cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24186799
title: Network pharmacology and molecular docking study-based approach to explore mechanism of benzimidazole-based anthelmintics for the treatment of lung cancer

description: Emerging studies have reported the potential anticancer activity of benzimidazole-based anthelmintics (BBA) against lung cancer (LC). However, mechanism underlying the anticancer activity of BBA is unclear. Therefore, in the current study, network pharmacology and molecular docking-based approach were used to explore the potential molecular mechanism for the treatment of LC. The potential targets for BBA were obtained from multiple databases including SwissTargetPrediction, Drug Bank, Therapeutic Target Database, and Comparative Toxicogenomics Database while LC targets were collected from DisGeNet gene discovery platform, Integrated Genomic Database of NSCLC, Catalogue of Somatic Mutations in Cancer and Online Mendelian Inheritance in Man database. Protein-protein interaction (PPI) diagram of common targets was constructed using STRING online platform. Topological analysis was performed using Cytoscape and gene enrichment analysis was conducted using FunRich software. Highest degree targets were then confirmed using molecular docking and molecular dynamics simulations. The BBA were prioritized according to their S scores, with ricobendazole ranking highest followed by flubendazole, fenbendazole, mebendazole, triclabendazole, albendazole, oxibendazole, parbendazole, thiabendazole and oxfendazole. The potential targets of BBA identified using topological analysis and molecular docking were found to be CCND1 (cyclin D1), EGFR (Epidermal Growth Factor Receptor), ERBB2 (Erb-B2 Receptor Tyrosine Kinase 2/CD340), PTGS2 (Prostaglandin-endoperoxide synthase 2), and SRC (Proto-oncogene tyrosine-protein kinase). Furthermore, molecular dynamics confirmed that CCND1 and EGFR are the potential targets of ricobendazole for the treatment of LC. BBA can be further explored as a therapeutic strategy for the treatment of lung cancer under <i>in vitro</i> and <i>in vivo</i> studies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24186799.v1
title: Network pharmacology and molecular docking study-based approach to explore mechanism of benzimidazole-based anthelmintics for the treatment of lung cancer

description: Emerging studies have reported the potential anticancer activity of benzimidazole-based anthelmintics (BBA) against lung cancer (LC). However, mechanism underlying the anticancer activity of BBA is unclear. Therefore, in the current study, network pharmacology and molecular docking-based approach were used to explore the potential molecular mechanism for the treatment of LC. The potential targets for BBA were obtained from multiple databases including SwissTargetPrediction, Drug Bank, Therapeutic Target Database, and Comparative Toxicogenomics Database while LC targets were collected from DisGeNet gene discovery platform, Integrated Genomic Database of NSCLC, Catalogue of Somatic Mutations in Cancer and Online Mendelian Inheritance in Man database. Protein-protein interaction (PPI) diagram of common targets was constructed using STRING online platform. Topological analysis was performed using Cytoscape and gene enrichment analysis was conducted using FunRich software. Highest degree targets were then confirmed using molecular docking and molecular dynamics simulations. The BBA were prioritized according to their S scores, with ricobendazole ranking highest followed by flubendazole, fenbendazole, mebendazole, triclabendazole, albendazole, oxibendazole, parbendazole, thiabendazole and oxfendazole. The potential targets of BBA identified using topological analysis and molecular docking were found to be CCND1 (cyclin D1), EGFR (Epidermal Growth Factor Receptor), ERBB2 (Erb-B2 Receptor Tyrosine Kinase 2/CD340), PTGS2 (Prostaglandin-endoperoxide synthase 2), and SRC (Proto-oncogene tyrosine-protein kinase). Furthermore, molecular dynamics confirmed that CCND1 and EGFR are the potential targets of ricobendazole for the treatment of LC. BBA can be further explored as a therapeutic strategy for the treatment of lung cancer under <i>in vitro</i> and <i>in vivo</i> studies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.27637930.v1
title: Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: <i>in silico</i> and immunoinformatics approach

