cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10852507
title: Kinodata-3D: an in silico kinase-ligand complex dataset for kinase-focused machine learning.

description: Project Description

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity. We provide kinodata-3D, a dataset of ~138 000 docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see github.com/volkamerlab/kinodata-3D-affinity-prediction).

Dataset

1. Data

This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 33. In particular, we use kinase activity data as curated through the OpenKinome kinodata project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a kinase-docking benchmark data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see kinodata-3D repository).

The final data set contains in total 138 286 deduplicated kinase-ligand pairs, covering ~98 000 distinct compounds and ~271 distinct kinase structures.

2. File structure

The archive kinodata_3d.zip uses the following file structure



data/raw |  kinodata_docked_with_rmsd.sdf.gz |  pocket_sequences.csv |  mol2/pocket     | 1_pocket.mol2     | ...


The file kinodata_docked_with_rmsd.sdf.gz contains the docked ligand poses and the information on the protein-ligand pair inherited from kinodata. The protein pockets located in mol2/pocket are stored according to the MOL2 file format.

The pocket structures were sourced from KLIFS (klifs.net) and complete the poses in the aforementioned SDF file. The files are named {klifs_structure_id}_pocket.mol2. The structure ID is given in the SDF file along with the ligand poses.

The file pocket_sequences.csv contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.

3. Related code

The code used to create the poses can be found in the kinodata-3D repository. The docking pipeline makes heavy use of the kinoml framework, which in turn uses OpenEye's Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by Schaller et al. (2023). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. bioRxiv.
DOI: 10.48420/24851436
title: Summary statistics of UK Biobank blood pressure genome-wide association studies (GWAS) using 337,422 unrelated white European individuals

description: Three blood pressure traits were analysed: systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP; the difference between SBP and DBP). Mean SBP and DBP values from automated values were calculated. After calculating blood pressure values, SBP and DBP were adjusted for medication use by adding 15 and 10 mm Hg to their values, respectively, for individuals reported to be taking blood pressure–lowering medication.For the UK Biobank genome-wide association studies (GWAS), we performed linear mixed model (LMM) association testing under an additive genetic model of the three continuous, medication-adjusted blood pressure traits (SBP, DBP, PP) for all measured and imputed genetic variants (Data Field-22828) with minor allele frequency (MAF) &gt;=1% and imputation score&gt;=0.3 in dosage format using the BOLT-LMM (v2.4.1) software. Covariates were age, age<sup>2</sup>, sex, BMI, genotyping array and 10PCs. Genomic inflation was not applied to the GWAS summary statistics.Sample QC was described below:We included up to 337,422 individuals from UK Biobank for the purpose of this project. We followed UK Biobank sample-based quality control criteria (Nature 2018;562:203-209); excluded were samples/individuals based on the following criteria: (i) outliers in heterozygosity and missingness, (ii) self-reported gender not consistent with genetic data inferred gender (ii) sample call rate (computed using probesets internal to Affymetrix) &lt;97% or the resolution of the distributions of intensity 'contrast' values &lt;0·82, (iii) carriers of sex chromosomal abnormalities (configurations other than XX or XY), (iv) subjects who had cryptic relatedness with other individuals, or (v) individuals of non-white British genetic ancestry.<br>If you use this data, please cite the following paper: Xu, X., Khunsriraksakul, C., Eales, J.M. et al. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 15, 2359 (2024). https://doi.org/10.1038/s41467-024-46132-y
DOI: 10.48420/24851436.v2
title: Summary statistics of UK Biobank blood pressure genome-wide association studies (GWAS) using 337,422 unrelated white European individuals

