cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12853383
title: Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 main protease (M<sup>pro</sup>) through docking, molecular mechanic & dynamic, and ADMET profiling

description: Due to an outbreak of COVID-19, the number of research papers devoted to <i>in-silico</i> drug discovery of potential antiviral drugs is increasing every day exponentially. Still, there is no specific drug to prevent or treat this novel coronavirus (SARS-CoV-2) disease. Thus, the screening for a potential remedy presents a global challenge for scientists. Up to date over a hundred crystallographic structures of SARS-CoV-2 M<sup>pro</sup> have been deposited to Protein Data Bank. With many known proteins, the demand for a reliable target has become higher than ever, so as the choice of an efficient computational methods. Therefore, in this study comparative methods have been used for receptor-based virtual screening, targeting 9 selected structures of viral M<sup>pro</sup>. Reliability analyses followed by re-docking of the specific co-crystallized ligand provided the best reproductivity for structures with PDB ID 6LU7, 6Y2G and 6Y2F. The influence of crystallographic water on an outcome of a virtual screening against selected targets was also investigated. Once the most reliable targets were selected, the library of easy purchasable natural compounds were retrieved from the MolPort database (10,305 compounds) and docked against the selected M<sup>pro</sup> proteins. To ensure the efficiency of the selected compounds, binding energies for top-15 hit ligands were calculated using Molecular Mechanics as well as their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were predicted. Based on predicted binding energies and toxicities, top-5 compounds were selected and subjected to Molecular Dynamics simulation and found to be stable in complex to act as possible inhibitors for SARS-CoV-2. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.8219879.v3
title: MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments

description: Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of <i>Andropogon gerardii</i>, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12853383.v1
title: Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 main protease (M<sup>pro</sup>) through docking, molecular mechanic & dynamic, and ADMET profiling

description: Due to an outbreak of COVID-19, the number of research papers devoted to <i>in-silico</i> drug discovery of potential antiviral drugs is increasing every day exponentially. Still, there is no specific drug to prevent or treat this novel coronavirus (SARS-CoV-2) disease. Thus, the screening for a potential remedy presents a global challenge for scientists. Up to date over a hundred crystallographic structures of SARS-CoV-2 M<sup>pro</sup> have been deposited to Protein Data Bank. With many known proteins, the demand for a reliable target has become higher than ever, so as the choice of an efficient computational methods. Therefore, in this study comparative methods have been used for receptor-based virtual screening, targeting 9 selected structures of viral M<sup>pro</sup>. Reliability analyses followed by re-docking of the specific co-crystallized ligand provided the best reproductivity for structures with PDB ID 6LU7, 6Y2G and 6Y2F. The influence of crystallographic water on an outcome of a virtual screening against selected targets was also investigated. Once the most reliable targets were selected, the library of easy purchasable natural compounds were retrieved from the MolPort database (10,305 compounds) and docked against the selected M<sup>pro</sup> proteins. To ensure the efficiency of the selected compounds, binding energies for top-15 hit ligands were calculated using Molecular Mechanics as well as their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were predicted. Based on predicted binding energies and toxicities, top-5 compounds were selected and subjected to Molecular Dynamics simulation and found to be stable in complex to act as possible inhibitors for SARS-CoV-2. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.12853335
title: In silico investigation of phytoconstituents from Indian medicinal herb ‘<i>Tinospora cordifolia</i> (giloy)’ against SARS-CoV-2 (COVID-19) by molecular dynamics approach

