cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10155791
title: GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction

description: 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.
The dataset files are saved as .pkl file for the convenience of use.
Paper Abstract:
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.
DOI: 10.5281/zenodo.10155792
title: GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction

description: 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.
The dataset files are saved as .pkl file for the convenience of use.
Paper Abstract:
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.
DOI: 10.5066/p9xk9aq1
title: Inorganic chemistry data for groundwater wells near selected oil fields in the southwestern San Joaquin Valley, central California

description: This digital dataset contains geochemical and other information for 1,209 samples of groundwater from 343 wells located within 3-miles of the Buena Vista, Elk Hills, Midway-Sunset, and North and South Coles Levee oil fields in the southwestern San Joaquin Valley of central California. Data were compiled by the U.S. Geological Survey (USGS) from preexisting datasets and transferred manually into two separate data files described as follows: 1) a summary data file identifying each well, it's location, basic well construction, the number of chemistry samples and period of record, and data sources; and 2) a data file of geochemistry analyses for selected water-quality indicators, major and minor ions, nutrients, and trace elements, plus parameter code and (or) method, reporting level, reporting level type, and supplemental notes. A data dictionary was created to describe the geochemistry data file and is provided with this data release.
DOI: 10.5066/p9kwnefw
title: Geophysical, geological, hydrological, and geochemical data for aquifer salinity mapping in the Elk Hills, Buena Vista, and Coles Levee Oil Fields, Kern County, California

description: The U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (State Water Board) compiled and analyzed data for mapping groundwater salinity, characterizing aquifer systems, and documenting the effects of oil field water injection in the Elk Hills Elk Hills, Buena Vista, and Coles Levee Oil Fields, San Joaquin Valley, California. Data provided in this data release include borehole geophysical data, geochemical analyses of water samples, hydrologic data, geological formation depths, and information on observed well log resistivity anomalies - interpreted to be from injection for disposal of produced water from oil wells or from natural causes. The data reported here were used in an accompanying interpretive manuscript describing aquifer salinity and the effects of produced water disposal in the study area. The geochemical data were compiled for water samples from 14 water source wells to supply groundwater for oil and gas operations and one groundwater monitoring well. The constituents compiled from geochemical data sources included water-quality indicators and detected concentrations of major ions, minor ions, and trace elements. The data represent several decades and most originated from oil and gas producers which are archived in scanned pages in raster format on the California Geologic Energy Management Division (CalGEM) website. The data were compiled into computer readable numerical data sets for the related study. Other data, such as interpolated surfaces of geologic layers, were created for the study based on information in drilling records and are provided here. All the data and the associated manuscript are part of the State Water Board Oil and Gas Regional Monitoring Program and the associated USGS California Oil, Gas, and Groundwater (COGG) Program.
DOI: 10.5066/p9gd7w9l
title: Historical Produced Water Chemistry Data Compiled for the North Coles Levee Oilfield, Kern County, California

description: This dataset contains geochemical and other information for 40 historical samples of produced water from the North Coles Levee Oil Field. Three of these samples are from commingled tanks containing produced water from multiple wells as noted in the Remarks column. Water that is produced as a byproduct of oil production is called produced water. The numerical water chemistry data were compiled by the U.S. Geological Survey (USGS) from scanned laboratory analysis reports available from the California Geologic Energy Management Division (CalGEM). Sample site characteristics, such as well construction details, were attributed using a combination of information provided with the scanned laboratory analysis reports and well history files from CalGEM Well Finder. The data were transferred manually into a numerical dataset and organized by specific produced water chemical characteristics. Each sample is identified by its API (American Petroleum Institute) number, latitude and longitude, well name, formation name, perforation interval, and date tested. The geochemical analyses include major ions, some minor ions, total dissolved solids (TDS), pH, specific gravity, resistivity, electrical conductivity, and charge balance. The collection and analysis methods and circumstances under which the historical samples were collected cannot be fully known. The intent is to use these historical data in grouped analysis including many sample results, rather than detailed interpretation of each individual sample. Caution should be used in basing interpretations of the data on single sample results.
DOI: 10.5066/p9kr7nsl
title: Farmington Lake Sediment Descriptions, Total Sediment Chemistry, and Batch Experiment Chemistry

description: These datasets provide information about the physical properties, total sediment chemistry, and batch experiment chemistry (to evaluate element mobility) of sediment cores from the Farmington Lake reservoir in Farmington, NM. The sediment cores were collected in November 2019.
DOI: 10.6084/m9.figshare.24586998
title: Gender and collective action participation

