cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6p9f8v
title: Occurrence Download

description: A dataset containing 1389 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eunicidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1389 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001567-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24843573
title: Structure-based molecular docking and molecular dynamics simulations study for the identification of dipeptidyl peptidase 4 inhibitors in type 2 diabetes

description: Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of −9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.
DOI: 10.6084/m9.figshare.24843573.v1
title: Structure-based molecular docking and molecular dynamics simulations study for the identification of dipeptidyl peptidase 4 inhibitors in type 2 diabetes

description: Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of −9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.
DOI: 10.6084/m9.figshare.24781532
title: Investigating the multitargeted anti-diabetic potential of cucurbitane-type triterpenoid from <i>Momordica charantia</i>: an LC-MS, docking-based MM\GBSA and MD simulation study

description: Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in <i>Momordica charantia</i> and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from <i>Momordica charantia</i> using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of <i>M. charantia</i>. The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, <i>in vitro</i> and <i>in vivo</i> studies of the ligand against the diabetic enzymes is recommended.
DOI: 10.6084/m9.figshare.24781532.v1
title: Investigating the multitargeted anti-diabetic potential of cucurbitane-type triterpenoid from <i>Momordica charantia</i>: an LC-MS, docking-based MM\GBSA and MD simulation study

description: Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in <i>Momordica charantia</i> and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from <i>Momordica charantia</i> using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of <i>M. charantia</i>. The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, <i>in vitro</i> and <i>in vivo</i> studies of the ligand against the diabetic enzymes is recommended.
DOI: 10.6084/m9.figshare.24774426
title: Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate

description: The fibroblast growth factor receptor 3 (FGFR3) is warranted as a promising therapeutic target in bladder cancer as it is described in 75% of papillary bladder tumors. Considering this, the present study was conducted to use different approaches of computer-aided drug discovery (CADD) to identify the best binding compounds against the active pocket of FGFR3. Compared to control pyrimidine derivative, the study identified three promising lead structures; BDC_24037121, BDC_21200852, and BDC_21206757 with binding energy value of −14.80 kcal/mol, −12.22 kcal/mol, and −11.67 kcal/mol, respectively. The control molecule binding energy score was −9.85 kcal/mol. The compounds achieved deep pocket binding and produced balanced interactions of hydrogen bonds and van der Waals. The FGFR3 enzyme residues such as Leu478, Lys508, Glu556, Asn562, Asn622, and Asp635. The molecular dynamic (MD) simulation studies additionally validated the docked conformation stability with respect to FGFR3 with a mean root mean square deviation (RMSD) value of &lt; 3 Å. The root mean square fluctuation (RMSF) complements the complexes structural stability and the residues showed less fluctuation in the presence of compounds. The Poisson–Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods revalidated compounds better binding and highlighted van der Waals energy to dominate the overall net energy. The docked stability was additionally confirmed by WaterSwap and AMBER normal mode entropy energy analyses. In a nutshell, the compounds shortlisted in this study are promising in term of theoretical binding affinity for FGFR3 but experimental validation is needed.
DOI: 10.6084/m9.figshare.24774426.v1
title: Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate

