cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22047526
title: Designing of new tetrahydro-β-carboline-based ABCG2 inhibitors using 3D-QSAR, molecular docking, and DFT tools

description: Human ATP-binding cassette superfamily G member 2 (ABCG2) protein is a member of the ABC transporter family, which is responsible for multidrug resistance (MDR) in cancerous cells. MDR reduces the effectiveness of chemotherapy in breast cancer, which is one of the leading causes of death in women globally. MDR in cancer cells is one of the immediate signs of progression of resistance; thus, various anticancer drugs can be designed. To reduce MDR, we utilized the tetrahydro-β-carboline (THβC) compound library. We accomplished a three-dimensional quantitative structure-activity relationship (3D-QSAR), scaffold hopping to design a new library of compounds of THβC, and further molecular docking, induced-fit docking (IFD), molecular mechanics energies combined with generalized born and surface area continuum solvation (MM-GBSA), drug-like features, ADMET properties, and density functional theory (DFT) studies were performed. From these studies, the best 3D-QSAR model (r<sup>2</sup> = 0.99, q<sup>2</sup> = 0.92) was found, and the necessity of electrostatic, steric, and hydrophobic field effects were determined that could modulate bioactivity. Moreover, based on electrostatic, steric, and hydrophobic field notations, new THβC derivatives (3409) were designed. These findings might provide new insight for researchers to perform <i>in vitro</i> and <i>in vivo</i> studies for better antagonists against MDR in treating breast cancer. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22047526.v1
title: Designing of new tetrahydro-β-carboline-based ABCG2 inhibitors using 3D-QSAR, molecular docking, and DFT tools

description: Human ATP-binding cassette superfamily G member 2 (ABCG2) protein is a member of the ABC transporter family, which is responsible for multidrug resistance (MDR) in cancerous cells. MDR reduces the effectiveness of chemotherapy in breast cancer, which is one of the leading causes of death in women globally. MDR in cancer cells is one of the immediate signs of progression of resistance; thus, various anticancer drugs can be designed. To reduce MDR, we utilized the tetrahydro-β-carboline (THβC) compound library. We accomplished a three-dimensional quantitative structure-activity relationship (3D-QSAR), scaffold hopping to design a new library of compounds of THβC, and further molecular docking, induced-fit docking (IFD), molecular mechanics energies combined with generalized born and surface area continuum solvation (MM-GBSA), drug-like features, ADMET properties, and density functional theory (DFT) studies were performed. From these studies, the best 3D-QSAR model (r<sup>2</sup> = 0.99, q<sup>2</sup> = 0.92) was found, and the necessity of electrostatic, steric, and hydrophobic field effects were determined that could modulate bioactivity. Moreover, based on electrostatic, steric, and hydrophobic field notations, new THβC derivatives (3409) were designed. These findings might provide new insight for researchers to perform <i>in vitro</i> and <i>in vivo</i> studies for better antagonists against MDR in treating breast cancer. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22566749
title: Machine-learning technique, QSAR and molecular dynamics for hERG–drug interactions

description: One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (<i>in silico</i>) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound’s IC<sub>50</sub> value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew’s correlation coefficient formula and F1score were applied to compare and track the developed models’ performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG–gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG–ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22517992.v1
title: Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies

description: Endocrine disrupter chemicals (EDCs) are both natural and man-made chemicals that mimic, block or interfere with human hormonal system. In the present manuscript, QSAR modeling was performed for the androgen disruptors that interfere with biosynthesis, metabolism or action of androgens that causes adverse effects on male reproductive system. A set of 96 EDCs that exhibited affinity towards androgen receptors (Log RBA) in rats were employed for carrying out QSAR studies using Hybrid descriptors (combination of HFG and SMILES) through Monte Carlo Optimization. Using index of ideality of correlation (TF2), five splits were formed and predictability of five models resulting from these splits was assessed by various validation parameters. Models resulted from first split was the top most one with R<sup>2</sup><sub>validation</sub> = 0.7878. Structural attributes responsible for change in endpoint were studied by employing correlation weights of structural attributes. In order to further validate the model, new EDCs were designed using these attributes. <i>In silico</i> molecular modelling studies were performed to assess the detailed interactions with the receptor. The binding energies of all the designed compounds were observed to be better than lead and are in the range of −10.46 to −14.80. Molecular dynamics simulation of 100 ns was performed for <b>ED01</b> and <b>NED05</b>. The results revealed that the protein-ligand complex bearing <b>NED05</b> was more stable than lead <b>ED01</b> exhibiting better interactions with the receptor. Further, in an attempt to assess their metabolism, ADME studies were evaluated using SwissADME. The developed model enables to predict the characteristics of designed compounds in an authentic way. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22566749.v1
title: Machine-learning technique, QSAR and molecular dynamics for hERG–drug interactions

