cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21716215
title: Pharmacoinformatic screening of phytoconstituent and evaluation of its anti-PDAC effect using <i>in vitro</i> studies

description: With no prominent treatment for pancreatic ductal adenocarcinoma (PDAC) in conventional chemotherapy, recent studies have focused on uniting conventional and traditional medicines including plant phytoconstituents. Herein, we used pharmacoinformatic studies to identify potent phytoconstituent as ligand having inhibition activities against canonical anticancer targets, and evaluated its effect on PDAC cell lines. SwissTargetPrediction and SuperPred tools were utilized to segregate protein targets of ligand in humans, following which FunRich was applied to garner its targets in PDAC. STRING analysis predicted protein–protein interactions and dynamic simulation studies confirmed stability of ligand–protein complex. For <i>in vitro</i> cytotoxic potential, ligand treatment at different concentrations was given to PDAC cell lines both alone and combined with gemcitabine, followed by evaluation of effects on migration. Differential gene expression was checked using PCR for evaluating mechanism of cytotoxicity. Results showed pentagalloylglucose (PGG) with highest docking and MMGBSA scores for Cyclooxygenase 2 (Cox2) inhibition site. SwissTargetPrediction and SuperPred analysis detected 40 targets of PGG in PDAC. Simulation data showed stability of protein–ligand complex. In i<i>n vitro</i> experiments Mia-PaCa-2 was more sensitive to PGG than Panc-1. PGG successfully inhibited migration both alone and in combination with gemcitabine. Additionally, PGG treatment induced apoptosis in both the cell lines; but showed antagonism when combined with gemcitabine. In conclusion, our report demonstrates PGG has good binding with Cox2 and showed anti-PDAC activity by inhibiting migration and inducing apoptosis, thus it can be used as a therapy option. But further studies are required to confirm its behaviour as a combination therapy drug. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21716215.v1
title: Pharmacoinformatic screening of phytoconstituent and evaluation of its anti-PDAC effect using <i>in vitro</i> studies

description: With no prominent treatment for pancreatic ductal adenocarcinoma (PDAC) in conventional chemotherapy, recent studies have focused on uniting conventional and traditional medicines including plant phytoconstituents. Herein, we used pharmacoinformatic studies to identify potent phytoconstituent as ligand having inhibition activities against canonical anticancer targets, and evaluated its effect on PDAC cell lines. SwissTargetPrediction and SuperPred tools were utilized to segregate protein targets of ligand in humans, following which FunRich was applied to garner its targets in PDAC. STRING analysis predicted protein–protein interactions and dynamic simulation studies confirmed stability of ligand–protein complex. For <i>in vitro</i> cytotoxic potential, ligand treatment at different concentrations was given to PDAC cell lines both alone and combined with gemcitabine, followed by evaluation of effects on migration. Differential gene expression was checked using PCR for evaluating mechanism of cytotoxicity. Results showed pentagalloylglucose (PGG) with highest docking and MMGBSA scores for Cyclooxygenase 2 (Cox2) inhibition site. SwissTargetPrediction and SuperPred analysis detected 40 targets of PGG in PDAC. Simulation data showed stability of protein–ligand complex. In i<i>n vitro</i> experiments Mia-PaCa-2 was more sensitive to PGG than Panc-1. PGG successfully inhibited migration both alone and in combination with gemcitabine. Additionally, PGG treatment induced apoptosis in both the cell lines; but showed antagonism when combined with gemcitabine. In conclusion, our report demonstrates PGG has good binding with Cox2 and showed anti-PDAC activity by inhibiting migration and inducing apoptosis, thus it can be used as a therapy option. But further studies are required to confirm its behaviour as a combination therapy drug. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21802957
title: Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach

description: Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive <i>insilico</i> analysis. Among the 319 missense nsSNPs reported, various <i>insilco</i> tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated <i>in vitro</i> to understand the association with some genetic diseases. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21802957.v1
title: Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach

description: Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive <i>insilico</i> analysis. Among the 319 missense nsSNPs reported, various <i>insilco</i> tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated <i>in vitro</i> to understand the association with some genetic diseases. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21802960
title: Exploring the binding capacity of lactic acid bacteria derived bacteriocins against RBD of SARS-CoV-2 Omicron variant by molecular simulations

