cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17714913
title: Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2

description: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronaviridae family, causing major destructions to human life directly and indirectly to the economic crisis around the world. Although there is significant reporting on the whole genome sequences and updated data for the different receptors are widely analyzed and screened to find a proper medication. Only a few bioassay experiments were completed against SARS-CoV-2 spike protein. We collected the compounds dataset from the PubChem Bioassay database having 1786 compounds and split it into the ratio of 80–20% for model training and testing purposes, respectively. Initially, we have created 11 models and validated them using a fivefold validation strategy. The hybrid consensus model shows a predictive accuracy of 95.5% for training and 94% for the test dataset. The model was applied to screen a virtual chemical library of Natural products of 2598 compounds. Our consensus model has successfully identified 75 compounds with an accuracy range of 70–100% as active compounds against SARS-CoV-2 RBD protein. The output of ML data (75 compounds) was taken for the molecular docking and dynamics simulation studies. In the complete analysis, the Epirubicin and Daunorubicin have shown the docking score of −9.937 and −9.812, respectively, and performed well in the molecular dynamics simulation studies. Also, Pirarubicin, an analogue of anthracycline, has widely been used due to its lower cardiotoxicity. It shows the docking score of −9.658, which also performed well during the complete analysis. Hence, after the following comprehensive pipeline-based study, these drugs can be further tested <i>in vivo</i> for further human utilization. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.17714913.v1
title: Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2

description: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronaviridae family, causing major destructions to human life directly and indirectly to the economic crisis around the world. Although there is significant reporting on the whole genome sequences and updated data for the different receptors are widely analyzed and screened to find a proper medication. Only a few bioassay experiments were completed against SARS-CoV-2 spike protein. We collected the compounds dataset from the PubChem Bioassay database having 1786 compounds and split it into the ratio of 80–20% for model training and testing purposes, respectively. Initially, we have created 11 models and validated them using a fivefold validation strategy. The hybrid consensus model shows a predictive accuracy of 95.5% for training and 94% for the test dataset. The model was applied to screen a virtual chemical library of Natural products of 2598 compounds. Our consensus model has successfully identified 75 compounds with an accuracy range of 70–100% as active compounds against SARS-CoV-2 RBD protein. The output of ML data (75 compounds) was taken for the molecular docking and dynamics simulation studies. In the complete analysis, the Epirubicin and Daunorubicin have shown the docking score of −9.937 and −9.812, respectively, and performed well in the molecular dynamics simulation studies. Also, Pirarubicin, an analogue of anthracycline, has widely been used due to its lower cardiotoxicity. It shows the docking score of −9.658, which also performed well during the complete analysis. Hence, after the following comprehensive pipeline-based study, these drugs can be further tested <i>in vivo</i> for further human utilization. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18697969
title: Virtual screening of natural products inspired in-house library to discover potential lead molecules against the SARS-CoV-2 main protease

description: SARS-CoV-2, a new coronavirus emerged in 2019, causing a global healthcare epidemic. Although a variety of drug targets have been identified as potential antiviral therapies, and effective candidate against SARS-CoV-2 remains elusive. One of the most promising targets for combating COVID-19 is SARS-CoV-2 Main protease (M<sup>pro</sup>, a protein responsible for viral replication. In this work, an in-house curated library was thoroughly evaluated for druggability against M<sup>pro</sup>. We identified four ligands (FG, Q5, P5, and PJ4) as potential inhibitors based on docking scores, predicted binding energies (MMGBSA), in silico ADME, and RMSD trajectory analysis. Among the selected ligands, FG, a natural product from Andrographis nallamalayana, exhibited the highest binding energy of −10.31 kcal/mol close to the docking score of clinical candidates Boceprevir and GC376. Other ligands (P5, natural product from cardiospermum halicacabum and two synthetic molecules Q5 and PJ4) have shown comparable docking scores ranging −7.65 kcal/mol to −7.18 kcal/mol. Interestingly, we found all four top ligands had Pi bond interaction with the main amino acid residues HIS41 and CYS145 (catalytic dyad), H-bonding interactions with GLU166, ARG188, and GLN189, and hydrophobic interactions with MET49 and MET165 in the binding site of M<sup>pro</sup>. According to the ADME analysis, Q5 and P5 are within the acceptable range of drug likeliness, compared to FG and PJ4. The interaction stability of the lead molecules with viral protease was verified using replicated MD simulations. Thus, the present study opens up the opportunity of developing drug candidates targeting SARS-CoV-2 main protease (M<sup>pro</sup>) to mitigate the disease. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18697965.v1
title: Identification of natural products against enoyl-acyl-carrier-protein reductase in malaria via combined pharmacophore modeling, molecular docking and simulations studies