description: This study explores how gender gaps manifest in collective action participation among the smallholder households from the central region of Kenya.
DOI: 10.6084/m9.figshare.24586998.v1
title: Gender and collective action participation

description: This study explores how gender gaps manifest in collective action participation among the smallholder households from the central region of Kenya.
DOI: 10.5066/p96162bf
title: Data Release for Luminescence: Construction and modification of debris-flow alluvial fans as captured in the geomorphic and sedimentary record: examples from the western Sangre de Cristo Mountains, south-central Colorado

description: The following report summarizes the dating results from the San Luis Valley-Pioneer Fan 2017-2019 dating project. Within this report, we detail the methodology used by the USGS Luminescence Geochronology Laboratory to obtain ages including sample preparation methods, luminescence measurement, equivalent dose determination, and dating-related calculations. We recommend that this report be included as the supplementary material for any publication(s) that use the ages within this report.
DOI: 10.5066/p9avghmb
title: Sediment model inputs and outputs for the Smith Creek watershed near New Market, Virginia for 2012-2016

description: This data release includes data created, collected, and/or otherwise modified in the process of quantifying a sediment budget for the Smith Creek Watershed near New Market, Virginia, USA. Five raster files are included, namely a modeled index of hydrological connectivity, a raster model of modeled floodplain depositional masses and extent, a rasters of modeled gross and delivered upland erosion, and a hydrologically conditioned Digital Elevation Model (DEM). Two vector shapefiles are also included. The first documents the results from the stepwise linear regression models developed to estimate erosion attributed to streambeds, streambanks, and floodplains along stream reaches throughout the watershed. The second documents the same results, now converted to mass rates, for streambeds and streambanks at the sub-watershed level. Floodplain mass results are included in the raster documenting depositional masses and modeled floodplain extent. The Finally, five comma delimited tables are included: three contain field-measured rates of erosion and deposition for streambeds, streambanks, and floodplains for a number of sites throughout the watershed, as well as the independent variables used for model development; a fourth table contains information pertaining to density of source samples collected in the field for the various source types; the final table contains data concerning suspended sediment loads measured at the watershed outlet, in addition to flow type classifications.
DOI: 10.5066/p9uc6wk9
title: Household survey data about participation in wildfire risk mitigation cost-share in western Colorado 2013-2017

description: Household survey data about participation in wildfire risk mitigation cost-share programs and related questions, including stated barriers to conducting wildfire risk mitigation, basic demographics, and willingness to pay toward that cost-share program. Data (n=1,689) were collected in 95 communities exposed to wildfire risk in six counties in western Colorado, 2013-2017, with an overall survey response rate of 41.9%. The household surveys providing data were organized and implemented by two regional wildfire risk mitigation organizations, West Region Wildfire Council and Wildfire Adapted Partnership (formerly Firewise of Southwest Colorado).
DOI: 10.5281/zenodo.10153016
title: Complete codes for "Linking functional traits and diversity-invasibility hypothesis in submerged macrophyte communities under eutrophication", by Li
DOI: 10.5281/zenodo.10153017
title: Complete codes for "Linking functional traits and diversity-invasibility hypothesis in submerged macrophyte communities under eutrophication", by Li
DOI: 10.6084/m9.figshare.24587457
title: Dataset for replication package of mining PRs

description: Dataset for replication package of mining PRs
DOI: 10.6084/m9.figshare.24587475
title: Additional file 1 of Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study

description: Additional file 1.
DOI: 10.6084/m9.figshare.24587475.v1
title: Additional file 1 of Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study

description: Additional file 1.
DOI: 10.6084/m9.figshare.24076908
title: Additional file 1 of Ribosome biogenesis disruption mediated chromatin structure changes revealed by SRAtac, a customizable end to end analysis pipeline for ATAC-seq