description: The merger of COVID-19 and seasonal influenza infections is considered a potentially serious threat to public health. These two viral agents can cause extensive and severe lower and upper respiratory tract infections with lung damage with host factors. Today, the development of vaccination has been shown to reduce the risk of hospitalization and mortality from the COVID-19 virus and influenza epidemics. Therefore, this study contributes to an immunoinformatics approach to producing a vaccine that can elicit strong and specific immune responses against COVID-19 and influenza A and B viruses. The NCBI, GISAID, and Uniprot databases were used to retrieve sequences. Linear B cell, Cytotoxic T lymphocyte, and Helper T lymphocyte epitopes were predicted using the online servers. Population coverage of MHC I epitopes worldwide for SARS-CoV-2, Influenza virus H3N2, H3N2, and Yamagata/Victoria were 99.93%, 68.67%, 68.38%, and 85.45%, respectively. Candidate epitopes were linked by GGGGS, GPGPG, and KK linkers. Different epitopes were permutated several times to form different peptides and then screened for antigenicity, allergenicity, and toxicity. The vaccine construct was analyzed for physicochemical properties, conformational B-cell epitopes, interaction with Toll-like receptors, and IFN-gamma-induced. Immune stimulation response of final construct was evaluated using C-IMMSIM. Eventually, the final construct sequence was codon-optimized for <i>Escherichia coli</i> K12 and <i>Homo sapiens</i> to design a multi-epitope vaccine and mRNA vaccine. In conclusion, due to the variable nature of SARS-CoV-2 and influenza proteins, the design of a multi-epitope vaccine can protect against all their standard variants, but laboratory validation is required. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24139647
title: Omics data analysis reveals common molecular basis of small cell lung cancer and COVID-19

description: The impact of COVID-19 infection on individuals with small cell lung cancer (SCLC) poses a serious threat. Unfortunately, the molecular basis of this severe comorbidity has yet to be elucidated. The present study addresses this gap utilizing publicly available omics data of COVID-19 and SCLC to explore the key molecules and associated pathways involved in the convergence of these diseases. Findings revealed 402 genes, that exhibited differential expression patterns in SCLC patients and also play a pivotal role in COVID-19 pathogenesis. Subsequent functional enrichment analyses identified relevant ontologies and pathways that are significantly associated with these genes, revealing important insights into their potential biological, molecular and cellular functions. The protein-protein interaction network, constructed under four combinatorial topological assessments, highlighted SMAD3, CAV1, PIK3R1, and FN1 as the primary components to this comorbidity. Our results suggest that these components significantly regulate this cross-talk triggering the PI3K-AKT and TGF-β signaling pathways. Lastly, this study made a multi-step computational attempt and identified corylifol A and ginkgetin from natural sources that can potentially inhibit these components. Therefore, the outcomes of this study offer novel perspectives on the common molecular mechanisms underlying SCLC and COVID-19 and present future opportunities for drug development. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24139647.v1
title: Omics data analysis reveals common molecular basis of small cell lung cancer and COVID-19

description: The impact of COVID-19 infection on individuals with small cell lung cancer (SCLC) poses a serious threat. Unfortunately, the molecular basis of this severe comorbidity has yet to be elucidated. The present study addresses this gap utilizing publicly available omics data of COVID-19 and SCLC to explore the key molecules and associated pathways involved in the convergence of these diseases. Findings revealed 402 genes, that exhibited differential expression patterns in SCLC patients and also play a pivotal role in COVID-19 pathogenesis. Subsequent functional enrichment analyses identified relevant ontologies and pathways that are significantly associated with these genes, revealing important insights into their potential biological, molecular and cellular functions. The protein-protein interaction network, constructed under four combinatorial topological assessments, highlighted SMAD3, CAV1, PIK3R1, and FN1 as the primary components to this comorbidity. Our results suggest that these components significantly regulate this cross-talk triggering the PI3K-AKT and TGF-β signaling pathways. Lastly, this study made a multi-step computational attempt and identified corylifol A and ginkgetin from natural sources that can potentially inhibit these components. Therefore, the outcomes of this study offer novel perspectives on the common molecular mechanisms underlying SCLC and COVID-19 and present future opportunities for drug development. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24270983
title: Identification of potential phytochemical inhibitors for the proven six chronic obstructive pulmonary disease biomarkers – A molecular dynamics study

