cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/6a57782a0b5efe667f57e9e54af1ae36
title: Water Temperatures and Climatology for Wachapreague, VA, 1982-2021

description: Daily average measured near-surface water temperature from 1982-2021 constructed for the site in the Virginia coastal bays where the NOAA Wachapreague station is located (Site CBW (Coastal Bay Wachapreague); 37.61N, 75.69W; water depth 1m) and for a site in the coastal ocean just outside the bays (Site COC (Coastal Ocean CHLV2); 36.91N, 75.71W; water depth 15m). Daily climatological mean temperature and marine heatwave threshold (90th percentile) calculated using 30 years of the record (1987-2016) are also provided.
DOI: 10.6084/m9.figshare.21231754
title: Drug repurposing and sequence analysis in S-glycoprotein variants reveals critical signature patterns and destabilization of receptor-binding domain in omicron variant

description: The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus since its emergence in 2019 has yielded several new viral variants with varied infectivity, disease severity, and antigenicity. Although most mutations are expected to be relatively neutral, mutations at the Spike region of the genome have shown to have a major impact on the viral transmission and infection in humans. Therefore, it is crucial to survey the structures of spike protein across the global virus population to contextualize the rate of therapeutic success against these variants. In this study, high-frequency mutational variants from different geographic regions were pooled in order to study the structural evolution of the spike protein through drug docking and MD simulations. We investigated the mutational burden in the spike subregions and have observed that the different variants harbour unique signature patterns in the spike subregions, with certain domains being highly prone to mutations. Further, the MD simulations and docking study revealed that different variants show differential stability when docked for the same set of drug targets. This work sheds light on the mutational burden and the stability landscape of the spike protein across the variants from different geographical regions. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21231754.v1
title: Drug repurposing and sequence analysis in S-glycoprotein variants reveals critical signature patterns and destabilization of receptor-binding domain in omicron variant

description: The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus since its emergence in 2019 has yielded several new viral variants with varied infectivity, disease severity, and antigenicity. Although most mutations are expected to be relatively neutral, mutations at the Spike region of the genome have shown to have a major impact on the viral transmission and infection in humans. Therefore, it is crucial to survey the structures of spike protein across the global virus population to contextualize the rate of therapeutic success against these variants. In this study, high-frequency mutational variants from different geographic regions were pooled in order to study the structural evolution of the spike protein through drug docking and MD simulations. We investigated the mutational burden in the spike subregions and have observed that the different variants harbour unique signature patterns in the spike subregions, with certain domains being highly prone to mutations. Further, the MD simulations and docking study revealed that different variants show differential stability when docked for the same set of drug targets. This work sheds light on the mutational burden and the stability landscape of the spike protein across the variants from different geographical regions. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21154820
title: Virtual screening of flavonoids from <i>Chamaecrista</i> genus: ADME and pharmacokinetic properties, interactions of flavonoid–DNA complex by molecular docking and molecular dynamics

description: This research aimed to conduct an <i>in silico</i> study of compounds, mainly flavonoids, that are found in several plants, including the species of the <i>Chamaecrista</i> genus. The ADME properties, the drug-likeness score and properties of Lipinski and Veber rules of the molecules were determined using online databases. Based on the predicted properties, four flavonoids, apigenin, fisetin, luteolin and ononin were selected for molecular docking and dynamic simulations to study their interactions with DNA (PDB ID: 1BNA). The molecular docking showed that ononin has a high affinity for B-DNA, exhibiting a ΔG value of −9.3 kcal mol<sup>−1</sup>, compared with the other flavonoids. The molecular dynamic simulations of the flavonoid–DNA complexes showed that the flavonoids interacted with DNA by hydrogen bonding, hydrophobic interaction and <i>π</i>-stacking. The flavonoid ononin showed the best interaction energy value of −291.3490 kJ mol<sup>−1</sup>, compared with the other flavonoids. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21154820.v1
title: Virtual screening of flavonoids from <i>Chamaecrista</i> genus: ADME and pharmacokinetic properties, interactions of flavonoid–DNA complex by molecular docking and molecular dynamics

description: This research aimed to conduct an <i>in silico</i> study of compounds, mainly flavonoids, that are found in several plants, including the species of the <i>Chamaecrista</i> genus. The ADME properties, the drug-likeness score and properties of Lipinski and Veber rules of the molecules were determined using online databases. Based on the predicted properties, four flavonoids, apigenin, fisetin, luteolin and ononin were selected for molecular docking and dynamic simulations to study their interactions with DNA (PDB ID: 1BNA). The molecular docking showed that ononin has a high affinity for B-DNA, exhibiting a ΔG value of −9.3 kcal mol<sup>−1</sup>, compared with the other flavonoids. The molecular dynamic simulations of the flavonoid–DNA complexes showed that the flavonoids interacted with DNA by hydrogen bonding, hydrophobic interaction and <i>π</i>-stacking. The flavonoid ononin showed the best interaction energy value of −291.3490 kJ mol<sup>−1</sup>, compared with the other flavonoids. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21185906
title: Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets

description: Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset–Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (<i>n</i> = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein–ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21217699
title: Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

description: Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, <i>PGR</i>, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the <i>PGR</i> gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the <i>PGR</i> gene’s SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the <i>PGR</i> gene. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21217699.v1
title: Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

description: Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, <i>PGR</i>, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the <i>PGR</i> gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the <i>PGR</i> gene’s SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the <i>PGR</i> gene. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21185906.v1
title: Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets

description: Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset–Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (<i>n</i> = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein–ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21196170
title: Bioinformatic analysis of SIRT7 sequence and structure

description: Sirtuins are highly conserved proteins that perform very important functions in different cellular processes. Notably, SIRT7 is the least studied human sirtuin, but it is known to be involved in a wide variety of processes in both health and disease. In this way, SIRT7 activity-regulating molecules could be beneficial for the treatment of relevant diseases such as cardiovascular and bone diseases, where SIRT7 levels are reduced, or obesity and cancer, where they are increased. In this work, using bioinformatic methods, the sequence and structure of SIRT7 orthologs in a wide variety of organisms were analyzed. Thus, the catalytic domain was found to be quite conserved (83.23% identity) and key residues such as D118, Y119, R120, D170, H187, N189, C198, C225, C228, V273, G298, F239 and V237 were identified. Furthermore, a phylogenetic tree was constructed where SIRT7 orthologs from mammals, birds, reptiles, amphibians, fish, insects, and arachnids were found to cluster in different groups. Finally, predicted three-dimensional structures showed a classic structure of the central catalytic region of most sirtuins, while the flanking N- and C-terminal regions were unique to each phylogenetic group. All this helps to understand a little more how SIRT7 works and gives clues for the future design and development of small molecules that benefit human and animal health. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21196170.v1
title: Bioinformatic analysis of SIRT7 sequence and structure

description: Sirtuins are highly conserved proteins that perform very important functions in different cellular processes. Notably, SIRT7 is the least studied human sirtuin, but it is known to be involved in a wide variety of processes in both health and disease. In this way, SIRT7 activity-regulating molecules could be beneficial for the treatment of relevant diseases such as cardiovascular and bone diseases, where SIRT7 levels are reduced, or obesity and cancer, where they are increased. In this work, using bioinformatic methods, the sequence and structure of SIRT7 orthologs in a wide variety of organisms were analyzed. Thus, the catalytic domain was found to be quite conserved (83.23% identity) and key residues such as D118, Y119, R120, D170, H187, N189, C198, C225, C228, V273, G298, F239 and V237 were identified. Furthermore, a phylogenetic tree was constructed where SIRT7 orthologs from mammals, birds, reptiles, amphibians, fish, insects, and arachnids were found to cluster in different groups. Finally, predicted three-dimensional structures showed a classic structure of the central catalytic region of most sirtuins, while the flanking N- and C-terminal regions were unique to each phylogenetic group. All this helps to understand a little more how SIRT7 works and gives clues for the future design and development of small molecules that benefit human and animal health. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21201943
title: Designing new drug candidates as inhibitors against wild and mutant type neuraminidases: molecular docking, molecular dynamics and binding free energy calculations

description: Influenza virus is the cause of the death of millions of people with about 3–4 pandemics every hundred years in history. It also turns into a seasonal disease, bringing about approximately 5–15% of the population to be infected and 290,000–650,000 people to die every year. These numbers reveal that it is necessary to be on the alert to work towards influenza in order to protect public health. There are FDA-approved antiviral drugs such as oseltamivir and zanamivir recommended by the World Center for Disease Prevention. However, after the recent outbreaks such as bird flu and swine flu, increasing studies have shown that the flu virus has gained resistance to these drugs. So, there is an urgent need to find new drugs effective against this virus. This study aims to investigate new drug candidates targeting neuraminidase (NA) for the treatment of influenza by using computer aided drug design approaches. They involve virtual scanning, de novo design, rational design, docking, MD, MMGB/PBSA. The investigation includes H1N1, H5N1, H2N2 and H3N2 neuraminidase proteins and their mutant variants possessing resistance to FDA-approved drugs. Virtual screening consists of approximately 30 thousand molecules while de novo and rational designs produced over a hundred molecules. These approaches produced three lead molecules with binding energies for both non-mutant (-34.84, −59.99 and −60.66 kcal/mol) and mutant (-40.40, −58.93, −76.19 kcal/mol) H2N2 NA calculated by MM-PBSA compared with those of oseltamivir −25.64 and −18.40 respectively. The results offer new drug candidates against influenza infection. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21201943.v1
title: Designing new drug candidates as inhibitors against wild and mutant type neuraminidases: molecular docking, molecular dynamics and binding free energy calculations

