Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in Momordica charantia and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from Momordica charantia using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of M. charantia. The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, in vitro and in vivo studies of the ligand against the diabetic enzymes is recommended.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Cutibacterium acnes is an opportunistic pathogen linked with acne vulgaris, affecting 80–90% of teenagers globally. On the leukocyte (WBCs) cell surface, the cell wall anchored sialidase in C. acnes virulence factor, catalysing the sialoconjugates into sialic acids and nutrients for C. acnes resulting in human skin inflammation. The clinical use of antibiotics for acne treatments has severe adverse effects, including microbial dysbiosis and resistance. Therefore, identifying inhibitors for primary virulence factors (Sialidase) was done using molecular docking of 1030 FDA-approved drugs. Initially, based on binding energies (ΔG), Naloxone (ZINC000000389747), Fenoldopam (ZINC000022116608), Labetalol (ZINC000000403010) and Thalitone (ZINC000000057255) were identified that showed high binding energies as −10.2, −10.1, −9.9 and −9.8 kcal/mol, respectively. In 2D analysis, these drugs also showed considerable structural conformer of hydrogen and hydrophobic interactions. Further, a 100 ns MD simulation study found the lowest deviation and fluctuations with various intermolecular interactions to stabilise the complexes. Out of 4, the Naloxone molecule showed robust, steady, and stable RMSD 0.23 ± 0.18 nm. Further, MMGBSA analysis supports MD results and found strong binding energy (ΔG) −29.71 ± 4.97 kcal/mol. In Comparative studies with Neu5Ac2en (native substrate) revealed naloxone has a higher affinity for sialidase. The PCA analysis showed that Naloxone and Thalitone were actively located on the active site, and other compounds were flickered. Our extensive computational and statistical report demonstrates that these FDA drugs can be validated as potential sialidase inhibitors. Communicated by Ramaswamy H. Sarma

Cutibacterium acnes is an opportunistic pathogen linked with acne vulgaris, affecting 80–90% of teenagers globally. On the leukocyte (WBCs) cell surface, the cell wall anchored sialidase in C. acnes virulence factor, catalysing the sialoconjugates into sialic acids and nutrients for C. acnes resulting in human skin inflammation. The clinical use of antibiotics for acne treatments has severe adverse effects, including microbial dysbiosis and resistance. Therefore, identifying inhibitors for primary virulence factors (Sialidase) was done using molecular docking of 1030 FDA-approved drugs. Initially, based on binding energies (ΔG), Naloxone (ZINC000000389747), Fenoldopam (ZINC000022116608), Labetalol (ZINC000000403010) and Thalitone (ZINC000000057255) were identified that showed high binding energies as −10.2, −10.1, −9.9 and −9.8 kcal/mol, respectively. In 2D analysis, these drugs also showed considerable structural conformer of hydrogen and hydrophobic interactions. Further, a 100 ns MD simulation study found the lowest deviation and fluctuations with various intermolecular interactions to stabilise the complexes. Out of 4, the Naloxone molecule showed robust, steady, and stable RMSD 0.23 ± 0.18 nm. Further, MMGBSA analysis supports MD results and found strong binding energy (ΔG) −29.71 ± 4.97 kcal/mol. In Comparative studies with Neu5Ac2en (native substrate) revealed naloxone has a higher affinity for sialidase. The PCA analysis showed that Naloxone and Thalitone were actively located on the active site, and other compounds were flickered. Our extensive computational and statistical report demonstrates that these FDA drugs can be validated as potential sialidase inhibitors. Communicated by Ramaswamy H. Sarma

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Type 2 diabetes accounts for the largest percentage of all diabetic cases worldwide. Cucurbitane-type triterpenes are mainly found in Momordica charantia and possess excellent pharmacological activities. This study was designed to identify cucurbitane-type triterpene from Momordica charantia using Liquid Chromatography-Mass Spectrometry (LC-MS) analysis, examine its anti-diabetic property with molecular docking against diabetes enzymes (alpha-amylase, alpha-glucosidase, dipeptidyl dipeptidase IV and peroxisome proliferator-activated receptor gamma). The stability and interactions of the docked complexes were investigated using molecular dynamics simulation, while the pharmacokinetic and toxicity profile of the ligand was examined using an ADMET server. (23E)-Cucurbita-5,23,25-triene-3,7-dione (CUB) was identified from the LC-MS profiling of the methanolic extract of M. charantia. The molecular docking studies showed that the identified phytochemical elicited good binding energy against all the target receptors. The RMSD and RMSF plots obtained from the 100 ns molecular dynamics simulation showed that the ligand was stable and established substantial interactions with the amino acid residues of the diabetes enzymes which were confirmed by the MM\GBSA computations. The pharmacokinetic and toxicity properties of the ligand showed it was safer as an anti-diabetic drug candidate. Extensive isolation, in vitro and in vivo studies of the ligand against the diabetic enzymes is recommended.

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 in vivo for further human utilization. Communicated by Ramaswamy H. Sarma

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 in vivo for further human utilization. Communicated by Ramaswamy H. Sarma

Supplemental material, sj-xlsx-1-npx-10.1177_1934578X221118549 for In silico Analysis of ACE2 Receptor to Find Potential Herbal Drugs in COVID-19 Associated Neurological Dysfunctions by Juan Hou, Adil Manzoor Bhat, Shaban Ahmad, Khalid Raza and Sahar Qazi in Natural Product Communications