>C]-glycine) in 60/30/10 DMSO/D2O/H2O with 25 mM Tempol</p> <p> </p> <p><strong>Dissolution Ac-L30 </strong></p> <p><em>Sample : 600 µM Sample 2 without and with 2 µM HSA</em></p> <p><em>Topspin Folder : 20210414-DrugScreening</em></p> <ul> <li>Dissolution fragment : 1</li> <ul> <li>D1 5 sec</li> <li>Int between 167.2 - 168.3 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 11.88 (10.84, 12.91)</li> <li> a = 1.017 (0.967, 1.066)</li> <li> d = 0.0005482 (-0.006744, 0.007841)</li> </ul> <li>TE : 2</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <li>Dissolution fragment + HSA : 3</li> <ul> <li>D1 5 sec</li> <li>Integration between 167.9 - 168.1 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 6.339 (5.57, 7.109)</li> <li> a = 1.027 (0.9665, 1.088)</li> <li> d = -0.007015 (-0.01521, 0.001185)</li> </ul> <li>TE : 4</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <ul> <li>Code used to plot : Ac-L30.m</li> </ul> </ul> <p> </p> <p><strong>Dissolution Ac-L30 </strong></p> <p><em>Sample : 600 µM Sample 4 without and with 2 µM HSA</em></p> <p><em>Topspin Folder : 20210414-DrugScreening</em></p> <ul> <li>Dissolution fragment : 5</li> <ul> <li>D1 2,5 sec</li> <li>Int between 168.6 - 169 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 29.25 (29.05, 29.46)</li> <li> a = 1.019 (1.015, 1.023)</li> <li> d = -0.001389 (-0.002239, -0.0005388)</li> </ul> <li>TE : 6</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <li>Dissolution fragment + HSA : 11</li> <ul> <li>D1 2,5 sec</li> <li>Integration between 168.6 - 169 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 29.73 (29.57, 29.88)</li> <li> a = 0.9938 (0.9905, 0.9971)</li> <li> d = -0.0003466 (-0.0007762, 8.314e-05)</li> </ul> <li>TE : 12</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <ul> <li>Code used to plot : Ac-L08.m</li> </ul> </ul>
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>
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>
A Folding–Docking–Affinity framework for protein–ligand binding affinity prediction
Open the record for dataset details and reuse information.
Supplementary Information: Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors
<p>Supplementary Information: Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors</p>
Transcriptome profiles of colonic stem cells of WT and AHR deficient mice treated with either DMSO or high affinity AHR ligand, FICZ for 4 hours
GEO Series GSE179481. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Chemo- versus immuno-precipitation of G-quadruplex-DNA (G4- DNA): a direct comparison of the efficiency of the antibody BG4 versus the small-molecule ligands TASQs for G4 affinity capture
GEO Series GSE200171. Oryza sativa. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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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
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