description: One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (<i>in silico</i>) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound’s IC<sub>50</sub> value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew’s correlation coefficient formula and F1score were applied to compare and track the developed models’ performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG–gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG–ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22193597
title: Identification of potential biological target for trypanocidal sesquiterpene lactones derivatives

description: Sesquiterpene lactones are natural products of the Asteraceae family that have shown trypanocidal activity against <i>Trypanosoma cruzi</i>, even exceeding the effectiveness of drugs used in the treatment of American trypanosomiasis. However, there is no agreement on their mechanism of action and their specificity to interact with parasite proteins. For this reason, we aimed to find biological targets that can interact with these compounds by reverse virtual screening with ligand pharmacophores and putative binding sites and the use of bioinformatic databases. Therefore, 41 possible biological targets were found, and four of them (with crystallized proteins), interfering directly or indirectly in the trypanosomatid redox system, were studied in detail. As a first approach, we focused on the study of trypanothione reductase, and protein-ligand interaction fingerprint analyses were performed to find binding site determinants that promote a possible inhibition of the enzyme. This study contributes to the understanding of one of the putative mechanisms of action of sesquiterpene lactones on one of the numerous suggested targets. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22193597.v1
title: Identification of potential biological target for trypanocidal sesquiterpene lactones derivatives

description: Sesquiterpene lactones are natural products of the Asteraceae family that have shown trypanocidal activity against <i>Trypanosoma cruzi</i>, even exceeding the effectiveness of drugs used in the treatment of American trypanosomiasis. However, there is no agreement on their mechanism of action and their specificity to interact with parasite proteins. For this reason, we aimed to find biological targets that can interact with these compounds by reverse virtual screening with ligand pharmacophores and putative binding sites and the use of bioinformatic databases. Therefore, 41 possible biological targets were found, and four of them (with crystallized proteins), interfering directly or indirectly in the trypanosomatid redox system, were studied in detail. As a first approach, we focused on the study of trypanothione reductase, and protein-ligand interaction fingerprint analyses were performed to find binding site determinants that promote a possible inhibition of the enzyme. This study contributes to the understanding of one of the putative mechanisms of action of sesquiterpene lactones on one of the numerous suggested targets. Communicated by Ramaswamy H. Sarma
DOI: 10.48677/a6078775-7d00-4127-8801-fff4d17ef09f
title: Verwaltungsgrenzen (VGD) BEV 1:50 000 Stichtag 01.10.2022

description: Die Verwaltungsgrenzen Stichtag BEV beinhalten die Grenzen der Verwaltungseinheiten Österreichs im Schnittstellenschema des BEV.

Die Daten sind für folgende Grenzen vorhanden: 
1) Katastralgemeindegrenze 
2) Gemeindegrenze 
3) Bezirksgrenze 
4) Gerichtsbezirksgrenze 
5) Vermessungssprengelgrenze 
6) Finanzamtsbereichsgrenze
7) Bundesländergrenze 
8) Staatsgrenze