description: Additional file 1: Figure-S1. SRAlign consists of a central processing workflow that carries out standard NGS processing steps, and writes key output files. Quality control (QC) workflow generates QC metrics for reads, alignments, library complexity, and sample reproducibility. Intermediate MultiQC reports and a final pipeline report includes output files as summarized. Figure S2. SRAlign effectively aligns RNA-seq data to a reference genome. A. Percentage of reads aligned [20]. B.Proportion of aligned reads aligned to e ach chromosome.C.Genome browser snapshots of RNA-seq alignments for both replicates with RefSeq genes. Figure S3. Validation experiments for RPOA-2 and GRWD-1 depletion. A. Depletion of GRWD-1::degron::GFP after 18 hours of auxin treatment. The numbers on top of the blot represent the ratio of GFP to actin. B. RT-qPCR showing the relative expression levels of the internal transcribed spacer of ribosomal RNA ITS1 and ITS2 in response to RPOA-2 depletion. The relative transcript levels were determined for each sample by normalizing them to mature 26S rRNA. Embryos of were treated with or without auxin for 18 hours. Data were obtained from three biological experiments with three technique replicates for each biological replicate. Statistical significance was determined using an independent t-test. Figure S4. Ribosome biogenesis disruption leads to significant changes in chromatin accessibility. A-B. PCA plots for the first principal components computed by DESeq2, prior to (A) and after (B) batch effect removal using RUVseq and limma::removeBatchEffect(). Green: no RPOA-2 depletion (RPOA-2); dark green: RPOA-2 depleted (RPOA-2; TIR1) light orange: no RRB-1 depletion (RRB-1); dark orange: RRB-1 depleted (RRB-1; TIR1) C-D. Number of differentially accessible regions (DARs) with increased–left– and decreased–right– accessibility for RPOA-2 (C) RRB-1 depletion (D). E-F. Heatmaps of log transformed normalized peak counts for the top 25 DARs in response to RPOA-2 (E) and RRB-1 (F) depletion. Figure S5. Most gene expression changes are not associated with chromatin accessibility changesVolcano plot of -log10 of adjusted p-value vs log2 fold change of ATAC-seq peaks overlapping annotated regulatory elements in response to RPOA-2 depletion. Points colored according to whether a particular peak is a differentially accessible region (DAR, green), or differentially expressed gene (DEG, purple), or both (navy).
DOI: 10.6084/m9.figshare.24076908.v1
title: Additional file 1 of Ribosome biogenesis disruption mediated chromatin structure changes revealed by SRAtac, a customizable end to end analysis pipeline for ATAC-seq

description: Additional file 1: Figure-S1. SRAlign consists of a central processing workflow that carries out standard NGS processing steps, and writes key output files. Quality control (QC) workflow generates QC metrics for reads, alignments, library complexity, and sample reproducibility. Intermediate MultiQC reports and a final pipeline report includes output files as summarized. Figure S2. SRAlign effectively aligns RNA-seq data to a reference genome. A. Percentage of reads aligned [20]. B.Proportion of aligned reads aligned to e ach chromosome.C.Genome browser snapshots of RNA-seq alignments for both replicates with RefSeq genes. Figure S3. Validation experiments for RPOA-2 and GRWD-1 depletion. A. Depletion of GRWD-1::degron::GFP after 18 hours of auxin treatment. The numbers on top of the blot represent the ratio of GFP to actin. B. RT-qPCR showing the relative expression levels of the internal transcribed spacer of ribosomal RNA ITS1 and ITS2 in response to RPOA-2 depletion. The relative transcript levels were determined for each sample by normalizing them to mature 26S rRNA. Embryos of were treated with or without auxin for 18 hours. Data were obtained from three biological experiments with three technique replicates for each biological replicate. Statistical significance was determined using an independent t-test. Figure S4. Ribosome biogenesis disruption leads to significant changes in chromatin accessibility. A-B. PCA plots for the first principal components computed by DESeq2, prior to (A) and after (B) batch effect removal using RUVseq and limma::removeBatchEffect(). Green: no RPOA-2 depletion (RPOA-2); dark green: RPOA-2 depleted (RPOA-2; TIR1) light orange: no RRB-1 depletion (RRB-1); dark orange: RRB-1 depleted (RRB-1; TIR1) C-D. Number of differentially accessible regions (DARs) with increased–left– and decreased–right– accessibility for RPOA-2 (C) RRB-1 depletion (D). E-F. Heatmaps of log transformed normalized peak counts for the top 25 DARs in response to RPOA-2 (E) and RRB-1 (F) depletion. Figure S5. Most gene expression changes are not associated with chromatin accessibility changesVolcano plot of -log10 of adjusted p-value vs log2 fold change of ATAC-seq peaks overlapping annotated regulatory elements in response to RPOA-2 depletion. Points colored according to whether a particular peak is a differentially accessible region (DAR, green), or differentially expressed gene (DEG, purple), or both (navy).
DOI: 10.6084/m9.figshare.23993127.v1
title: Additional file 1 of Jdp2 is a spatiotemporal transcriptional activator of the AhR via the Nrf2 gene battery