description: The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different possibilities (including herbal medicines having known therapeutic significance) have been explored by the scientists. The systematic scientific study (beginning with in silico study) of herbal medicines in particular and any drug in general is now possible as the structural components (proteins) of COVID-19 are already characterized. The main protease of COVID-19 virus is M<sup>pro</sup> or 3CL<sup>pro</sup> which is a key CoV enzyme and an attractive drug target as it plays a pivotal role in mediating viral replication and transcription. In the present study, 3CL<sup>pro</sup> is used to study drug:3CL<sup>pro</sup> interactions and thus to investigate whether all or any of the main chemical constituents of <i>Tinospora cordifolia</i> (e.g. berberine (C<sub>20</sub>H<sub>18</sub>NO<sub>4</sub>), β-sitosterol (C<sub>29</sub>H<sub>50</sub>O), coline (C<sub>5</sub>H<sub>14</sub>NO), tetrahydropalmatine (C<sub>21</sub>H<sub>25</sub>NO<sub>4</sub>) and octacosanol (C<sub>28</sub>H<sub>58</sub>O)) can be used as an anti-viral drug against SARS-CoV-2. The in silico study performed using tools of network pharmacology, molecular docking including molecular dynamics have revealed that among all considered phytochemicals in <i>Tinospora cordifolia</i>, berberine can regulate 3CL<sup>pro</sup> protein's function due to its easy inhibition and thus can control viral replication. The selection of <i>Tinospora cordifolia</i> was motivated by the fact that the main constituents of it are known to be responsible for various antiviral activities and the treatment of jaundice, rheumatism, diabetes, etc. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.12853335.v1
title: In silico investigation of phytoconstituents from Indian medicinal herb ‘<i>Tinospora cordifolia</i> (giloy)’ against SARS-CoV-2 (COVID-19) by molecular dynamics approach

description: The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different possibilities (including herbal medicines having known therapeutic significance) have been explored by the scientists. The systematic scientific study (beginning with in silico study) of herbal medicines in particular and any drug in general is now possible as the structural components (proteins) of COVID-19 are already characterized. The main protease of COVID-19 virus is M<sup>pro</sup> or 3CL<sup>pro</sup> which is a key CoV enzyme and an attractive drug target as it plays a pivotal role in mediating viral replication and transcription. In the present study, 3CL<sup>pro</sup> is used to study drug:3CL<sup>pro</sup> interactions and thus to investigate whether all or any of the main chemical constituents of <i>Tinospora cordifolia</i> (e.g. berberine (C<sub>20</sub>H<sub>18</sub>NO<sub>4</sub>), β-sitosterol (C<sub>29</sub>H<sub>50</sub>O), coline (C<sub>5</sub>H<sub>14</sub>NO), tetrahydropalmatine (C<sub>21</sub>H<sub>25</sub>NO<sub>4</sub>) and octacosanol (C<sub>28</sub>H<sub>58</sub>O)) can be used as an anti-viral drug against SARS-CoV-2. The in silico study performed using tools of network pharmacology, molecular docking including molecular dynamics have revealed that among all considered phytochemicals in <i>Tinospora cordifolia</i>, berberine can regulate 3CL<sup>pro</sup> protein's function due to its easy inhibition and thus can control viral replication. The selection of <i>Tinospora cordifolia</i> was motivated by the fact that the main constituents of it are known to be responsible for various antiviral activities and the treatment of jaundice, rheumatism, diabetes, etc. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.8132450.v3
title: Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data

description: The statistical modeling of space-time extremes in environmental applications is key to understanding complex dependence structures in original event data and to generating realistic scenarios for impact models. In this context of high-dimensional data, we propose a novel hierarchical model for high threshold exceedances defined over continuous space and time by embedding a space-time Gamma process convolution for the rate of an exponential variable, leading to asymptotic independence in space and time. Its physically motivated anisotropic dependence structure is based on geometric objects moving through space-time according to a velocity vector. We demonstrate that inference based on weighted pairwise likelihood is fast and accurate. The usefulness of our model is illustrated by an application to hourly precipitation data from a study region in Southern France, where it clearly improves on an alternative censored Gaussian space-time random field model. While classical limit models based on threshold-stability fail to appropriately capture relatively fast joint tail decay rates between asymptotic dependence and classical independence, strong empirical evidence from our application and other recent case studies motivates the use of more realistic asymptotic independence models such as ours. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12853302
title: Molecular dynamics simulation for screening phytochemicals as α-amylase inhibitors from medicinal plants