description: Influenza virus is the cause of the death of millions of people with about 3–4 pandemics every hundred years in history. It also turns into a seasonal disease, bringing about approximately 5–15% of the population to be infected and 290,000–650,000 people to die every year. These numbers reveal that it is necessary to be on the alert to work towards influenza in order to protect public health. There are FDA-approved antiviral drugs such as oseltamivir and zanamivir recommended by the World Center for Disease Prevention. However, after the recent outbreaks such as bird flu and swine flu, increasing studies have shown that the flu virus has gained resistance to these drugs. So, there is an urgent need to find new drugs effective against this virus. This study aims to investigate new drug candidates targeting neuraminidase (NA) for the treatment of influenza by using computer aided drug design approaches. They involve virtual scanning, de novo design, rational design, docking, MD, MMGB/PBSA. The investigation includes H1N1, H5N1, H2N2 and H3N2 neuraminidase proteins and their mutant variants possessing resistance to FDA-approved drugs. Virtual screening consists of approximately 30 thousand molecules while de novo and rational designs produced over a hundred molecules. These approaches produced three lead molecules with binding energies for both non-mutant (-34.84, −59.99 and −60.66 kcal/mol) and mutant (-40.40, −58.93, −76.19 kcal/mol) H2N2 NA calculated by MM-PBSA compared with those of oseltamivir −25.64 and −18.40 respectively. The results offer new drug candidates against influenza infection. Communicated by Ramaswamy H. Sarma
DOI: 10.17632/8sjny373pz.1
title: Alphonso Mangoes Image Dataset

description: The dataset folder contains images of Alphonso mangoes. There are total of 71 samples each with images of two orientations. Thus , the total mango  images is 142. Each image is of resolution 2048 X 1536. The folder size for the dataset is 44.7 MB and is provided for convenient downloading. Each sample is separated in a folder labeled with a number that corresponds to the sampleID. 

Dataset  contains the following information:
• sampleID – one for the entire fruit and other two for the 2 orientations  (pertaining to the labeled folders in the ‘Alphonso mangoes image dataset folder)
• Physical parameters - the extracted physical features of the whole fruits such as fruit diameter (mm), width across shoulder (mm) and actual weight(gms) stored in .xls file.
DOI: 10.6084/m9.figshare.24066369
title: LT IgA.csv

description: LT IgA serum responses
DOI: 10.6084/m9.figshare.24066369.v1
title: LT IgA.csv

description: LT IgA serum responses
DOI: 10.6084/m9.figshare.24066366
title: EtpA IgG.csv

description: serum IgG responses to EtpA
DOI: 10.6084/m9.figshare.24066366.v1
title: EtpA IgG.csv

description: serum IgG responses to EtpA
DOI: 10.6084/m9.figshare.24066363
title: EtpA IgA.csv

description: serum IgA responses to EtpA
DOI: 10.6084/m9.figshare.23669304
title: Data for "Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields"

description: Soil organic carbon measurements at eight commercial U.S. Midwest fields. Data is split into two CSV files: measurements.csv: site: name of site location_id: identifier for location within site, not necessarily unique across sites sample_depth_min: top depth of sample segment (cm) sample_depth_max: bottom depth of sample segment (cm) SOCc: soil organic carbon concentration (%) BD: bulk density (g/cm<sup>3</sup>) locations.csv: site: name of site location_id: identifier for location within site, not necessarily unique across sites X: longitude, in decimal degrees Y: latitude, in decimal degrees See Geoderma publication for detailed measurement methodology and additional information.
DOI: 10.6084/m9.figshare.23669304.v1
title: Data for "Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields"

description: Soil organic carbon measurements at eight commercial U.S. Midwest fields. Data is split into two CSV files: measurements.csv: site: name of site location_id: identifier for location within site, not necessarily unique across sites sample_depth_min: top depth of sample segment (cm) sample_depth_max: bottom depth of sample segment (cm) SOCc: soil organic carbon concentration (%) BD: bulk density (g/cm<sup>3</sup>) locations.csv: site: name of site location_id: identifier for location within site, not necessarily unique across sites X: longitude, in decimal degrees Y: latitude, in decimal degrees See Geoderma publication for detailed measurement methodology and additional information.