description: Additional file 1: Supplementary Figure S1. Luciferase activity driven by the Cyp1b1 promoter in response to TCDD. Supplementary Figure S2. Characteristics of AhR protein expression and effects of Jdp2 on AhR expression. Supplementary Figure S3. Characterization of AhR promoter activity. Supplementary Figure S4. Interaction of the AhR–Jdp2–Nrf2 axis in nuclei and cytoplasm in WT and Jdp2–/–MEFs in response to TCDD or DMSO exposure for the times indicated. Supplementary Figure S5. Characterization of the DRE2 mutation in the AhR promoter region and expression of the Jdp2 target genes. Supplementary Figure S6. EMSA assay of GST–AhR–basic helix-loop-helix (aHLH) binding to DRE2, DRE3 and ARE1 in vitro. Supplementary Figure S7. Relative mRNA expression of the AhR target genes Aldh3a1 (A), Cyp1b1 (B), and Tiparp (C) after exposure to DMSO and TCDD in WT and Jdp2−/− MEFs. Supplementary Figure S8. Uncropped raw data of western blots which were used in this article. Supplementary Table S1. Antibodies used in this study. Supplementary Table S2. Oligonucleotides. Supplementary Table S3. Mutation of ARE1, ARE2, DRE1, DRE2, and DRE3 in AhR promoter. Supplementary Table S4. siRNAs used in this study. Supplementary Table S5. Experimental models: organisms/strains. Supplementary Table S6. Experimental models: cell line. Supplementary Table S7. Critical commercial assay. Supplementary Table S8. Chemicals, peptides, and recombinant proteins. Supplementary Table S9. Recombinant DNA Supplementary Table S10. Software and algorithms.
DOI: 10.6084/m9.figshare.24130107.v1
title: Additional file 1 of Murine leukemia virus (MLV) P50 protein induces cell transformation via transcriptional regulatory function

description: Additional file 1: Table S1. Cellular RNAs up-regulated by the P50 MLV protein.
DOI: 10.6084/m9.figshare.23993127
title: Additional file 1 of Jdp2 is a spatiotemporal transcriptional activator of the AhR via the Nrf2 gene battery

description: Additional file 1: Supplementary Figure S1. Luciferase activity driven by the Cyp1b1 promoter in response to TCDD. Supplementary Figure S2. Characteristics of AhR protein expression and effects of Jdp2 on AhR expression. Supplementary Figure S3. Characterization of AhR promoter activity. Supplementary Figure S4. Interaction of the AhR–Jdp2–Nrf2 axis in nuclei and cytoplasm in WT and Jdp2–/–MEFs in response to TCDD or DMSO exposure for the times indicated. Supplementary Figure S5. Characterization of the DRE2 mutation in the AhR promoter region and expression of the Jdp2 target genes. Supplementary Figure S6. EMSA assay of GST–AhR–basic helix-loop-helix (aHLH) binding to DRE2, DRE3 and ARE1 in vitro. Supplementary Figure S7. Relative mRNA expression of the AhR target genes Aldh3a1 (A), Cyp1b1 (B), and Tiparp (C) after exposure to DMSO and TCDD in WT and Jdp2−/− MEFs. Supplementary Figure S8. Uncropped raw data of western blots which were used in this article. Supplementary Table S1. Antibodies used in this study. Supplementary Table S2. Oligonucleotides. Supplementary Table S3. Mutation of ARE1, ARE2, DRE1, DRE2, and DRE3 in AhR promoter. Supplementary Table S4. siRNAs used in this study. Supplementary Table S5. Experimental models: organisms/strains. Supplementary Table S6. Experimental models: cell line. Supplementary Table S7. Critical commercial assay. Supplementary Table S8. Chemicals, peptides, and recombinant proteins. Supplementary Table S9. Recombinant DNA Supplementary Table S10. Software and algorithms.
DOI: 10.6084/m9.figshare.24130107
title: Additional file 1 of Murine leukemia virus (MLV) P50 protein induces cell transformation via transcriptional regulatory function

description: Additional file 1: Table S1. Cellular RNAs up-regulated by the P50 MLV protein.