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.6084/m9.figshare.22068025
title: Exploring the potential and identifying <i>Withania somnifera</i> alkaloids as novel dihydrofolate reductase (DHFR) inhibitors by the AlteQ method

description: There is an urgent need to discover and develop novel drugs to combat <i>Mycobacterium tuberculosis</i>, the causative agent of tuberculosis (TB) in humans. Alkaloids have been shown to have wide-ranging therapeutic application and could be ideal candidates for drug development, and research is underway to develop new anti-tubercular drugs from natural sources. In this regard, the current research deals with finding novel lead compounds from the <i>Withania somnifera</i> (WS) plant. Broad health benefits of WS are due to the presence of diverse chemical constituents which include anaferine and anahygrine and which belong to the alkaloid family. In the present study, these two compounds have been theoretically studied to understand their electronic properties using the density functional theory (DFT) at the B3LYP/6-311 + G (d,p) level. HOMO and LUMO properties and molecular electrostatic potential (MEP) surface were calculated. Further, to understand the mechanism of action of these compounds and to identify their putative drug target, molecular docking and dynamics studies were employed against <i>Mycobacterium tuberculosis</i> dihydrofolate reductase (DHFR). It was determined that NADP<sup>+</sup> affects stability of the complexes by reducing fluctuations of residues 14–23 and 117–126. It was also found that Ile5 and Gln28 play an important role in complexation. Electron density analysis (using the AlteQ method) of the intermolecular region, analyzing both the anaferin-NADP<sup>+</sup> and anahygrin-NADP<sup>+</sup> complexes showed that anaferin and anahygrin complexes are more stable in the presence of NADP<sup>+</sup>. It has been established that in most intermolecular contacts the contribution of the ligand to the electron density is greater than that of NADP<sup>+</sup>. The present study thus provides an excellent way to analyze the effect of anaferine and anahygrine in essential processes of <i>M. tuberculosis</i>. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22068025.v1
title: Exploring the potential and identifying <i>Withania somnifera</i> alkaloids as novel dihydrofolate reductase (DHFR) inhibitors by the AlteQ method

description: There is an urgent need to discover and develop novel drugs to combat <i>Mycobacterium tuberculosis</i>, the causative agent of tuberculosis (TB) in humans. Alkaloids have been shown to have wide-ranging therapeutic application and could be ideal candidates for drug development, and research is underway to develop new anti-tubercular drugs from natural sources. In this regard, the current research deals with finding novel lead compounds from the <i>Withania somnifera</i> (WS) plant. Broad health benefits of WS are due to the presence of diverse chemical constituents which include anaferine and anahygrine and which belong to the alkaloid family. In the present study, these two compounds have been theoretically studied to understand their electronic properties using the density functional theory (DFT) at the B3LYP/6-311 + G (d,p) level. HOMO and LUMO properties and molecular electrostatic potential (MEP) surface were calculated. Further, to understand the mechanism of action of these compounds and to identify their putative drug target, molecular docking and dynamics studies were employed against <i>Mycobacterium tuberculosis</i> dihydrofolate reductase (DHFR). It was determined that NADP<sup>+</sup> affects stability of the complexes by reducing fluctuations of residues 14–23 and 117–126. It was also found that Ile5 and Gln28 play an important role in complexation. Electron density analysis (using the AlteQ method) of the intermolecular region, analyzing both the anaferin-NADP<sup>+</sup> and anahygrin-NADP<sup>+</sup> complexes showed that anaferin and anahygrin complexes are more stable in the presence of NADP<sup>+</sup>. It has been established that in most intermolecular contacts the contribution of the ligand to the electron density is greater than that of NADP<sup>+</sup>. The present study thus provides an excellent way to analyze the effect of anaferine and anahygrine in essential processes of <i>M. tuberculosis</i>. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22883345
title: Anti-inflammatory potential of selective small compounds by targeting TNF-α & NF-kB signaling: a comprehensive molecular docking and simulation study

