cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13525696
title: Monte-Carlo method-based QSAR model to discover phytochemical urease inhibitors using SMILES and GRAPH descriptors

description: Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the <i>Helicobacter pylori</i> infection. Amongst a number of urease inhibitors, a large number of molecules fail <i>in vivo</i> and in clinical trials due to their hydrolytic instability and toxicity profile. The search for potential inhibitors may require screening of large and diverse databases of small molecules and to design novel molecules. We developed a Monte-Carlo method-based QSAR model to predict urease inhibiting potency of molecules using SMILES and GRAPH descriptors on an existing diverse database of urease inhibitors. The QSAR model satisfies all the statistical parameters required for acceptance as a good model. The model is applied to identify urease inhibitors among the wide range of compounds in the phytochemical database, NPACT, as a test case. We combine the ligand-based and structure-based drug discovery methods to improve the accuracy of the prediction. The method predicts pIC<sub>50</sub> and estimates docking score of compounds in the database. The method may be applied to any other database or compounds designed <i>in silico</i> to discover novel drugs targeting urease. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13525696.v2
title: Monte-Carlo method-based QSAR model to discover phytochemical urease inhibitors using SMILES and GRAPH descriptors

description: Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the <i>Helicobacter pylori</i> infection. Amongst a number of urease inhibitors, a large number of molecules fail <i>in vivo</i> and in clinical trials due to their hydrolytic instability and toxicity profile. The search for potential inhibitors may require screening of large and diverse databases of small molecules and to design novel molecules. We developed a Monte-Carlo method-based QSAR model to predict urease inhibiting potency of molecules using SMILES and GRAPH descriptors on an existing diverse database of urease inhibitors. The QSAR model satisfies all the statistical parameters required for acceptance as a good model. The model is applied to identify urease inhibitors among the wide range of compounds in the phytochemical database, NPACT, as a test case. We combine the ligand-based and structure-based drug discovery methods to improve the accuracy of the prediction. The method predicts pIC<sub>50</sub> and estimates docking score of compounds in the database. The method may be applied to any other database or compounds designed <i>in silico</i> to discover novel drugs targeting urease. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13525696.v1
title: Monte-Carlo method-based QSAR model to discover phytochemical urease inhibitors using SMILES and GRAPH descriptors

description: Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the <i>Helicobacter pylori</i> infection. Amongst a number of urease inhibitors, a large number of molecules fail <i>in vivo</i> and in clinical trials due to their hydrolytic instability and toxicity profile. The search for potential inhibitors may require screening of large and diverse databases of small molecules and to design novel molecules. We developed a Monte-Carlo method-based QSAR model to predict urease inhibiting potency of molecules using SMILES and GRAPH descriptors on an existing diverse database of urease inhibitors. The QSAR model satisfies all the statistical parameters required for acceptance as a good model. The model is applied to identify urease inhibitors among the wide range of compounds in the phytochemical database, NPACT, as a test case. We combine the ligand-based and structure-based drug discovery methods to improve the accuracy of the prediction. The method predicts pIC<sub>50</sub> and estimates docking score of compounds in the database. The method may be applied to any other database or compounds designed <i>in silico</i> to discover novel drugs targeting urease. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13468876
title: Structural analyses and classification of novel isoniazid resistance coupled mutational landscapes in <i>Mycobacterium tuberculosis</i>: a combined molecular docking and MD simulation study

description: Drug resistance in <i>Mycobacterium tuberculosis</i> has become a major challenge to the current regime of treatment as well as to the containment of the disease globally. The molecular and genetic studies identified frequently occurring point mutations in the virulent protein such as KatG of <i>M. tuberculosis</i> resulted in the development of isoniazid tolerance in the pathogen. This study aims to analyze the structural basis of the disease mutations available in the literature as well as to predict novel alteration in the KatG which may cause similar deleterious effects. Around 15 experimentally derived mutations were included in this study and pathogenic mutational landscapes containing 60 site-specific alterations were predicted using the available <i>in silico</i> techniques. The effects of these mutations on the stability of the protein were studied and an exhaustive docking study was conducted for each classified perturbations, which identify the highest changes in the binding energies in p.Meth255Ile among experimental and p.Ala222Arg in computationally predicted mutations. Furthermore, the structural effects on these substitutions were analyzed using the principles of molecular dynamic simulations each for a 100 ns time scale, which validated the interaction studies. The outcome of this study may enable the identification of the novel drug resistance-associated point mutations which were not previously reported and may contribute significantly in a variety of experimental studies as well as facilitate the process of drug design and discovery. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13468876.v1
title: Structural analyses and classification of novel isoniazid resistance coupled mutational landscapes in <i>Mycobacterium tuberculosis</i>: a combined molecular docking and MD simulation study