description: The changes in the SARS-CoV-2 genome have resulted in the emergence of new variants. Some of the variants have been classified as variants of concern (VOC). These strains have higher transmission rate and improved fitness. One of the prevalent were the Omicron variant. Unlike previous VOCs, the Omicron possesses fifteen mutations on the spike protein's receptor binding domain (RBD). The modifications of spike protein's key amino acid residues facilitate the virus' binding capability against ACE2, resulting in an increase in the infectiousness of Omicron variant. Consequently, investigating the prevention and treatment of the Omicron variant is crucial. In the present study, we aim to explore the binding capacity of twenty-two bacteriocins derived from Lactic Acid Bacteria (LAB) against the Omicron variant by using protein-peptidedocking and molecular dynamics (MD) simulations. The Omicron variant RBD was prepared by introducing fifteen mutations using PyMol. The protein-peptide complexes were obtained using HADDOCK v2.4 docking webserver. Top scoring complexes obtained from HADDOCK webserver were retrieved and submitted to the PRODIGY server for the prediction of binding energies. RBD-bacteriocin complexes were subjected to MD simulations. We discovered promising peptide-based therapeutic candidates for the inhibition of Omicron variant for example Salivaricin B, Pediocin PA 1, Plantaricin W, Lactococcin mmfii and Enterocin A. The lead bacteriocins, except Enterocin A, are biosynthesized by food-grade lactic acid bacteria. Our study puts forth a preliminary information regarding potential utilization of food-grade LAB-derived bacteriocins, particularly Salivaricin B and Pediocin PA 1, for Covid-19 treatment and prophylaxis. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21786750
title: Exploring molecular docking with MM-GBSA and molecular dynamics simulation to predict potent inhibitors of cyclooxygenase (COX-2) enzyme from terpenoid-based active principles of <i>Zingiber</i> species

description: Cyclooxygenase 2 (COX-2), the key enzyme involved in prostaglandin (PGs) production, is known to take part in inflammatory and immune responses. Though COX-2 inhibitors are therapeutically effective anti-inflammatory drugs, they deficit anti-thrombotic activity thus leading to increased cardiovascular diseases. Therefore, COX-2 inhibitors with improved therapeutic efficacy and tolerance are still needed. In recent years, traditional medicine systems have paid attention to the essential oil of genus <i>Zingiber</i>, particularly for the treatment of various inflammatory illnesses, with lesser side effects. Thus, the present study aims to explore the anti-inflammatory activity of <i>Zingiber</i> essential oil through computational-biology approaches. In this regard, virtual screening, molecular docking, and simulations were carried out on 53 compounds derived from the essential oil of <i>Zingiber</i> species in order to provide mechanistic insights into COX-2 inhibition and identify the most actively potent anti-inflammatory compounds. Among all the docked ligands, epi-cubenol, δ-cadinene, γ-eudesmol, cubenol, and α-terpineol were found to be powerful bioactive compounds with an increased binding affinity towards COX-2 along with favorable physiochemical properties. Additionally, MD simulation in DPPC lipid bilayers was studied to examine the intrinsic dynamics and adaptability of the chosen ligands and COX-2-complexes. The findings showed that the selected five components interacted steadily with the COX-2 active site residues throughout the simulation via different bondings. The integrative-computational approach showed that the identified natural compounds may be taken into further consideration for potential <i>in vitro</i> and <i>in vivo</i> evaluation as COX-2 inhibitors, which would lead to the development of more potent and efficient anti-inflammatory drugs. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21802960.v1
title: Exploring the binding capacity of lactic acid bacteria derived bacteriocins against RBD of SARS-CoV-2 Omicron variant by molecular simulations