description: COPD is a multifactorial lung disease causing breathing difficulties in individuals and is becoming a major health concern worldwide. The unclear pathogenic mechanism and high mortality rate urge the development of drugs against this disease. In this study, around six COPD biomarkers identified from the preceding research through integrated gene expression analysis were taken as COPD target proteins. A total of 3307 phytocompounds were included in the COPD phytocompound library from 59 therapeutic plant sources. Furthermore, a crucial three-step virtual screening process (Absorption, Distribution, Metabolism, and Excretion, respiratory nontoxicity, less harmful and nontoxic compound category) was implemented to filter the potential drug-like phytocompounds. As a result, 160 phytocompounds were filtered with desired Absorption, Distribution, Metabolism, Excretion and Toxicity properties. The filtered 160 phytocompounds were docked against six COPD target proteins and the best-docked complexes were identified through visual inspection based on six unique parameters in SeeSAR. Furthermore, the best docked complexes were subjected to molecular dynamics simulation studies to assess the stability and conformational changes of the complexes. The presence of few amino acid residues within the crucial active, allosteric and catalytic sites of the six target proteins were revealed from the binding interaction types and major residual fluctuations from molecular docking and dynamic simulation studies respectively. This is indicative of the potential inhibitory activity of phytocompounds against the COPD biomarkers. Here we report the identified phytocompounds as a promising lead molecule for the six COPD biomarkers through <i>in silico</i> analysis opening new avenues in COPD pathogenesis and treatments. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24270983.v1
title: Identification of potential phytochemical inhibitors for the proven six chronic obstructive pulmonary disease biomarkers – A molecular dynamics study

description: COPD is a multifactorial lung disease causing breathing difficulties in individuals and is becoming a major health concern worldwide. The unclear pathogenic mechanism and high mortality rate urge the development of drugs against this disease. In this study, around six COPD biomarkers identified from the preceding research through integrated gene expression analysis were taken as COPD target proteins. A total of 3307 phytocompounds were included in the COPD phytocompound library from 59 therapeutic plant sources. Furthermore, a crucial three-step virtual screening process (Absorption, Distribution, Metabolism, and Excretion, respiratory nontoxicity, less harmful and nontoxic compound category) was implemented to filter the potential drug-like phytocompounds. As a result, 160 phytocompounds were filtered with desired Absorption, Distribution, Metabolism, Excretion and Toxicity properties. The filtered 160 phytocompounds were docked against six COPD target proteins and the best-docked complexes were identified through visual inspection based on six unique parameters in SeeSAR. Furthermore, the best docked complexes were subjected to molecular dynamics simulation studies to assess the stability and conformational changes of the complexes. The presence of few amino acid residues within the crucial active, allosteric and catalytic sites of the six target proteins were revealed from the binding interaction types and major residual fluctuations from molecular docking and dynamic simulation studies respectively. This is indicative of the potential inhibitory activity of phytocompounds against the COPD biomarkers. Here we report the identified phytocompounds as a promising lead molecule for the six COPD biomarkers through <i>in silico</i> analysis opening new avenues in COPD pathogenesis and treatments. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.27637933
title: XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm

description: Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (&gt;99%) and high sensitivity (&gt;99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost–PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.27637933.v1
title: XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm

description: Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (&gt;99%) and high sensitivity (&gt;99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost–PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24190852.v1
title: Lead identification against <i>Mycobacterium tuberculosis</i> using highly enriched active molecules against pantothenate synthetase

