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datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “small molecule binding”
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</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>
An Integrated Machine Learning Approach Delineates an Entropic Expansion Mechanism for the Binding of a Small Molecule to α-Synuclein
<p>The <a href="https://zenodo.org/uploads/14177307" target="_blank" rel="noopener noreferrer">TRAJECTORY.zip</a> contains two trajectories. One for the apo simulation (traj_mw_c4.xtc) and the other for the water simulation in .gro format (traj.gro). The run1.tpr is the binary file for running the apo simulation in gromacs. For water simulation, the raw DOSPT files are also included to calculate the entropy.</p> <p>The <a href="https://zenodo.org/uploads/14177307" target="_blank" rel="noopener noreferrer">figure_raw_data.zip</a> file contains the raw data that was used for plotting the figures in the main text and in the supplementary material.</p> <p>For any further inquiries or requests for additional data, please contact <a rel="noopener">jmondal@tifrh.res.in</a>.</p>
Small Molecule Agonist Binding at human NOP Receptor Data
<p>This entry contains:</p> <ul> <li>MD input files and scripts to run the simulations of N/OFQ(1-13)-NH2 in complex with the human NOP receptor (PDB ID: 8F7X)</li> <li>Initial docking poses and MD input files and scripts to run the simulations of predicted small molecule agonist in complex with the human NOP receptor <br> <table> <tbody> <tr> <td>Ligand</td> <td>Binding Mode</td> <td>Receptor Structure</td> </tr> <tr> <td><strong>(<em>R</em>)-Ro 65-6570</strong></td> <td>RBM01 </td> <td>8F7X</td> </tr> <tr> <td><strong>(<em>R</em>)-Ro 65-6570</strong></td> <td>RBM03 </td> <td>8F7X_mod</td> </tr> <tr> <td><strong>MCOPPB </strong></td> <td>MBM04</td> <td>8F7X_mod</td> </tr> <tr> <td><strong>MCOPPB </strong></td> <td>MBM05</td> <td>8F7X_mod</td> </tr> </tbody> </table> </li> <li>MD topology (.psf) and trajectory (.dcd) files of all five simulated systems (three replicas each)</li> </ul>
Enhanced Mapping of Small Molecule Binding Sites in Cells
<p>Selected docking poses for Wozniak et al manuscript. File name include the structure id (PDBID or Alphafold model) and the probe id, separated by an underscore (i.e., 6tjk_4.pdb, or AF-P10620-F1-model_6.pdb).</p>
In silico study on a binding mechanism of ssDNA aptamers targeting glycosidic bond-containing small molecules
<p>Files with the 5 ssDNA and their complexes with glycosidic bond-containing small molecules after docking, used in the MD simulations, along with MD simulation trajectories, are included as compressed files (.zip). </p><p>Glycosidic bond-containing small molecules docking performed using AutoDock Vina, and MD simulations based on amber software and DNA.bsc1 force fields.</p>
Augmented base pairing networks encode RNA-small molecule binding preferences
<p>Dataset used to train and validate the RNAmigos model from "Augmented base pairing networks encode RNA-small molecule binding preferences".</p> <p> </p> <p>This will give you a cleaned up version of the data used to train the RNAmigos 1.0 models.</p> <p> </p> <p>If you run `python make_nice.py` you will generate a CSV file `rnamigos1_dataset.csv` which contains all the info you need.</p> <p>The script will also use DecoyFinder to generate the decoys for each pocket.</p> <p> </p> <p> </p> <p>### Pockets</p> <p> </p> <p>The CSV has one row for each binding pocket.</p> <p> </p> <p>The columns are:</p> <p> </p> <p>* pdbid: the PDBID this pocket belongs to</p> <p>* model_num: the model number inside the PDB we took</p> <p>* chain: the chain the pocket belongs to</p> <p>* ligand_id: the 3-letter code of the ligand (e.g. ATP) which you can look up on RCSB.org</p> <p>* ligand_resnum: the residue number of the ligand in the PDB</p> <p>* nodelist: a list of nodes separated by ';' in the pocket as a string in the format `<node1 pdbid>.<node1 chain>.