description: Three blood pressure traits were analysed: systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP; the difference between SBP and DBP). Mean SBP and DBP values from automated values were calculated. After calculating blood pressure values, SBP and DBP were adjusted for medication use by adding 15 and 10 mm Hg to their values, respectively, for individuals reported to be taking blood pressure–lowering medication.For the UK Biobank genome-wide association studies (GWAS), we performed linear mixed model (LMM) association testing under an additive genetic model of the three continuous, medication-adjusted blood pressure traits (SBP, DBP, PP) for all measured and imputed genetic variants (Data Field-22828) with minor allele frequency (MAF) &gt;=1% and imputation score&gt;=0.3 in dosage format using the BOLT-LMM (v2.4.1) software. Covariates were age, age<sup>2</sup>, sex, BMI, genotyping array and 10PCs. Genomic inflation was not applied to the GWAS summary statistics.Sample QC was described below:We included up to 337,422 individuals from UK Biobank for the purpose of this project. We followed UK Biobank sample-based quality control criteria (Nature 2018;562:203-209); excluded were samples/individuals based on the following criteria: (i) outliers in heterozygosity and missingness, (ii) self-reported gender not consistent with genetic data inferred gender (ii) sample call rate (computed using probesets internal to Affymetrix) &lt;97% or the resolution of the distributions of intensity 'contrast' values &lt;0·82, (iii) carriers of sex chromosomal abnormalities (configurations other than XX or XY), (iv) subjects who had cryptic relatedness with other individuals, or (v) individuals of non-white British genetic ancestry.<br>If you use this data, please cite the following paper: Xu, X., Khunsriraksakul, C., Eales, J.M. et al. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 15, 2359 (2024). https://doi.org/10.1038/s41467-024-46132-y
DOI: 10.48420/24851436.v1
title: UK Biobank blood pressure GWAS summary statistics using 337,422 unrelated white European individuals

description: Three blood pressure traits were analysed: systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP; the difference between SBP and DBP). Mean SBP and DBP values from automated values were calculated. After calculating blood pressure values, SBP and DBP were adjusted for medication use by adding 15 and 10 mm Hg to their values, respectively, for individuals reported to be taking blood pressure–lowering medication.For the UK Biobank GWAS, we performed linear mixed model (LMM) association testing under an additive genetic model of the three continuous, medication-adjusted blood pressure traits (SBP, DBP, PP) for all measured and imputed genetic variants (Data Field-22828) with MAF&gt;=1% and imputation score&gt;=0.3 in dosage format using the BOLT-LMM (v2.4.1) software. Covariates were age, age<sup>2</sup>, sex, BMI, genotyping array and 10PCs. Genomic inflation was not applied to the GWAS summary statistics.Sample QC was described below: We included up to 337,422 individuals from UK Biobank for the purpose of this project. We followed UK Biobank sample-based quality control criteria (Nature 2018;562:203-209); excluded were samples/individuals based on the following criteria: (i) outliers in heterozygosity and missingness, (ii) self-reported gender not consistent with genetic data inferred gender (ii) sample call rate (computed using probesets internal to Affymetrix) &lt;97% or the resolution of the distributions of intensity 'contrast' values &lt;0·82, (iii) carriers of sex chromosomal abnormalities (configurations other than XX or XY), (iv) subjects who had cryptic relatedness with other individuals, or (v) individuals of non-white British genetic ancestry.
DOI: 10.24385/lincoln.25245448
title: EMS & pandemics Repository Data 19.02.2024.xlsx

description: The aim of this scoping review was to identify and present published quantitative and qualitative evidence of EMS pandemic interventions, and how this translates into practice.
DOI: 10.24385/lincoln.25245448.v1
title: EMS & pandemics Repository Data 19.02.2024.xlsx

description: The aim of this scoping review was to identify and present published quantitative and qualitative evidence of EMS pandemic interventions, and how this translates into practice.
DOI: 10.15480/336.5041
title: 3D Point Cloud of the Main Campus of Hamburg University of Technology (5 cm subsampled)

description: Punktwolkendatei (e57) des Hauptcampus der Technischen Universität Hamburg mit Intensitäts- und Farbinformationen für 125 separate Scans.
description: Point cloud file (e57) of the main campus of Hamburg University of Technology containing intensity and color information for 125 separate scans.
DOI: 10.5281/zenodo.10852522
title: Toy dataset with pairs of molecules differing in total charge
title: From: On the inclusion of charge and spin states in Cartesian tensor neural network potentials

description: On the inclusion of charge and spin states in Cartesian tensor neural network potentials. (2024)Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez, Raimondas Galvelis and Gianni De Fabritiis.