description: Drug resistance in <i>Mycobacterium tuberculosis</i> has become a major challenge to the current regime of treatment as well as to the containment of the disease globally. The molecular and genetic studies identified frequently occurring point mutations in the virulent protein such as KatG of <i>M. tuberculosis</i> resulted in the development of isoniazid tolerance in the pathogen. This study aims to analyze the structural basis of the disease mutations available in the literature as well as to predict novel alteration in the KatG which may cause similar deleterious effects. Around 15 experimentally derived mutations were included in this study and pathogenic mutational landscapes containing 60 site-specific alterations were predicted using the available <i>in silico</i> techniques. The effects of these mutations on the stability of the protein were studied and an exhaustive docking study was conducted for each classified perturbations, which identify the highest changes in the binding energies in p.Meth255Ile among experimental and p.Ala222Arg in computationally predicted mutations. Furthermore, the structural effects on these substitutions were analyzed using the principles of molecular dynamic simulations each for a 100 ns time scale, which validated the interaction studies. The outcome of this study may enable the identification of the novel drug resistance-associated point mutations which were not previously reported and may contribute significantly in a variety of experimental studies as well as facilitate the process of drug design and discovery. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13468872
title: Electronic properties investigation of human dihydrofolate reductase complexes with ligands

description: Despite the fact that there are already drugs for cancer, they still show strong toxicity to the human organism. That is why it is necessary to establish the factors affecting activity in order to develop new, more effective drugs aimed at tumor cells, minimizing harm to healthy cells. The present research is based on electronic properties calculation of the complexes using AlteQ approach. In the focus of this study are complexes of human dihydrofolate reductase (hDHFR) with a series of known inhibitors bound in the active site. Further, a statistical analysis was performed to establish the relationships between a myriad electronic characteristics and IC<sub>50</sub>. The change in total volume and the change of own electrons number of hydrogen atoms in their atomic basins are identified as the descriptors correlating the most with the hDHFR inhibition potency. Additionally, two lipophilic parts of protein (Thr56, Ser59, Ile60 and Ile7, Val8, Ala9) were found, which act as a key factor in decreasing bioactivity. The depth analysis of intermolecular interactions showed that the interactions between water molecules and ligand play a crucial role in hDHFR inhibition. Furthermore, the molecular dynamics simulations were used for deeper understanding of the structural inhibition, each for 50 ns time scale in explicit water conditions. Thus, the AlteQ approach made it possible to determine the factors influencing the activity and evaluate them not only qualitatively, but also quantitatively. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13468872.v1
title: Electronic properties investigation of human dihydrofolate reductase complexes with ligands

description: Despite the fact that there are already drugs for cancer, they still show strong toxicity to the human organism. That is why it is necessary to establish the factors affecting activity in order to develop new, more effective drugs aimed at tumor cells, minimizing harm to healthy cells. The present research is based on electronic properties calculation of the complexes using AlteQ approach. In the focus of this study are complexes of human dihydrofolate reductase (hDHFR) with a series of known inhibitors bound in the active site. Further, a statistical analysis was performed to establish the relationships between a myriad electronic characteristics and IC<sub>50</sub>. The change in total volume and the change of own electrons number of hydrogen atoms in their atomic basins are identified as the descriptors correlating the most with the hDHFR inhibition potency. Additionally, two lipophilic parts of protein (Thr56, Ser59, Ile60 and Ile7, Val8, Ala9) were found, which act as a key factor in decreasing bioactivity. The depth analysis of intermolecular interactions showed that the interactions between water molecules and ligand play a crucial role in hDHFR inhibition. Furthermore, the molecular dynamics simulations were used for deeper understanding of the structural inhibition, each for 50 ns time scale in explicit water conditions. Thus, the AlteQ approach made it possible to determine the factors influencing the activity and evaluate them not only qualitatively, but also quantitatively. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13181852.v1
title: Transcriptomic analysis of glioblastoma multiforme providing new insights into GPR17 signaling communication