<node1 position>-<nucleotide type>;<node2 pdbid>...`</p> <p>* edgelist: a list of edges separated by ';' in the pocket as a string in the format nodes are in the same format as above, and connected by a '-' char, with an additional label field. e.g. of a two edge list `1aju.A.1-1aju.A.5-CWW;1aju.A.1-1aju.A.2-B53`</p> <p>* fp_native_maccs: bit string of the MACCS for the native ligand</p> <p>* split_{k}_train: one col for all the splits we ran (k \in {0-9}) contains True if this pocket was in the train set for this split</p> <p>* split_{k}_test: one col for all the splits we ran (k \in {0-9}) contains True if this pocket was in the test set for this split</p> <p> </p> <p>### Decoys</p> <p> </p> <p>The folder `decoy_db/` has the following structure:</p> <p> </p> <p>```</p> <p>decoy_db</p> <p> <pdbid>_<chain>_{ligand_id}_{ligand_position}</p> <p> decoyfinder</p> <p> actives.txt</p> <p> decoys.txt</p> <p> pdb</p> <p> actives.txt</p> <p> decoys.txt</p> <p> </p> <p> </p> <p>Each `actives.txt` and `decoys.txt` is a file with one SMILES per line. </p> <p> </p> <p>`decoyfinder/` has decoys computed by DecoyFinder and the acvtives are just the native ligands.</p> <p>`pdb/` has decoys taken from other pockets in the PDB and actives are just the native ligands.</p>
Targeting multiple effector pathways in pancreatic ductal adenocarcinoma with a G-quadruplex-binding small molecule
GEO Series GSE105083. Homo sapiens. 104 samples. Type: Expression profiling by high throughput sequencing.
Custom DNA microarrays reveal diverse binding preferences of proteins and small molecules to thousands of G-quadruplexes [design 1]
GEO Series GSE133365. synthetic construct. 32 samples. Type: Other.
Custom DNA microarrays reveal diverse binding preferences of proteins and small molecules to thousands of G-quadruplexes [design 2]
GEO Series GSE133366. synthetic construct. 12 samples. Type: Other.
A genome-wide and dose-dependent inhibition map of androgen receptor binding by small molecules reveals its regulatory program upon antagonism
GEO Series GSE32892. Homo sapiens. 20 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by high throughput sequencing.
CAG repeat-binding small molecule improves repeat-length-dependent dysregulation of gene expression profiles in a mouse model of Dentatorubral–pallidoluysian atrophy
GEO Series GSE183652. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Custom DNA microarrays reveal diverse binding preferences of proteins and small molecules to thousands of G-quadruplexes [design 3]
GEO Series GSE133367. Homo sapiens; Bos taurus; synthetic construct. 114 samples. Type: Other.
Binding to SMN2 pre-mRNA-Protein complex elicits specificity for small molecule splicing modifiers
GEO Series GSE86001. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
The small-molecule flunarizine in spinal muscular atrophy patient fibroblasts impacts on the Gemin components of the SMN complex and RNA-binding proteins relevant to motor neuron diseases
GEO Series GSE145146. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Small molecule inhibition of multiple RNA binding proteins underlies Musashi-2 independent phenotypes
GEO Series GSE213218. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
Custom DNA microarrays reveal diverse binding preferences of proteins and small molecules to thousands of G-quadruplexes
GEO Series GSE133368. Homo sapiens; Bos taurus; synthetic construct. 158 samples. Type: Other.
Whole-Transcriptome Profiling of Canine and Human in Vitro Models Exposed to a G-Quadruplex Binding Small Molecule
GEO Series GSE120272. Canis lupus familiaris; Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
Genomic i-Motifs Display Diverse Binding Preferences Towards Small Molecules, Transcription Factors, and Antibodies
GEO Series GSE227616. Homo sapiens; unidentified. 25 samples. Type: Other.
Disulfide Tethering to Map Small Molecule Binding Sites Transcriptome-Wide
GEO Series GSE261913. Homo sapiens. 134 samples. Type: Other; Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.