description: <i>Plasmodium falciparum</i> is counted as one of the deadly species causing malaria. In that respect, enoyl acyl carrier protein reductase is recognized as one of the attractive druggable targets for the identification of antimalarials. Thus, from the structural proteome of ENR, common feature pharmacophores were constructed. To identify the representative models, all the hypotheses were subjected to validation methods, like, test set, enrichment factor, and Güner-Henry method, and the selected representative hypotheses were used to screen out the drug-like natural products. Further, the screened candidates were advanced to molecular docking calculations. Based on the docking score criteria and presence of essential interaction with Tyr277, seven candidates were shortlisted to conduct the HYDE and QSAR assessment. Further, the stability of these complexes was evaluated by employing molecular dynamics simulations, molecular mechanics‐generalized born surface area approach‐based free binding energy calculations with the residue-wise contribution of <i>Pf</i>ENR to the total binding free energy of the complex. On comparing the root mean square deviation, and fluctuation plots of the docked candidates with the reference, all the candidates displayed stable behavior, and the same outcome was depicted from the secondary structure element. However, from the free energy calculations, and residue-wise contribution conducted after dynamics, it was observed that out of seven, only five candidates sustain the binding with Tyr277 and cofactor of <i>Pf</i>ENR. Therefore, in the current work, the hybrid study of screening and stability lead to the identification of five structurally diverse candidates that can be employed for the design of novel antimalarials. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18697969.v1
title: Virtual screening of natural products inspired in-house library to discover potential lead molecules against the SARS-CoV-2 main protease

description: SARS-CoV-2, a new coronavirus emerged in 2019, causing a global healthcare epidemic. Although a variety of drug targets have been identified as potential antiviral therapies, and effective candidate against SARS-CoV-2 remains elusive. One of the most promising targets for combating COVID-19 is SARS-CoV-2 Main protease (M<sup>pro</sup>, a protein responsible for viral replication. In this work, an in-house curated library was thoroughly evaluated for druggability against M<sup>pro</sup>. We identified four ligands (FG, Q5, P5, and PJ4) as potential inhibitors based on docking scores, predicted binding energies (MMGBSA), in silico ADME, and RMSD trajectory analysis. Among the selected ligands, FG, a natural product from Andrographis nallamalayana, exhibited the highest binding energy of −10.31 kcal/mol close to the docking score of clinical candidates Boceprevir and GC376. Other ligands (P5, natural product from cardiospermum halicacabum and two synthetic molecules Q5 and PJ4) have shown comparable docking scores ranging −7.65 kcal/mol to −7.18 kcal/mol. Interestingly, we found all four top ligands had Pi bond interaction with the main amino acid residues HIS41 and CYS145 (catalytic dyad), H-bonding interactions with GLU166, ARG188, and GLN189, and hydrophobic interactions with MET49 and MET165 in the binding site of M<sup>pro</sup>. According to the ADME analysis, Q5 and P5 are within the acceptable range of drug likeliness, compared to FG and PJ4. The interaction stability of the lead molecules with viral protease was verified using replicated MD simulations. Thus, the present study opens up the opportunity of developing drug candidates targeting SARS-CoV-2 main protease (M<sup>pro</sup>) to mitigate the disease. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18697965
title: Identification of natural products against enoyl-acyl-carrier-protein reductase in malaria via combined pharmacophore modeling, molecular docking and simulations studies

description: <i>Plasmodium falciparum</i> is counted as one of the deadly species causing malaria. In that respect, enoyl acyl carrier protein reductase is recognized as one of the attractive druggable targets for the identification of antimalarials. Thus, from the structural proteome of ENR, common feature pharmacophores were constructed. To identify the representative models, all the hypotheses were subjected to validation methods, like, test set, enrichment factor, and Güner-Henry method, and the selected representative hypotheses were used to screen out the drug-like natural products. Further, the screened candidates were advanced to molecular docking calculations. Based on the docking score criteria and presence of essential interaction with Tyr277, seven candidates were shortlisted to conduct the HYDE and QSAR assessment. Further, the stability of these complexes was evaluated by employing molecular dynamics simulations, molecular mechanics‐generalized born surface area approach‐based free binding energy calculations with the residue-wise contribution of <i>Pf</i>ENR to the total binding free energy of the complex. On comparing the root mean square deviation, and fluctuation plots of the docked candidates with the reference, all the candidates displayed stable behavior, and the same outcome was depicted from the secondary structure element. However, from the free energy calculations, and residue-wise contribution conducted after dynamics, it was observed that out of seven, only five candidates sustain the binding with Tyr277 and cofactor of <i>Pf</i>ENR. Therefore, in the current work, the hybrid study of screening and stability lead to the identification of five structurally diverse candidates that can be employed for the design of novel antimalarials. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18980603.v1
title: Ligand pathways in estrogen-related receptors

