Searching possible SARS-CoV-2 main protease inhibitors in constituents from herbal medicines using in silico studies.

The largest threat to civilization since the Second World War is the spread of the new coronavirus disease (COVID-19). Therefore, there is an urgent need for innovative therapeutic medicines to treat COVID-19. Reusing bio-actives is a workable and efficient strategy in the battle against new epidemics because the process of developing new drugs is time-consuming. This research aimed to identify which herbal remedies had the highest affinity for the receptor and assess a variety of them for potential targets to suppress the SARS-CoV-2 Mpro. The use of AutoDock Vina for structure-based virtual screening was done first due to the importance of protein interactions in the development of drugs. Molecular docking was used in the comparative study to assess 89 different chemicals from medicinal herbs. To anticipate their effectiveness against the primary protease of SARS-CoV-2, more analysis was done on the ADMET profile, drug-likeness, and Lipinski's rule of five. The next step involved three replicas of 100 ns-long molecular dynamics simulations on the potential candidates, which were preceded by calculations of the binding free energy of MM-GBSA. The outcomes showed that Achyrodimer A, Cinchonain Ib, Symphonone F, and Lupeol acetate all performed well and had the highest 6LU7 binding affinities. Using RMSD, RMSF, and protein-ligand interactions, the stability of the protein-ligand complex was assessed. The studies indicate that bioactive substances obtained from herbal medicines may function as a COVID-19 therapeutic agent, necessitating additional wet lab research to confirm their therapeutic potential, efficacy, and pharmacological capacity against the condition.Communicated by Ramaswamy H. Sarma.

Identification of natural diterpenes isolated from Azorella species targeting dispersin B using in silico approaches.

CONTEXT: A bacterial biofilm is a cluster of bacterial cells embedded in a self-produced matrix of extracellular polymeric substances such as DNA, proteins, and polysaccharides. Several diseases have been reported to cause by bacterial biofilms, and difficulties in treating these infections are of concern. This work aimed to identify the inhibitor with the highest binding affinity for the receptor protein by screening various inhibitors obtained from Azorella species for a potential target to inhibit dispersin B. This work shows that azorellolide has the highest binding affinity (- 8.2 kcal/mol) among the compounds tested, followed by dyhydroazorellolide, mulinone A, and 7-acetoxy-mulin-9,12-diene which all had a binding affinity of - 8.0 kcal/mol. To the best of our knowledge, this is the first study to evaluate and contrast several diterpene compounds as antibacterial biofilm chemicals. METHODS: Here, molecular modelling techniques tested 49 diterpene compounds of Azorella and six FDA-approved antibiotics medicines for antibiofilm activity. Since protein-like interactions are crucial in drug discovery, AutoDock Vina was initially employed to carry out structure-based virtual screening. The drug-likeness and ADMET properties of the chosen compounds were examined to assess the antibiofilm activity further. Lipinski's rule of five was then applied to determine the antibiofilm activity. Then, molecular electrostatic potential was used to determine the relative polarity of a molecule using the Gaussian 09 package and GaussView 5.08. Following three replica molecular dynamic simulations (using the Schrodinger program, Desmond 2019-4 package) that each lasted 100 ns on the promising candidates, binding free energy was estimated using MM-GBSA. Structural visualisation was used to test the binding affinity of each compound to the crystal structure of dispersin B protein (PDB: 1YHT), a well-known antibiofilm compound.

Discovery of potential mTOR inhibitors from Cichorium intybus to find new candidate drugs targeting the pathological protein related to the breast cancer: an integrated computational approach.