description: Tumor necrosis factor alpha (TNF-α) is the major cause of inflammation in autoimmune diseases like rheumatoid arthritis (RA). It’s mechanisms of signal transduction through nuclear factor kappa B (NF-kB) pathway via small molecules such as metabolite crosstalk are still elusive. In this study, we have targeted TNF-α and NF-kB through metabolites of RA, to inhibit TNF-α activity and deter NF-kB signaling pathways, thereby mitigating the disease severity of RA. TNF-α and NF-kB structure was obtained from PDB database and metabolites of RA were selected from literature survey. <i>In-silico</i> studies were carried out by molecular docking using AutoDock Vina software and further, known TNF-α and NF-kB inhibitors were compared and revealed metabolite’s capacity to targets the respective proteins. Most suitable metabolite was then validated by MD simulation to verify its efficiency against TNF-α. Total 56 known differential metabolites of RA were docked with TNF-α and NF-kB compared to their corresponding inhibitor compounds. Four metabolites such as Chenodeoxycholic acid, 2-Hydroxyestrone, 2-Hydroxyestradiol (2-OHE2), and 16-Hydroxyestradiol were identified as a common TNF-α inhibitor’s having binding energies ranging from −8.3 to −8.6 kcal/mol, followed by docking with NF-kB. Further, 2-OHE2 was selected because of having binding energy −8.5 kcal/mol, found to inhibit inflammation and the effectiveness was validated by root mean square fluctuation, radius of gyration and molecular mechanics with generalized born and surface area solvation against TNF-α. Thus 2-OHE2, an estrogen metabolite was identified as the potential inhibitor, attenuated inflammatory activation and can be utilized as a therapeutic target to disseminate severity of RA. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22883345.v1
title: Anti-inflammatory potential of selective small compounds by targeting TNF-α & NF-kB signaling: a comprehensive molecular docking and simulation study

description: Tumor necrosis factor alpha (TNF-α) is the major cause of inflammation in autoimmune diseases like rheumatoid arthritis (RA). It’s mechanisms of signal transduction through nuclear factor kappa B (NF-kB) pathway via small molecules such as metabolite crosstalk are still elusive. In this study, we have targeted TNF-α and NF-kB through metabolites of RA, to inhibit TNF-α activity and deter NF-kB signaling pathways, thereby mitigating the disease severity of RA. TNF-α and NF-kB structure was obtained from PDB database and metabolites of RA were selected from literature survey. <i>In-silico</i> studies were carried out by molecular docking using AutoDock Vina software and further, known TNF-α and NF-kB inhibitors were compared and revealed metabolite’s capacity to targets the respective proteins. Most suitable metabolite was then validated by MD simulation to verify its efficiency against TNF-α. Total 56 known differential metabolites of RA were docked with TNF-α and NF-kB compared to their corresponding inhibitor compounds. Four metabolites such as Chenodeoxycholic acid, 2-Hydroxyestrone, 2-Hydroxyestradiol (2-OHE2), and 16-Hydroxyestradiol were identified as a common TNF-α inhibitor’s having binding energies ranging from −8.3 to −8.6 kcal/mol, followed by docking with NF-kB. Further, 2-OHE2 was selected because of having binding energy −8.5 kcal/mol, found to inhibit inflammation and the effectiveness was validated by root mean square fluctuation, radius of gyration and molecular mechanics with generalized born and surface area solvation against TNF-α. Thus 2-OHE2, an estrogen metabolite was identified as the potential inhibitor, attenuated inflammatory activation and can be utilized as a therapeutic target to disseminate severity of RA. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.22232798
title: In silico screening of inhibitors against human dihydrofolate reductase to identify potential anticancer compounds

description: In all species, dihydrofolate reductase (DHFR) is an essential enzyme that regulates the cellular amount of tetrahydrofolate. Human DHFR (hDHFR) activity inhibition results in tetrahydrofolate depletion and cell death. This property has made hDHFR a therapeutic target for cancer. Methotrexate is a well-known hDHFR inhibitor, but its administration has shown some light to severe adverse effects. Therefore, we aimed to find new potential hDHFR inhibitors using structure-based virtual screening, ADMET prediction, molecular docking, and molecular dynamics simulations. Here, we used the PubChem database to find all compounds with at least 90% structural similarity to known natural DHFR inhibitors. To explore their interaction pattern and estimate their binding affinities, the screened compounds (2023) were subjected to structure-based molecular docking against hDHFR. The fifteen compounds that showed higher binding affinity to the hDHFR than the reference compound (methotrexate) displayed important molecular orientation and interactions with key residues in the enzyme’s active site. These compounds were subjected to Lipinski and ADMET prediction. PubChem CIDs: 46886812 and 638190 were identified as putative inhibitors. In addition, molecular dynamics simulations revealed that the binding of compounds (CIDs: 46886812 and 63819) stabilized the hDHFR structure and caused minor conformational changes. Our findings suggest that two compounds (CIDs: 46886812 and 63819) could be promising potential inhibitors of hDHFR in cancer therapy. Communicated by Ramaswamy H. Sarma