description: <i>Diabetes mellitus</i> (DM) is a complicated metabolic disorder with several enzymes, including α-amylase and α-glycosidase. The α-amylase is responsible for postprandial glucose levels; therefore, inhibiting its activity is helpful in diabetes management. Hence, to find natural inhibitors of α-amylase, we have prepared a 257 phytochemical library from selected medicinal plants with antidiabetic activity and conducted a virtual screening and molecular dynamics study. Seventy-nine phytochemicals were screened out of 257 phytochemicals based on binding energy, ranged from −10.1 kcal mol<sup>−1</sup> to −7.6 kcal mol<sup>−1</sup>. The binding energies of screened compounds were lower or equal to the reference molecule (−7.6 kcal mol<sup>−1</sup>). The binding affinity of six screened phytochemicals was re-scored by X-SCORE. These phytochemicals were subjected to ADMET and Drug-likeness analysis. After screening docking and drug-likeness analysis, six phytochemicals viz., Shahidine, Epicatechin, Quercetin, Isocolumbin, Ellagic acid, Luteolin and a reference molecule (Acarbose) were subjected to Molecular dynamics (MD) simulation to analyze the stability of the docked protein-ligand complex. The values of root mean square deviation, RMSF, RG, SASA, H-Bond, the interaction energy of all protein-ligand complexes were calculated after 30 ns of MD simulation. The results of screened complexes revealed good stability as compared to reference Acarbose. Pharmacophore features of the screened phytochemicals and α-amylase inhibitors showed many common pharmacophore features. Based on finding the screened phytochemicals, e.g. Shahidine, Epicatechin, Quercetin, Isocolumbin, Ellagic acid, and Luteolin, may be used as a potential inhibitors against α-amylase. These phytochemicals could be optimized and synthesized to develop potential drugs to manage and treat diabetes, targeting α-amylase. Communicated by Ramaswamy H. Sarma.
DOI: 10.6084/m9.figshare.12853302.v1
title: Molecular dynamics simulation for screening phytochemicals as α-amylase inhibitors from medicinal plants

description: <i>Diabetes mellitus</i> (DM) is a complicated metabolic disorder with several enzymes, including α-amylase and α-glycosidase. The α-amylase is responsible for postprandial glucose levels; therefore, inhibiting its activity is helpful in diabetes management. Hence, to find natural inhibitors of α-amylase, we have prepared a 257 phytochemical library from selected medicinal plants with antidiabetic activity and conducted a virtual screening and molecular dynamics study. Seventy-nine phytochemicals were screened out of 257 phytochemicals based on binding energy, ranged from −10.1 kcal mol<sup>−1</sup> to −7.6 kcal mol<sup>−1</sup>. The binding energies of screened compounds were lower or equal to the reference molecule (−7.6 kcal mol<sup>−1</sup>). The binding affinity of six screened phytochemicals was re-scored by X-SCORE. These phytochemicals were subjected to ADMET and Drug-likeness analysis. After screening docking and drug-likeness analysis, six phytochemicals viz., Shahidine, Epicatechin, Quercetin, Isocolumbin, Ellagic acid, Luteolin and a reference molecule (Acarbose) were subjected to Molecular dynamics (MD) simulation to analyze the stability of the docked protein-ligand complex. The values of root mean square deviation, RMSF, RG, SASA, H-Bond, the interaction energy of all protein-ligand complexes were calculated after 30 ns of MD simulation. The results of screened complexes revealed good stability as compared to reference Acarbose. Pharmacophore features of the screened phytochemicals and α-amylase inhibitors showed many common pharmacophore features. Based on finding the screened phytochemicals, e.g. Shahidine, Epicatechin, Quercetin, Isocolumbin, Ellagic acid, and Luteolin, may be used as a potential inhibitors against α-amylase. These phytochemicals could be optimized and synthesized to develop potential drugs to manage and treat diabetes, targeting α-amylase. Communicated by Ramaswamy H. Sarma.
DOI: 10.6084/m9.figshare.14723988.v2
title: Impacts of the invasive European Starling on two neotropical woodpecker species: agonistic responses and reproductive interactions