Two toy datasets, Dataset A and Dataset B, each one comprising five members of five pairs ofunique molecular systems, each of the elements in the pair differing in total charge. These pairs are indistinguishable to a neural network that does not account for total charge,as they share identical atomic numbers and geometric configurations.The datasets include total charges, Gasteiger partial charges computed with RDKit,and calculated energies and atomic forces for 2000 conformers per molecule using GFN2-xTB.Therefore, each dataset contains a total of 10k data points.Conformers were generated by minimizing each molecule, displacing atomic positions with Gaussiannoise with a standard deviation of 0.2Å, and filtering them such that maximum atomic forces are <100 eV/Å.
Data Format and Availability:
Both datasets are available in HDF5 (Hierarchical Data Format version 5) format,compatible with Ace dataset (layout version 2.0) in TorchMD-Net (https://github.com/torchmd/torchmd-net/blob/main/torchmdnet/datasets/ace.py).

Data Structure:

Each molecule is assigned a unique integer identifier, consistent across both sub-datasets,facilitating pairing between conformations.
The HDF5 file is structured as follows:
Each top-level group corresponds to a unique molecule.
Within each group, considering N is the number of atoms in the molecule and M is the number ofconformations, the following datasets are provided:
- atomic_numbers: Atomic numbers of atoms in the molecule, with a shape of (N,);
- formal_charges: Formal charges of atoms in the molecule, with a shape of (N,);
- formation_energies: Formation energy of each conformation, with a shape of (M,),  expressed in units of eV;- forces: Forces on atoms in each conformation, with a shape of (M, N, 3), expressed in eV/Å.
- partial_charges: Gasteiger partial charges of atoms in each conformation,  with a shape of (M, N), expressed in e;
- positions: Cartesian coordinates of atoms in each conformation, with a shape of (M, N, 3),  expressed in Å.
DOI: 10.5281/zenodo.10852523
title: Toy dataset with pairs of molecules differing in total charge
title: From: On the inclusion of charge and spin states in Cartesian tensor neural network potentials

description: On the inclusion of charge and spin states in Cartesian tensor neural network potentials. (2024)Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez, Raimondas Galvelis and Gianni De Fabritiis.

Two toy datasets, Dataset A and Dataset B, each one comprising five members of five pairs ofunique molecular systems, each of the elements in the pair differing in total charge. These pairs are indistinguishable to a neural network that does not account for total charge,as they share identical atomic numbers and geometric configurations.The datasets include total charges, Gasteiger partial charges computed with RDKit,and calculated energies and atomic forces for 2000 conformers per molecule using GFN2-xTB.Therefore, each dataset contains a total of 10k data points.Conformers were generated by minimizing each molecule, displacing atomic positions with Gaussiannoise with a standard deviation of 0.2Å, and filtering them such that maximum atomic forces are <100 eV/Å.
Data Format and Availability:
Both datasets are available in HDF5 (Hierarchical Data Format version 5) format,compatible with Ace dataset (layout version 2.0) in TorchMD-Net (https://github.com/torchmd/torchmd-net/blob/main/torchmdnet/datasets/ace.py).

Data Structure:

Each molecule is assigned a unique integer identifier, consistent across both sub-datasets,facilitating pairing between conformations.
The HDF5 file is structured as follows:
Each top-level group corresponds to a unique molecule.
Within each group, considering N is the number of atoms in the molecule and M is the number ofconformations, the following datasets are provided:
- atomic_numbers: Atomic numbers of atoms in the molecule, with a shape of (N,);
- formal_charges: Formal charges of atoms in the molecule, with a shape of (N,);
- formation_energies: Formation energy of each conformation, with a shape of (M,),  expressed in units of eV;- forces: Forces on atoms in each conformation, with a shape of (M, N, 3), expressed in eV/Å.
- partial_charges: Gasteiger partial charges of atoms in each conformation,  with a shape of (M, N), expressed in e;
- positions: Cartesian coordinates of atoms in each conformation, with a shape of (M, N, 3),  expressed in Å.
DOI: 10.5281/zenodo.10719964
title: High resolution rice yield data of Jiangsu, China

description: This dataset presents rice yields of Jiangsu Province, China during 2001-2020, the spatial resolution is 1 km. 

The dataset was generated using Random Forest Regression algorithm and multi-phase remote sensing data, the accuracy is R2 = 0.65, RMSE = 388.79 kg/ha, and rRMSE = 4.48%.
DOI: 10.5281/zenodo.10807211
title: High resolution rice yield data of Jiangsu, China

description: This dataset presents rice yields of Jiangsu Province, China during 2001-2020, the spatial resolution is 1 km. 