description: Glioblastoma Multiforme (GBM) is one of the most aggressive malignant tumors in the central nervous system, which arises due to the failure or crosstalk in the signaling networks. GPR17, an orphan G protein-coupled receptor is anticipated to be associated with the biology of the GBM disease progression. In the present study, we have identified the differential expressions of around 170 genes along with GPR17 through the RNA-Seq analysis of 169 GBM samples. Coordinated expression patterns of all other gene products with this receptor were analysed using gene ontology and protein–protein interaction data. Several crucial signaling components and genes that play a significant role in tumor progression have been identified among which GPR17 was found to be significantly interacting with about 30 different pathways. High-throughput molecular docking of GPR17 by homology-based model against differentially expressed proteins, showed effective recognition and binding of PX, SH3, and Ig-like domains besides Gi protein. Pathways of PI3, Src, Ptdn, Ras, cytoplasmic tyrosine kinases, phospholipases, nexins and other proteins possessing these structural domains are identified as critical signaling components of the complex GBM signaling network. Our findings also provide a mechanistic insight of GPR17-T0510-3657 interaction, which potentially regulates the interaction of PX domain and helical mPTS recognition domain-containing proteins. Overall, our results demonstrate that GPR17 mediated signaling networks could be used as a therapeutic target for GBM. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13181852
title: Transcriptomic analysis of glioblastoma multiforme providing new insights into GPR17 signaling communication

description: Glioblastoma Multiforme (GBM) is one of the most aggressive malignant tumors in the central nervous system, which arises due to the failure or crosstalk in the signaling networks. GPR17, an orphan G protein-coupled receptor is anticipated to be associated with the biology of the GBM disease progression. In the present study, we have identified the differential expressions of around 170 genes along with GPR17 through the RNA-Seq analysis of 169 GBM samples. Coordinated expression patterns of all other gene products with this receptor were analysed using gene ontology and protein–protein interaction data. Several crucial signaling components and genes that play a significant role in tumor progression have been identified among which GPR17 was found to be significantly interacting with about 30 different pathways. High-throughput molecular docking of GPR17 by homology-based model against differentially expressed proteins, showed effective recognition and binding of PX, SH3, and Ig-like domains besides Gi protein. Pathways of PI3, Src, Ptdn, Ras, cytoplasmic tyrosine kinases, phospholipases, nexins and other proteins possessing these structural domains are identified as critical signaling components of the complex GBM signaling network. Our findings also provide a mechanistic insight of GPR17-T0510-3657 interaction, which potentially regulates the interaction of PX domain and helical mPTS recognition domain-containing proteins. Overall, our results demonstrate that GPR17 mediated signaling networks could be used as a therapeutic target for GBM. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13193942
title: Traditional medicinal plants against replication, maturation and transmission targets of SARS-CoV-2: computational investigation