description: The European Starling (<i>Sturnus vulgaris</i>) – hereafter ‘starling’ – has been introduced in many countries, and its South American population recently started expanding exponentially. This invasive species has a worldwide negative impact on the breeding performance of woodpeckers, competing for cavity use. Nevertheless, information is still lacking regarding southern temperate neotropical woodpeckers nest defence strategies and starling effects on woodpeckers’ breeding performance. We monitored Campo Flicker (<i>Colaptes campestris</i>) and Green-barred Woodpecker (<i>Colaptes melanochloros</i>) nests in a native southern temperate forest of central-eastern Argentina, to detect interactions with starlings and to assess the effect of starlings presence on woodpeckers’ breeding performance. We assessed whether woodpeckers perform defensive behaviours against the starlings by exposing taxidermied starling models to woodpecker breeding pairs. We detected interactions with starlings at 11% of the nests. These nests had a significantly higher probability of being abandoned during the early stages (until the third incubation day), compared to nests without interactions. Moreover, woodpeckers attacked and made distress calls more frequently in response to the presentation of the starling, compared to predator and non-competing species models. We also documented evidence of joint nesting, as four breeding pairs of woodpeckers shared their nest chamber with starlings. Our results indicate that neotropical woodpeckers are more likely to abandon their cavity when they interact with starlings. As the starling is expanding quickly in Argentina, this information points at the need to develop management programmes to control the impacts of this invasive species on the native fauna, especially on species with conservation concerns.
DOI: 10.6084/m9.figshare.14723988
title: Impacts of the invasive European Starling on two neotropical woodpecker species: agonistic responses and reproductive interactions

description: The European Starling (<i>Sturnus vulgaris</i>) – hereafter ‘starling’ – has been introduced in many countries, and its South American population recently started expanding exponentially. This invasive species has a worldwide negative impact on the breeding performance of woodpeckers, competing for cavity use. Nevertheless, information is still lacking regarding southern temperate neotropical woodpeckers nest defence strategies and starling effects on woodpeckers’ breeding performance. We monitored Campo Flicker (<i>Colaptes campestris</i>) and Green-barred Woodpecker (<i>Colaptes melanochloros</i>) nests in a native southern temperate forest of central-eastern Argentina, to detect interactions with starlings and to assess the effect of starlings presence on woodpeckers’ breeding performance. We assessed whether woodpeckers perform defensive behaviours against the starlings by exposing taxidermied starling models to woodpecker breeding pairs. We detected interactions with starlings at 11% of the nests. These nests had a significantly higher probability of being abandoned during the early stages (until the third incubation day), compared to nests without interactions. Moreover, woodpeckers attacked and made distress calls more frequently in response to the presentation of the starling, compared to predator and non-competing species models. We also documented evidence of joint nesting, as four breeding pairs of woodpeckers shared their nest chamber with starlings. Our results indicate that neotropical woodpeckers are more likely to abandon their cavity when they interact with starlings. As the starling is expanding quickly in Argentina, this information points at the need to develop management programmes to control the impacts of this invasive species on the native fauna, especially on species with conservation concerns.
DOI: 10.6084/m9.figshare.14600697
title: Transformation in the Stability of Tide-induced Upper Saline Plume Driven by Transient External Forcing

description: These data contains experimental and simulated results related to our latest work—Fang et al., Transformation in the Stability of Tide-induced Upper Saline Plume Driven by Transient External Forcing, under review.
DOI: 10.6084/m9.figshare.14600697.v1
title: Transformation in the Stability of Tide-induced Upper Saline Plume Driven by Transient External Forcing

description: These data contains experimental and simulated results related to our latest work—Fang et al., Transformation in the Stability of Tide-induced Upper Saline Plume Driven by Transient External Forcing, under review.
DOI: 10.6084/m9.figshare.9976928
title: Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

description: In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently nonzero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high-dimensional penalized generalized linear mixed model is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.9976928.v3
title: Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

description: In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently nonzero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high-dimensional penalized generalized linear mixed model is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.