The dataset was generated using Random Forest Regression algorithm and multi-phase remote sensing data, the accuracy is R2 = 0.65, RMSE = 388.79 kg/ha, and rRMSE = 4.48%.
DOI: 10.5285/088c213b-6397-4296-a4b9-138ccb937729
title: BGS BritPits version 2.1

description: The BGS BritPits version 2.1 is derived from the BGS BritPits database, which contains more than 255 000 records of mineral workings covering the Great Britain, Northern Ireland, Isle of Man and Channel Islands. The coverage includes active, inactive, dormant and ceased sites and also a range of mineral operations, including mines, quarries, onshore oil and gas fields, together with wharfs and rail depots handling mineral products and industrial processes which produce a mineral product. This digital archive of mining records held by the BGS, includes information from the Memoirs of the Geological Survey and Museum of Practical Geology (1846 -1872); the Mineral Statistics publications (1855 - 1986); the Inspectorate of Mines (1897, 1948 - 1993); the Directory of Mines and Quarries (1984 onwards) and the BGS Geology 50 (Geological mapping at 1:50,000 scale).The information on currently active sites has been updated continually with the cooperation of both local authorities and the minerals industry.
DOI: 10.18716/dch/a.00000049
title: RTI corpus Byzantine lead seals

description: This corpus contains the digitized facsimile of Byzantine lead seals of the Collection Robert Feind (Cologne) taken in the framework of the DigiByzSeal project and aimed at the creation of an RTI presentation interface.
DOI: 10.6084/m9.figshare.4990823
title: Callaghanetal_JGR_2017_SurfactantImprintInFoamEvolution_FigureData.zip

description: These files contain the data used in the manuscript "On the Imprint of Surfactant-Driven Stabilization of Laboratory Breaking Wave Foam With Comparison to Oceanic Whitecaps" by Adrian H. Callaghan, Grant B. Deane, M. Dale Stokes. At the time of upload, the manuscript is in the review process.<br>
DOI: 10.6084/m9.figshare.19584053
title: Long non-coding RNA-PCGEM1 contributes to prostate cancer progression by sponging microRNA miR-129-5p to enhance chromatin licensing and DNA replication factor 1 expression

description: PCGEM1 facilitates prostate cancer (PCa) progression. This study aimed to elucidate the mechanism of action of PCGEM1 in PCa. The expression of PCGEM1, microRNA miR-129-5p, chromatin licensing, and DNA replication factor 1 (CDT1) was detected by quantitative reverse transcription-PCR (qRT-PCR). A series of function experiments including cell counting kit-8 (CCK-8), caspase-3 activity, and cell cycle assays were performed to evaluate the influence of PCGEM1, miR-129-5p, and CDT1 on the biological processes of PCa cells. CyclinD1, cyclin dependent kinase 4 (CDK4), Bax, and Bcl-2 protein levels were measured by western blotting. Subcellular isolation revealed the distribution of PCa cells. The connections between PCGEM1, miR-129-5p, and CDT1 were evaluated by luciferase, RIP assay, and Pearson correlation analysis. Both PCGEM1 and CDT1 were upregulated in PCa, while miR-129-5p was downregulated and negatively correlated with PCGEM1 and CDT1. Downregulation of PCGEM1 or CDT1 inhibited the viability, promoted apoptosis and cycle arrest of PCa cells <i>in vitro</i>, and controlled tumor growth <i>in vivo</i>. PCGEM1 plays a crucial role in the progression of PCa by sponging miR-129-5p as a ceRNA of CDT1. PCGEM1 is a CDT1-dependent PCa promoter site that absorbs miR-129-5p.
DOI: 10.6084/m9.figshare.19584053.v1
title: Long non-coding RNA-PCGEM1 contributes to prostate cancer progression by sponging microRNA miR-129-5p to enhance chromatin licensing and DNA replication factor 1 expression