description: The Pantothenate synthetase (PS) from the <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>) holds a crucial role in the survival and robust proliferation of bacteria through its catalysis of coenzyme A and acyl carrier protein synthesis. The present study undertook the PS drug target in complex with a co-crystallized ligand and subjected it to docking and virtual screening approaches. The experimental design encompassed three discrete datasets: an active dataset featuring 136 compounds, an inactive dataset comprising 56 compounds, and a decoys dataset curated from the zinc library, comprising an extensive compilation of approximately 53,000 compounds. The compounds’ binding energies were observed to be in the range of −5 to ∼−14 kcal/mol. Additionally, binding energy results were further refined through Enrichment Factor analysis (EF). EF is a new statistical approach which uses the scores obtained from docking-based virtual screening and predicts the precision of the scoring function. Remarkably, the Enrichment Factor (EF) analysis produced exceptionally favorable outcomes, attaining an EF of approximately 49% within the uppermost 1% fraction of the compound distribution. Finally, a total of eight compounds, evenly distributed between the active dataset and the decoys dataset, emerged as potent inhibitors of the Pantothenate synthetase (PS) enzyme. The analysis of inhibition constants and binding energy revealed a notable correlation, with an r-squared value (<i>r</i><sup>2</sup>) of 0.912 between the two parameters. Furthermore, the shortlisted compounds were subjected to 100 ns MD simulation to determine their stability and dynamics behavior. The decoy compounds that have been identified, exhibiting properties comparable to the active compounds, are postulated as potential candidates for targeting the Pantothenate synthetase (PS) enzyme to treat <i>Mtb</i> infection. Nevertheless, in the pursuit of a comprehensive investigation, it is advisable to undertake additional experimental validation as a component of the subsequent study. Communicated by Ramaswamy H. Sarma.
DOI: 10.6084/m9.figshare.24160290
title: High-throughput virtual screening and molecular dynamics simulation reveals NPC170742 a novel chalconoid compound as a potential inhibitor of D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase in <i>Helicobacter pylori</i>

description: <i>Helicobacter pylori</i> is a gram negative spiral shaped bacteria that causes peptic ulcer and gastric cancer. It Is the sixth most prevalent cancer in the world and the third leading cause of cancer death. The increase in reported cases of <i>H. pylori</i> resistance to the drugs and antibiotics shows the need for the development of new and efficient drugs against the pathogen. In the present study, D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB), an enzyme involved in the biosynthesis of lipopolysaccharides that encourages bacterial adherence, self-aggregation and identifying the host cells was modelled and the active sites were predicted through POCASA which is an automated ligand binding site prediction server. Natural product activity and species source (NPASS) is a database of 96,481 natural compounds that were subjected to virtual screening workflow that includes Qikprop, Lipinski rule, filtering out reactive functional groups followed by high throughput virtual screening and the top 10 compounds were selected for further induced fit docking along with the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate. The compound NPC170742 (Alpha, Beta, 3,4,5,2′,4′,6′-Octahydroxy dihydrochalcone) showed higher affinity than the substrate, and both the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate and the compound NPC170742 were subjected to molecular dynamics simulation. The results exposed the compound NPC170742 could be a potential lead compound against the enzyme D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase of <i>H. pylori</i>. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.24160290.v1
title: High-throughput virtual screening and molecular dynamics simulation reveals NPC170742 a novel chalconoid compound as a potential inhibitor of D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase in <i>Helicobacter pylori</i>

description: <i>Helicobacter pylori</i> is a gram negative spiral shaped bacteria that causes peptic ulcer and gastric cancer. It Is the sixth most prevalent cancer in the world and the third leading cause of cancer death. The increase in reported cases of <i>H. pylori</i> resistance to the drugs and antibiotics shows the need for the development of new and efficient drugs against the pathogen. In the present study, D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB), an enzyme involved in the biosynthesis of lipopolysaccharides that encourages bacterial adherence, self-aggregation and identifying the host cells was modelled and the active sites were predicted through POCASA which is an automated ligand binding site prediction server. Natural product activity and species source (NPASS) is a database of 96,481 natural compounds that were subjected to virtual screening workflow that includes Qikprop, Lipinski rule, filtering out reactive functional groups followed by high throughput virtual screening and the top 10 compounds were selected for further induced fit docking along with the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate. The compound NPC170742 (Alpha, Beta, 3,4,5,2′,4′,6′-Octahydroxy dihydrochalcone) showed higher affinity than the substrate, and both the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate and the compound NPC170742 were subjected to molecular dynamics simulation. The results exposed the compound NPC170742 could be a potential lead compound against the enzyme D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase of <i>H. pylori</i>. Communicated by Ramaswamy H. Sarma