description: The fibroblast growth factor receptor 3 (FGFR3) is warranted as a promising therapeutic target in bladder cancer as it is described in 75% of papillary bladder tumors. Considering this, the present study was conducted to use different approaches of computer-aided drug discovery (CADD) to identify the best binding compounds against the active pocket of FGFR3. Compared to control pyrimidine derivative, the study identified three promising lead structures; BDC_24037121, BDC_21200852, and BDC_21206757 with binding energy value of −14.80 kcal/mol, −12.22 kcal/mol, and −11.67 kcal/mol, respectively. The control molecule binding energy score was −9.85 kcal/mol. The compounds achieved deep pocket binding and produced balanced interactions of hydrogen bonds and van der Waals. The FGFR3 enzyme residues such as Leu478, Lys508, Glu556, Asn562, Asn622, and Asp635. The molecular dynamic (MD) simulation studies additionally validated the docked conformation stability with respect to FGFR3 with a mean root mean square deviation (RMSD) value of &lt; 3 Å. The root mean square fluctuation (RMSF) complements the complexes structural stability and the residues showed less fluctuation in the presence of compounds. The Poisson–Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods revalidated compounds better binding and highlighted van der Waals energy to dominate the overall net energy. The docked stability was additionally confirmed by WaterSwap and AMBER normal mode entropy energy analyses. In a nutshell, the compounds shortlisted in this study are promising in term of theoretical binding affinity for FGFR3 but experimental validation is needed.
DOI: 10.6084/m9.figshare.24781534
title: Crystallographic mining driven computer-guided approach to identify the ASK1 inhibitor likely to perturb the catalytic region

description: The pathological levels of reactive oxygen species (ROS) and oxidative stress has been recognized as a critical driver for inflammatory disorders. Apoptosis signal-regulating kinase 1 (ASK1) has been reported to be activated by intracellular ROS and its inhibition leads to a down regulation of p38-and JNK-dependent signaling. ASK1 inhibitors are reported to have the potential to treat clinically important inflammatory pathologies including liver, pulmonary and renal disorders. In view of its biological and pathological significance, inhibition of ASK1 with small molecules has been pursued as an attractive strategy to combat human diseases such as non-alcoholic steatohepatitis (NASH). Despite several ASK1 inhibitors being developed, the failure in Phase 3 clinical trials of most advanced candidate selonsertib’s, underscores to discover therapeutic agents with diverse chemical moiety. Here, by using structural pharmacophore and enumeration strategy on mining co-crystals of ASK1, different scaffolds were generated to enhance the chemical diversity keeping the critical molecular interaction in the catalytic site intact. A total of 15,772 compounds were generated from diverse chemical scaffolds and were evaluated using a virtual screening pipeline. Based on docking and MM-GBSA scores, a lead candidate, S3C-1-D424 was identified from top hits. A comparative molecular dynamics simulations (MD) of APO, Selonsertib and shortlisted potential candidates combined with pharmacokinetics profiling and thermodynamic analysis, demonstrating their suitability as potential ASK1 inhibitors to explore further for establishment towards <i>hit-to-lead</i> campaign.
DOI: 10.6084/m9.figshare.24781534.v1
title: Crystallographic mining driven computer-guided approach to identify the ASK1 inhibitor likely to perturb the catalytic region

description: The pathological levels of reactive oxygen species (ROS) and oxidative stress has been recognized as a critical driver for inflammatory disorders. Apoptosis signal-regulating kinase 1 (ASK1) has been reported to be activated by intracellular ROS and its inhibition leads to a down regulation of p38-and JNK-dependent signaling. ASK1 inhibitors are reported to have the potential to treat clinically important inflammatory pathologies including liver, pulmonary and renal disorders. In view of its biological and pathological significance, inhibition of ASK1 with small molecules has been pursued as an attractive strategy to combat human diseases such as non-alcoholic steatohepatitis (NASH). Despite several ASK1 inhibitors being developed, the failure in Phase 3 clinical trials of most advanced candidate selonsertib’s, underscores to discover therapeutic agents with diverse chemical moiety. Here, by using structural pharmacophore and enumeration strategy on mining co-crystals of ASK1, different scaffolds were generated to enhance the chemical diversity keeping the critical molecular interaction in the catalytic site intact. A total of 15,772 compounds were generated from diverse chemical scaffolds and were evaluated using a virtual screening pipeline. Based on docking and MM-GBSA scores, a lead candidate, S3C-1-D424 was identified from top hits. A comparative molecular dynamics simulations (MD) of APO, Selonsertib and shortlisted potential candidates combined with pharmacokinetics profiling and thermodynamic analysis, demonstrating their suitability as potential ASK1 inhibitors to explore further for establishment towards <i>hit-to-lead</i> campaign.
DOI: 10.6084/m9.figshare.24807713
title: The effect of double (S238F/W159H) mutations on the structure and dynamics of PET degrading enzyme