description: The changes in the SARS-CoV-2 genome have resulted in the emergence of new variants. Some of the variants have been classified as variants of concern (VOC). These strains have higher transmission rate and improved fitness. One of the prevalent were the Omicron variant. Unlike previous VOCs, the Omicron possesses fifteen mutations on the spike protein's receptor binding domain (RBD). The modifications of spike protein's key amino acid residues facilitate the virus' binding capability against ACE2, resulting in an increase in the infectiousness of Omicron variant. Consequently, investigating the prevention and treatment of the Omicron variant is crucial. In the present study, we aim to explore the binding capacity of twenty-two bacteriocins derived from Lactic Acid Bacteria (LAB) against the Omicron variant by using protein-peptidedocking and molecular dynamics (MD) simulations. The Omicron variant RBD was prepared by introducing fifteen mutations using PyMol. The protein-peptide complexes were obtained using HADDOCK v2.4 docking webserver. Top scoring complexes obtained from HADDOCK webserver were retrieved and submitted to the PRODIGY server for the prediction of binding energies. RBD-bacteriocin complexes were subjected to MD simulations. We discovered promising peptide-based therapeutic candidates for the inhibition of Omicron variant for example Salivaricin B, Pediocin PA 1, Plantaricin W, Lactococcin mmfii and Enterocin A. The lead bacteriocins, except Enterocin A, are biosynthesized by food-grade lactic acid bacteria. Our study puts forth a preliminary information regarding potential utilization of food-grade LAB-derived bacteriocins, particularly Salivaricin B and Pediocin PA 1, for Covid-19 treatment and prophylaxis. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21786750.v1
title: Exploring molecular docking with MM-GBSA and molecular dynamics simulation to predict potent inhibitors of cyclooxygenase (COX-2) enzyme from terpenoid-based active principles of <i>Zingiber</i> species

description: Cyclooxygenase 2 (COX-2), the key enzyme involved in prostaglandin (PGs) production, is known to take part in inflammatory and immune responses. Though COX-2 inhibitors are therapeutically effective anti-inflammatory drugs, they deficit anti-thrombotic activity thus leading to increased cardiovascular diseases. Therefore, COX-2 inhibitors with improved therapeutic efficacy and tolerance are still needed. In recent years, traditional medicine systems have paid attention to the essential oil of genus <i>Zingiber</i>, particularly for the treatment of various inflammatory illnesses, with lesser side effects. Thus, the present study aims to explore the anti-inflammatory activity of <i>Zingiber</i> essential oil through computational-biology approaches. In this regard, virtual screening, molecular docking, and simulations were carried out on 53 compounds derived from the essential oil of <i>Zingiber</i> species in order to provide mechanistic insights into COX-2 inhibition and identify the most actively potent anti-inflammatory compounds. Among all the docked ligands, epi-cubenol, δ-cadinene, γ-eudesmol, cubenol, and α-terpineol were found to be powerful bioactive compounds with an increased binding affinity towards COX-2 along with favorable physiochemical properties. Additionally, MD simulation in DPPC lipid bilayers was studied to examine the intrinsic dynamics and adaptability of the chosen ligands and COX-2-complexes. The findings showed that the selected five components interacted steadily with the COX-2 active site residues throughout the simulation via different bondings. The integrative-computational approach showed that the identified natural compounds may be taken into further consideration for potential <i>in vitro</i> and <i>in vivo</i> evaluation as COX-2 inhibitors, which would lead to the development of more potent and efficient anti-inflammatory drugs. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21762873
title: Design of new chemical entities targeting both native and H275Y mutant influenza a virus by deep reinforcement learning

description: Influenza virus remains a major public health challenge due to its high morbidity and mortality and seasonal surge. Although antiviral drugs against the influenza virus are widely used as a first-line defense, the virus undergoes rapid genetic changes, resulting in the emergence of drug-resistant strains. Thus, new antiviral drugs that can outwit resistant strains are of significant importance. Herein, we used deep reinforcement learning (RL) algorithm to design new chemical entities (NCEs) that are able to bind to the native and H275Y mutant (oseltamivir-resistant) neuraminidases (NAs) of influenza A virus with better binding energy than oseltamivir. We generated more than 66211 NCEs, which were prioritized based on the filtering rules, structural alerts, and synthetic accessibility. Then, 18 NCEs with better MM/PBSA scores than oseltamivir were further analyzed in molecular dynamics (MD) simulations conducted for 100 ns. The MD experiments showed that 8 NCEs formed very stable complexes with the binding pocket of both native and H275Y mutant NAs of H1N1. Furthermore, most NCEs demonstrated much better binding affinity to group 2 (N2, N3, and N9) and influenza B virus NAs than oseltamivir. Although all 8 NCEs have non-sialic acid-like structures, they showed a similar binding mode as oseltamivir, indicating that it is possible to find new scaffolds with better binding and antiviral properties than sialic acid-like inhibitors. In conclusion, we have designed potential compounds as antiviral candidates for further synthesis and testing against wild and mutant influenza virus. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21762873.v1
title: Design of new chemical entities targeting both native and H275Y mutant influenza a virus by deep reinforcement learning