description: The three subtypes of estrogen-related receptors ERR<i>α</i>, ERR<i>β</i>, and ERR<i>γ</i> are nuclear receptors mediating metabolic processes in various tissues such as the skeletal muscle, fat tissue, bone, and liver. Although the knowledge on their physiological ligands is limited, they have been implicated as drug targets for important indications including diabetes, cardiovascular diseases, and osteoporosis. As in other nuclear receptors, their ligand binding pocket is buried within the core of the receptor and connected to its surrounding by ligand pathways. Here, we investigated these pathways with conventional molecular dynamics as well as metadynamics simulations to reveal their distribution and their capability to facilitate ligand translocation. Dependent on the ERR subtype and the conformational state of the receptor, we could detect different pathways to be favored. Overall, the results suggested pathways IIIa and IIIb to be favored in the agonistic conformation, while antagonists preferred pathways I, II, and V. Along the pathways, the ligands passed different gating mechanisms of the receptor, including groups of protein residues as well as whole secondary structure elements, to leave the binding site. Even though these pathways are suggested to influence ligand specificity of the receptors and their elucidation might advance rational drug design, they have not yet been studied in ERRs. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.17917955.v2
title: The interaction of DNMT1 and DNMT3A epigenetic enzymes with phthalates and perfluoroalkyl substances: an <i>in silico</i> approach

description: The occurrence of long-lasting adverse effects of the environmental contaminants on human health is a current emerging issue. In particular, phthalates, poly- and perfluoroalkyl substances are proposed to trigger toxic effects as well as persistent changes on human development and metabolism by different mechanisms, including epigenetic modifications, although the specific underlying pathways are still unknown. This study contributes to identify the potential molecular initiating events of epigenetic-mediated adverse effects by an <i>in silico</i> approach, which combines molecular docking and molecular dynamics simulation. The approach probes the potential molecular interaction between several different phthalates and persistent organic pollutants and a specific class of epigenetic modulators, namely the DNA methyltransferases (DNMTs). The dynamics of interaction and the binding free energies of the ligand-DNMTs complexes demonstrated that pollutants can be classified into two main groups, according to the ligand-target complex stability: (1) a larger class of phthalates (DBP, DEHP, MBP and MEHP) acting as inhibitors of the enzymatic activity of the epigenetic targets and (2) a smaller class of phthalates (DMP and MMP) and perfluoroalkyl substances (PFOA and PFOS) which do not interact stably with the human DNMTs. These findings provide the first valuable <i>in silico</i> insights on the ability of these specific environmental pollutants to directly bind and inhibit a key class of epigenetic regulators. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18134166.v1
title: Search for potentially biased epidermal growth factor receptor (EGFR) inhibitors through pharmacophore modelling, molecular docking, and molecular dynamics (MD) simulation approaches

description: Epidermal growth factor receptor (EGFR), being one of the most crucial receptor in cancer therapy, has been selected as a potential target for the present study. Ligand-based pharmacophore model (<i>n</i> = 30, <i>R</i><sup>2</sup>=0.93 with root mean square deviation = 1.14, ΔCost = 144.27 and configuration cost = 21) was developed and validated with Fischer’s randomisation (at 95% confidence), test set (<i>n</i> = 225, <i>R</i><sup>2</sup> pred = 0.81), external data set (<i>n</i> = 13, <i>R</i><sup>2</sup> pred = 0.95) and decoy set (<i>n</i> = 70), further the model has been used to search for novel EGFR inhibitors. The validated model was used for virtual screening of zinc database. A pool of 115,948 candidate molecules was screened through the model. Subsequently, molecules having predicted IC<sub>50</sub>&lt;0.2 µM were selected for screening through drug-like properties filter. Based on pharmacokinetic profile (ADMET study), Lipinski’s rule of five and Veber’s rule, 62 molecules were shortlisted for molecular docking. Using consensus docking, five hit molecules were selected, which were further considered for molecular dynamics simulation. Additionally MM-GBSA analysis was carried which showed that affinity of hits towards the receptor of three compound mainly ZINC305, ZINC131796 and ZINC131785 were similar to the standard vanedtinib. The simulation, performed for 100 ns, revealed that two hit molecules, namely ZINC305 and ZINC131785, showing potential interactions at the ligand-binding domain of EGFR protein with good ligand–protein stability. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18980603
title: Ligand pathways in estrogen-related receptors