description: Polyethylene terephthalate (PET) is one of the highly produced synthetic polymers worldwide and had acquired attention due to its impact resistance, high clarity, and light weight. PET has become the first choice in making disposable bottles, leading to massive scales of production resulting in very high utilization across various facets of our daily life. Unfortunately, PET accumulates as waste and is highly resistant to biodegradation, thus presenting a serious threat to the ecosystem. Degradation of PET by enzymatic hydrolysis is a promising strategy to depolymerize the PET into its monomers. In recent studies, a plastic-degrading enzyme known as PETase (<i>Is</i>PETase) from the <i>Ideonella sakaiensis</i> has been identified to hydrolyze PET. The wild-type enzyme from <i>Ideonella</i> sp., has been engineered to improve the catalytic activity. While the <i>Is</i>PETase and its variants have been the subject of extensive structural and biochemical studies, the corresponding computational studies to support the improved activity of the mutant enzyme is not fully understood. In this work, we employed all-atom classical molecular dynamics simulations of the wild-type and double mutant <i>Is</i>PETase enzymes to investigate the underlying reason for the improved catalytic activity in the double mutant by means of structure-dynamics-function relationship. Our results show that the engineered mutations reshape the active site structure, volume, and dynamics of the protein loops which is crucial for substrate binding. We also demonstrate that addition of aromatic and hydrogen bond-forming residues near catalytic site improves binding affinity. This work will enable the rational design of mutants for enhanced PET degrading activity.
DOI: 10.6084/m9.figshare.24807713.v1
title: The effect of double (S238F/W159H) mutations on the structure and dynamics of PET degrading enzyme

description: Polyethylene terephthalate (PET) is one of the highly produced synthetic polymers worldwide and had acquired attention due to its impact resistance, high clarity, and light weight. PET has become the first choice in making disposable bottles, leading to massive scales of production resulting in very high utilization across various facets of our daily life. Unfortunately, PET accumulates as waste and is highly resistant to biodegradation, thus presenting a serious threat to the ecosystem. Degradation of PET by enzymatic hydrolysis is a promising strategy to depolymerize the PET into its monomers. In recent studies, a plastic-degrading enzyme known as PETase (<i>Is</i>PETase) from the <i>Ideonella sakaiensis</i> has been identified to hydrolyze PET. The wild-type enzyme from <i>Ideonella</i> sp., has been engineered to improve the catalytic activity. While the <i>Is</i>PETase and its variants have been the subject of extensive structural and biochemical studies, the corresponding computational studies to support the improved activity of the mutant enzyme is not fully understood. In this work, we employed all-atom classical molecular dynamics simulations of the wild-type and double mutant <i>Is</i>PETase enzymes to investigate the underlying reason for the improved catalytic activity in the double mutant by means of structure-dynamics-function relationship. Our results show that the engineered mutations reshape the active site structure, volume, and dynamics of the protein loops which is crucial for substrate binding. We also demonstrate that addition of aromatic and hydrogen bond-forming residues near catalytic site improves binding affinity. This work will enable the rational design of mutants for enhanced PET degrading activity.
DOI: 10.6084/m9.figshare.27950517.v8
title: <b>CheloniansTraits:</b><b> a comprehensive trait database of global turtles and tortoises</b>

description: We compiled 69 trait data for all 358 recognized chelonian species, utilizing ~2,000 literature sources, covering 33 morphological, 21 life-history, 7 ecological traits, and 8 conservation information.
DOI: 10.15468/dl.v9y8jg
title: Occurrence Download

description: A dataset containing 1931 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polynoidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1931 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001568-250213122211068/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14847597
title: Inclusive Early Childhood Education (ECE) in ASEAN: Policies and Budget Dataset)