description: COVID-19 is an infectious pandemic caused by the SARS-CoV-2 virus. The critical components of SARS-CoV-2 are the spike protein (S-protein) and the main protease (M<sup>pro</sup>). M<sup>pro</sup> is required for the maturation of the various polyproteins involved in replication and transcription. S-protein helps the SARS-CoV-2 to enter the host cells through the angiotensin-converting enzyme 2 (ACE2). Since ACE2 is required for the binding of SARS-CoV-2 on the host cells, ACE2 inhibitors and blockers have got wider attention, in addition to S-protein and M<sup>pro</sup> modulators as potential therapeutics for COVID-19. So far, no specific drugs have shown promising therapeutic potential against COVID-19. The current study was undertaken to evaluate the therapeutic potential of traditional medicinal plants against COVID-19. The bioactives from the medicinal plants, along with standard drugs, were screened for their binding against S-protein, M<sup>pro</sup> and ACE2 targets using molecular docking followed by molecular dynamics. Based on the higher binding affinity compared with standard drugs, bioactives were selected and further analyzed for their pharmacological properties such as drug-likeness, ADME/<i>T</i>-test, biological activities using <i>in silico</i> tools. The binding energies of several bioactives analyzed with target proteins were relatively comparable and even better than the standard drugs. Based on Lipinski factors and lower binding energies, seven bioactives were further analyzed for their pharmacological and biological characteristics. The selected bioactives were found to have lower toxicity with a higher GI absorption rate and potent anti-inflammatory and anti-viral activities against targets of COVID-19. Therefore, the bioactives from these medicinal plants can be further developed as phytopharmaceuticals for the effective treatment of COVID-19. Communicated by Ramaswamy H. Sarma Traditional medicinal plant bioactives can potentially inhibit SARS-CoV-2 protein targets. Computational investigation revealed that selected medicinal plant bioactives have the optimal pharmacological and biological activities against COVID-19.
DOI: 10.6084/m9.figshare.13193942.v1
title: Traditional medicinal plants against replication, maturation and transmission targets of SARS-CoV-2: computational investigation

description: COVID-19 is an infectious pandemic caused by the SARS-CoV-2 virus. The critical components of SARS-CoV-2 are the spike protein (S-protein) and the main protease (M<sup>pro</sup>). M<sup>pro</sup> is required for the maturation of the various polyproteins involved in replication and transcription. S-protein helps the SARS-CoV-2 to enter the host cells through the angiotensin-converting enzyme 2 (ACE2). Since ACE2 is required for the binding of SARS-CoV-2 on the host cells, ACE2 inhibitors and blockers have got wider attention, in addition to S-protein and M<sup>pro</sup> modulators as potential therapeutics for COVID-19. So far, no specific drugs have shown promising therapeutic potential against COVID-19. The current study was undertaken to evaluate the therapeutic potential of traditional medicinal plants against COVID-19. The bioactives from the medicinal plants, along with standard drugs, were screened for their binding against S-protein, M<sup>pro</sup> and ACE2 targets using molecular docking followed by molecular dynamics. Based on the higher binding affinity compared with standard drugs, bioactives were selected and further analyzed for their pharmacological properties such as drug-likeness, ADME/<i>T</i>-test, biological activities using <i>in silico</i> tools. The binding energies of several bioactives analyzed with target proteins were relatively comparable and even better than the standard drugs. Based on Lipinski factors and lower binding energies, seven bioactives were further analyzed for their pharmacological and biological characteristics. The selected bioactives were found to have lower toxicity with a higher GI absorption rate and potent anti-inflammatory and anti-viral activities against targets of COVID-19. Therefore, the bioactives from these medicinal plants can be further developed as phytopharmaceuticals for the effective treatment of COVID-19. Communicated by Ramaswamy H. Sarma Traditional medicinal plant bioactives can potentially inhibit SARS-CoV-2 protein targets. Computational investigation revealed that selected medicinal plant bioactives have the optimal pharmacological and biological activities against COVID-19.
DOI: 10.6084/m9.figshare.13181368
title: Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: <i>in silico</i> approaches

description: SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein–protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (<i>n</i> = 7489) in <i>hACE2</i> using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (–3.93 to −18.43), weak (–18.42 to −32.94), moderate (–32.94 to −47.44), strong (–47.44 to −61.95) and very strong (–61.95 to −76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (<i>p</i> &lt; 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.13181368.v1
title: Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: <i>in silico</i> approaches

description: SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein–protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (<i>n</i> = 7489) in <i>hACE2</i> using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (–3.93 to −18.43), weak (–18.42 to −32.94), moderate (–32.94 to −47.44), strong (–47.44 to −61.95) and very strong (–61.95 to −76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (<i>p</i> &lt; 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies. Communicated by Ramaswamy H. Sarma