description: The three subtypes of estrogen-related receptors ERR<i>α</i>, ERR<i>β</i>, and ERR<i>γ</i> are nuclear receptors mediating metabolic processes in various tissues such as the skeletal muscle, fat tissue, bone, and liver. Although the knowledge on their physiological ligands is limited, they have been implicated as drug targets for important indications including diabetes, cardiovascular diseases, and osteoporosis. As in other nuclear receptors, their ligand binding pocket is buried within the core of the receptor and connected to its surrounding by ligand pathways. Here, we investigated these pathways with conventional molecular dynamics as well as metadynamics simulations to reveal their distribution and their capability to facilitate ligand translocation. Dependent on the ERR subtype and the conformational state of the receptor, we could detect different pathways to be favored. Overall, the results suggested pathways IIIa and IIIb to be favored in the agonistic conformation, while antagonists preferred pathways I, II, and V. Along the pathways, the ligands passed different gating mechanisms of the receptor, including groups of protein residues as well as whole secondary structure elements, to leave the binding site. Even though these pathways are suggested to influence ligand specificity of the receptors and their elucidation might advance rational drug design, they have not yet been studied in ERRs. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.17917955
title: The interaction of DNMT1 and DNMT3A epigenetic enzymes with phthalates and perfluoroalkyl substances: an <i>in silico</i> approach

description: The occurrence of long-lasting adverse effects of the environmental contaminants on human health is a current emerging issue. In particular, phthalates, poly- and perfluoroalkyl substances are proposed to trigger toxic effects as well as persistent changes on human development and metabolism by different mechanisms, including epigenetic modifications, although the specific underlying pathways are still unknown. This study contributes to identify the potential molecular initiating events of epigenetic-mediated adverse effects by an <i>in silico</i> approach, which combines molecular docking and molecular dynamics simulation. The approach probes the potential molecular interaction between several different phthalates and persistent organic pollutants and a specific class of epigenetic modulators, namely the DNA methyltransferases (DNMTs). The dynamics of interaction and the binding free energies of the ligand-DNMTs complexes demonstrated that pollutants can be classified into two main groups, according to the ligand-target complex stability: (1) a larger class of phthalates (DBP, DEHP, MBP and MEHP) acting as inhibitors of the enzymatic activity of the epigenetic targets and (2) a smaller class of phthalates (DMP and MMP) and perfluoroalkyl substances (PFOA and PFOS) which do not interact stably with the human DNMTs. These findings provide the first valuable <i>in silico</i> insights on the ability of these specific environmental pollutants to directly bind and inhibit a key class of epigenetic regulators. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18071130.v1
title: Charge reversal mutations in mesophilic-thermophilic orthologous protein pairs and their role in enhancing coulombic interaction energy

description: Proteins from thermophilic organisms are a matter of immense interest for decades because of its application in fields like de-novo protein design, thermostable variants of biocatalysts etc. Previous studies have found several sequence and structural adaptations related to thermal stability, while charge reversal study remains ignored. Here we address whether charge reversal mutations naturally occur in mesophilic-thermophilic/hyperthermophilic orthologous proteins. Do they contribute to thermal stability? Our systematic study on 1550 mesophilic-thermophilic/hyperthermophilic orthologous protein pairs with remarkable structural and topological similarity, shows gain in coulombic interaction energy in thermophilic/hyperthermophilic proteins at short range associated with partially exposed and buried charge reversal mutations, which may enhance thermostability. Our findings call forth its application in future protein engineering studies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.18134166
title: Search for potentially biased epidermal growth factor receptor (EGFR) inhibitors through pharmacophore modelling, molecular docking, and molecular dynamics (MD) simulation approaches

description: Epidermal growth factor receptor (EGFR), being one of the most crucial receptor in cancer therapy, has been selected as a potential target for the present study. Ligand-based pharmacophore model (<i>n</i> = 30, <i>R</i><sup>2</sup>=0.93 with root mean square deviation = 1.14, ΔCost = 144.27 and configuration cost = 21) was developed and validated with Fischer’s randomisation (at 95% confidence), test set (<i>n</i> = 225, <i>R</i><sup>2</sup> pred = 0.81), external data set (<i>n</i> = 13, <i>R</i><sup>2</sup> pred = 0.95) and decoy set (<i>n</i> = 70), further the model has been used to search for novel EGFR inhibitors. The validated model was used for virtual screening of zinc database. A pool of 115,948 candidate molecules was screened through the model. Subsequently, molecules having predicted IC<sub>50</sub>&lt;0.2 µM were selected for screening through drug-like properties filter. Based on pharmacokinetic profile (ADMET study), Lipinski’s rule of five and Veber’s rule, 62 molecules were shortlisted for molecular docking. Using consensus docking, five hit molecules were selected, which were further considered for molecular dynamics simulation. Additionally MM-GBSA analysis was carried which showed that affinity of hits towards the receptor of three compound mainly ZINC305, ZINC131796 and ZINC131785 were similar to the standard vanedtinib. The simulation, performed for 100 ns, revealed that two hit molecules, namely ZINC305 and ZINC131785, showing potential interactions at the ligand-binding domain of EGFR protein with good ligand–protein stability. Communicated by Ramaswamy H. Sarma