description: PCGEM1 facilitates prostate cancer (PCa) progression. This study aimed to elucidate the mechanism of action of PCGEM1 in PCa. The expression of PCGEM1, microRNA miR-129-5p, chromatin licensing, and DNA replication factor 1 (CDT1) was detected by quantitative reverse transcription-PCR (qRT-PCR). A series of function experiments including cell counting kit-8 (CCK-8), caspase-3 activity, and cell cycle assays were performed to evaluate the influence of PCGEM1, miR-129-5p, and CDT1 on the biological processes of PCa cells. CyclinD1, cyclin dependent kinase 4 (CDK4), Bax, and Bcl-2 protein levels were measured by western blotting. Subcellular isolation revealed the distribution of PCa cells. The connections between PCGEM1, miR-129-5p, and CDT1 were evaluated by luciferase, RIP assay, and Pearson correlation analysis. Both PCGEM1 and CDT1 were upregulated in PCa, while miR-129-5p was downregulated and negatively correlated with PCGEM1 and CDT1. Downregulation of PCGEM1 or CDT1 inhibited the viability, promoted apoptosis and cycle arrest of PCa cells <i>in vitro</i>, and controlled tumor growth <i>in vivo</i>. PCGEM1 plays a crucial role in the progression of PCa by sponging miR-129-5p as a ceRNA of CDT1. PCGEM1 is a CDT1-dependent PCa promoter site that absorbs miR-129-5p.
DOI: 10.6084/m9.figshare.19689166
title: Overexpression of microRNA-145 enhanced docetaxel sensitivity in breast cancer cells via inactivation of protein kinase B gamma-mediated phosphoinositide 3-kinase -protein kinase B pathway

description: Chemoresistance is a major challenge for the treatment of breast cancer (BC). Previous studies showed that miR-145 level decreases in chemoresistant BC tissues. Nevertheless, the biological function of miR-145 on docetaxel resistance of BC cells remains unclear, which is what our research attempted to clarify. RT-qPCR analyzed miR-145 level, and cell viability and colony formation assays assessed the impact of miR-145 on docetaxel resistance. Molecular mechanisms of miR-145-mediated docetaxel sensitivity were examined by Luciferase reporter assay and Western Blot assessed the function of AKT3 and PI3K/AKT signaling. Our research found that miR-145 expression presented significant downregulation in docetaxel-resistant BC cells. Meanwhile, miR-145 overexpression facilitated the docetaxel sensitivity of BC cells <i>in vivo</i> and <i>in vitro</i>, while the miR-145 inhibitor decreased the sensitivity of BC cells to docetaxel. We also observed that miR-145 inhibited docetaxel resistance mainly via downregulation of the AKT3 expression and further inhibited PI3K/AKT pathway. To conclude, this research provides a novel strategy for improving chemosensitivity through the newly identified miR-145-AKT3/PI3K-AKT signaling pathway in BC.
DOI: 10.6084/m9.figshare.20131929
title: Identification of the metabolic remodeling profile in the early-stage of myocardial ischemia and the contributory role of mitochondrion

description: Cardiac remodeling is the primary pathological feature of chronic heart failure. Prompt inhibition of remodeling in acute coronary syndrome has been a standard procedure, but the morbidity and mortality are still high. Exploring the characteristics of ischemia in much earlier stages and identifying its biomarkers are essential for introducing novel mechanisms and therapeutic strategies. Metabolic and structural remodeling of mitochondrion is identified to play key roles in ischemic heart disease. The mitochondrial metabolic features in early ischemia have not previously been described. In the present study, we established a mouse heart in early ischemia and explored the mitochondrial metabolic profile using metabolomics analysis. We also discussed the role of mitochondrion in the global cardiac metabolism. Transmission electron microscopy revealed that mitochondrial structural injury was invoked at 8 minutes post-coronary occlusion. In total, 75 metabolites in myocardium and 26 in mitochondria were screened out. About 23% of the differentiated metabolites in mitochondria overlapped with the differentiated metabolites in myocardium; Total 81% of the perturbed metabolic pathway in mitochondria overlapped with the perturbed pathway in myocardium, and these pathways accounted for 50% of the perturbed pathway in myocardium. Purine metabolism was striking and mechanically important. In conclusion, in the early ischemia, myocardium exacerbated metabolic remodeling. Mitochondrion was a contributor to the myocardial metabolic disorder. Purine metabolism may be a potential biomarker for early ischemia diagnosis. Our study introduced a perspective for prompt identification of ischemia.