description: Influenza virus remains a major public health challenge due to its high morbidity and mortality and seasonal surge. Although antiviral drugs against the influenza virus are widely used as a first-line defense, the virus undergoes rapid genetic changes, resulting in the emergence of drug-resistant strains. Thus, new antiviral drugs that can outwit resistant strains are of significant importance. Herein, we used deep reinforcement learning (RL) algorithm to design new chemical entities (NCEs) that are able to bind to the native and H275Y mutant (oseltamivir-resistant) neuraminidases (NAs) of influenza A virus with better binding energy than oseltamivir. We generated more than 66211 NCEs, which were prioritized based on the filtering rules, structural alerts, and synthetic accessibility. Then, 18 NCEs with better MM/PBSA scores than oseltamivir were further analyzed in molecular dynamics (MD) simulations conducted for 100 ns. The MD experiments showed that 8 NCEs formed very stable complexes with the binding pocket of both native and H275Y mutant NAs of H1N1. Furthermore, most NCEs demonstrated much better binding affinity to group 2 (N2, N3, and N9) and influenza B virus NAs than oseltamivir. Although all 8 NCEs have non-sialic acid-like structures, they showed a similar binding mode as oseltamivir, indicating that it is possible to find new scaffolds with better binding and antiviral properties than sialic acid-like inhibitors. In conclusion, we have designed potential compounds as antiviral candidates for further synthesis and testing against wild and mutant influenza virus. Communicated by Ramaswamy H. Sarma
DOI: 10.5281/zenodo.10090707
title: Blankenburg Rats-Apotheke

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a
German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf.
gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope):
https://kalliope-verbund.info/DE-611-BF-70963
[Funktion: Im Findbuch anzeigen]
 
Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot
be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf
Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht
veröffentlicht werden dürfen).
DOI: 10.5281/zenodo.10090715
title: Heat risk map of Riyadh

description: The dataset is used to assess the urban heat risk in the city of Riyadh using proxy variables to evaluate the environmental, infrastructural, and social dimensions of the city.
The environmental component was evaluated using the mean values of land surface temperature (LST), air temperature (T2m), and discomfort index (DI) across the districts of Riyadh. These factors, derived from data like MODIS LST and available WRF simulations, represented the degree of heat exposure in different regions. 
The infrastructural component of heat risk was evaluated by looking at the city's infrastructure, that is the building density per district. Buildings can act as "heat traps," thus higher building density suggests increased heat risk.
The social component considered demographic factors such as the percentage of the population over 65 old (OP) and under 14 years old (YP), which can indicate sensitivity to extreme heat conditions. 
To map the heat risk, these components were combined into a composite heat risk indicator. For this to be achieved, each parameter was reclassified into three categories (1-less, 2-moderate, and 3-high) using the quantile classification which is a data classification method that distributes a set of values into groups that contain an equal number of values. 
 
                                      LST  (°C)                   DI                     T2m  (°C)                <14 y.o. (%)             >65 y.o (%)                Buildings per sq. m.(BD)
1-Less risk                       <47.2                    <28                         <40.6                       <23                          <1                                      <66
2-Moderate risk       47.2 ≤ LST ≤ 47.9    28≤ DI ≤ 28.2     40.6 ≤ T2m ≤ 40.8       23≤ YP ≤28               1≤ OP ≤ 3                         66≤ BD ≤ 109
3-High risk                      >47.9                    >28.2                       >40.8                        >28                          >3                                     >109
LST: Land Surface Temperature; DI: Discomfort Index; T2m: Air temperature at 2m height; YP<14 y.o.: People under 14 years old; OP y.o.: Older people over 65 years old; 
Since the relative importance of each parameter is unknown, we considered that all parameters contributed equally to the composite heat risk index and the arithmetic values were aggregated. The final value for each district was then reclassified into three categories using the quantile classification method resulting in the final three categories of Urban Heat Risk (Less heat risk, Moderate heat risk, High heat risk)