description: "This dataset covers inclusive early childhood education policies in five ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, and Vietnam), including budget data and implementation challenges."
DOI: 10.5281/zenodo.14847598
title: Inclusive Early Childhood Education (ECE) in ASEAN: Policies and Budget Dataset)

description: "This dataset covers inclusive early childhood education policies in five ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, and Vietnam), including budget data and implementation challenges."
DOI: 10.5281/zenodo.14869323
title: Inclusive Early Childhood Education (ECE) in ASEAN: Policies and Budget Dataset)

description: "This dataset covers inclusive early childhood education policies in five ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, and Vietnam), including budget data and implementation challenges."
DOI: 10.5281/zenodo.14544687
title: Data for Contrasting action and posture coding with hierarchical deep neural network models of proprioception

description: #############

Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023

#############

Authors: Kai J Sandbrink, Pranav Mamidanna, Claudio Michaelis, Matthias Bethge, Mackenzie W Mathis and Alexander Mathis

Affiliation: Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland, The Rowland Institute at Harvard, Harvard University, United States; Tübingen AI Center, Eberhard Karls Universität Tübingen & Institute for Theoretical Physics, Germany

Date of upload: December, 2024

Earlier the data was available via dropbox (see github).

Link to the eLife article: 

https://elifesciences.org/articles/81499

--------------------------------

Here we provide the data and code for this project:

We share the proprioceptive character recognition dataset (contained in 'pcr_data.zip') it has approximately ~29GB when uncompressed.

We share the weights of all the trained networks (contained in 'network-weights.zip'): about ~3.5GB

The compressed code is also available here ('DeepDrawCode.zip').

The activations are shared in a separate Zenodo project (due to the size). Check out the repository below to find the link.

The up to date code is at: https://github.com/amathislab/DeepDraw

--------------------------------

The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.

If you find this useful, please cite:

@article{sandbrink2023contrasting,  title={Contrasting action and posture coding with hierarchical deep neural network models of proprioception},  author={Sandbrink, Kai J and Mamidanna, Pranav and Michaelis, Claudio and Bethge, Matthias and Mathis, Mackenzie Weygandt and Mathis, Alexander},  journal={Elife},  volume={12},  pages={e81499},  year={2023},  publisher={eLife Sciences Publications Limited}}
DOI: 10.5281/zenodo.14544688
title: Data for Contrasting action and posture coding with hierarchical deep neural network models of proprioception

description: #############

Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023

#############

Authors: Kai J Sandbrink, Pranav Mamidanna, Claudio Michaelis, Matthias Bethge, Mackenzie W Mathis and Alexander Mathis

Affiliation: Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland, The Rowland Institute at Harvard, Harvard University, United States; Tübingen AI Center, Eberhard Karls Universität Tübingen & Institute for Theoretical Physics, Germany

Date of upload: December, 2024

Earlier the data was available via dropbox (see github).

Link to the eLife article: 

https://elifesciences.org/articles/81499

--------------------------------

Here we provide the data and code for this project:

We share the proprioceptive character recognition dataset (contained in 'pcr_data.zip') it has approximately ~29GB when uncompressed.

We share the weights of all the trained networks (contained in 'network-weights.zip'): about ~3.5GB

The compressed code is also available here ('DeepDrawCode.zip').

The activations are shared in a separate Zenodo project (due to the size). Check out the repository below to find the link.

The up to date code is at: https://github.com/amathislab/DeepDraw

--------------------------------

The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.

If you find this useful, please cite:

@article{sandbrink2023contrasting,  title={Contrasting action and posture coding with hierarchical deep neural network models of proprioception},  author={Sandbrink, Kai J and Mamidanna, Pranav and Michaelis, Claudio and Bethge, Matthias and Mathis, Mackenzie Weygandt and Mathis, Alexander},  journal={Elife},  volume={12},  pages={e81499},  year={2023},  publisher={eLife Sciences Publications Limited}}