Breast cancer is the most common malignancy among women. It is a complex condition with many subtypes based on the hormone receptor. The mammalian target of the rapamycin (mTOR) pathway regulates cell survival, metabolism, growth, and protein synthesis in response to upstream signals in both normal physiological and pathological situations, primarily in cancer. The objective of this study was to screen for a potential target to inhibit the mTOR using a variety of inhibitors derived from Cichorium intybus and to identify the one with the highest binding affinity for the receptor protein. Initially, AutoDock Vina was used to perform structure-based virtual screening, as protein-like interactions are critical in drug development. For the comparative analysis, 110 components of Cichorium intybus were employed and ten FDA-approved anticancer medicines, including everolimus, an mTOR inhibitor. Further, the drug-likeness and ADMET properties were investigated to evaluate the anti-breast cancer activity by applying Lipinski's rule of five to the selected molecules. The promising candidates were then subjected to three replica molecular dynamics simulations run for 100 ns, followed by binding free energy estimation using MM-GBSA. The data were analyzed using root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and protein-ligand interactions to determine the stability of the protein-ligand complex. Based on the results, taraxerone (98) revealed optimum binding affinities with mTOR, followed by stigmasterol (110) and rutin (104), which compared favorably to the control compounds. Subsequently, bioactive compounds derived from Cichorium intybus may serve as lead molecules for developing potent and effective mTOR inhibitors to treat breast cancer.

Screening the possible anti-cancer constituents of Hibiscus rosa-sinensis flower to address mammalian target of rapamycin: an in silico molecular docking, HYDE scoring, dynamic studies, and pharmacokinetic prediction.

One of the most common malignancies diagnosed and the leading cause of death for cancer-stricken women globally is breast cancer. The molecular subtype affects therapy options because it is a complex disorder with multiple subtypes. By concentrating on receptor activation, mTOR (mammalian target of rapamycin) can be employed as a therapeutic target. The goal of this work was to screen a number of inhibitors produced from Hibiscus rosa-sinensis for possible target to inhibit the mTOR and to determine which has the greatest affinity for the receptor. Primarily, the ionization states of the chosen compounds were predicted using the ChemAxon web platform, and their pKa values were estimated. Given the significance of interactions between proteins in the development of drugs, structure-based virtual screening was done using AutoDock Vina. Approximately 120 Hibiscus components and ten approved anti-cancer drugs, including the mTOR inhibitor everolimus, were used in the comparative analysis. By using Lipinski's rule of five to the chosen compounds, the ADMET profile and drug-likeness characteristics were further examined to assess the anti-breast cancer activity. The compounds with the highest ranked binding poses were loaded using the SeeSAR tool and the HYDE scoring to give interactive, desolvation, and visual ΔG estimation for ligand binding affinity assessment. Following, the prospective candidates underwent three replicas of 100 ns long molecular dynamics simulations, preceded with MM-GBSA binding free energy calculation. The stability of the protein-ligand complex was determined using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interactions. The results demonstrated that the best mTOR binding affinities were found for stigmastadienol (107), lupeol (66), and taraxasterol acetate (111), which all performed well in comparison to the control compounds. Thus, bioactive compounds isolated from Hibiscus rosa-sinensis could serve as lead molecules for the creation of potent and effective mTOR inhibitors for the breast cancer therapy.

In silico molecular docking and dynamic simulation of eugenol compounds against breast cancer.

Breast cancer is one of the most severe problems, and it is the primary cause of cancer-related death in females worldwide. The adverse effects and therapeutic resistance development are among the most potent clinical issues for potent medications for breast cancer treatment. The eugenol molecules have a significant affinity for breast cancer receptors. The aim of the study has been on the eugenol compounds, which has potent actions on Erα, PR, EGFR, CDK2, mTOR, ERBB2, c-Src, HSP90, and chemokines receptors inhibition. Initially, the drug-likeness property was examined to evaluate the anti-breast cancer activity by applying Lipinski's rule of five on 120 eugenol molecules. Further, structure-based virtual screening was performed via molecular docking, as protein-like interactions play a vital role in drug development. The 3D structure of the receptors has been acquired from the protein data bank and is docked with 87 3D PubChem and ZINC structures of eugenol compounds, and five FDA-approved anti-cancer drugs using AutoDock Vina. Then, the compounds were subjected to three replica molecular dynamic simulations run of 100 ns per system. The results were evaluated using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interactions to indicate protein-ligand complex stability. The results confirm that Eugenol cinnamaldehyde has the best docking score for breast cancer, followed by Aspirin eugenol ester and 4-Allyl-2-methoxyphenyl cinnamate. From the results obtained from in silico studies, we propose that the selected eugenols can be further investigated and evaluated for further lead optimization and drug development.