Design of novel DABO derivatives as HIV-1 RT inhibitors using molecular docking, molecular dynamics simulations and ADMET properties

HIV-1 reverse transcriptase is an important target for developing effective anti-HIV-1 inhibitors. Different types of small molecules have been designed based on this target, showing different levels of inhibitory activity against various types of HIV-1 strains. The relationship between structure and activity of DABO derivatives was investigated by means of 3D-QSAR molecular model, molecular docking, molecular dynamics and ADMET properties. The statistical results of molecular models show that the CoMFA and CoMSIA models have good internal stability (CoMFA: q2 = 0.623, r2 = 0.946; CoMSIA: q2 = 0.668, r2 = 0.983) and external prediction ability (CoMFA: rpred2 = 0.961; CoMSIA: rpred2 = 0.961). In addition, molecular docking has explored the mechanism of action between small molecules and receptor proteins, and the results show that hydrogen bonding between amino acid Lys101 and small molecules can improve the affinity of ligands to receptor binding. A total of 12 novel molecules were designed and their activities were predicted based on the 3D-QSAR model and molecular docking results. The results showed that the designed molecules had higher predictive activity. Subsequently, 100 ns MD simulation and binding free energy verified the stability of molecular docking results. Finally, the pharmacokinetic properties of the novel designed molecule were verified by using ADMET to predict its properties. These results can provide reference for the design and development of novel and effective HIV-1 RT inhibitors, and provide new ideas for the design of subsequent drugs.Communicated by Ramaswamy H. Sarma.

In silico network pharmacology study on Glycyrrhiza glabra: Analyzing the immune-boosting phytochemical properties of Siddha medicinal plant against COVID-19

Immunosenescence is a pertinent factor in the mortality rate caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The changes in the immune system are strongly associated with age and provoke the deterioration of the individual's health. Traditional medical practices in ancient India effectively deal with COVID-19 by boosting natural immunity through medicinal plants. The anti-inflammatory and antiviral properties of Glycyrrhiza glabra are potent in fighting against COVID-19 and promote immunity boost against the severity of the infection. Athimadhura Chooranam, a polyherbal formulation containing Glycyrrhiza glabra as the main ingredient, is recommended as an antiviral Siddha herb by the Ministry of AYUSH. This paper is intended to identify the phytoconstituents of Glycyrrhiza glabra that are actively involved in preventing individuals from COVID-19 transmission. The modulated pathways, enrichment study, and drug-likeness are calculated from the target proteins of the phytoconstituents at the pharmacological activity (Pa) of more than 0.7. Liquiritigenin and Isoliquiritin, the natural compounds in Glycyrrhiza glabra, belong to the flavonoid class and exhibit ameliorative effects against COVID-19. The latter compound displays a higher protein interaction to a maximum of six, out of which HMOX1, PLAU, and PGR are top-hub genes. ADMET screening further confirms the significance of the abovementioned components containing better drug-likeness. The molecular docking and molecular dynamics method identified liquiritigenin as a possible lead molecule capable of inhibiting the activity of the major protease protein of SARS-CoV-2. The findings emphasize the importance of in silico network pharmacological assessments in delivering cost-effective, time-bound clinical drugs.

Inhibition of SARS-CoV2 viral infection with natural antiviral plants constituents: An in-silico approach

Background and Objective

In 2019, a novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) was declared pandemic. Advancement in computational technology has provided rapid and cost-effective techniques to test the efficacy of newer therapeutic agents. This study evaluated some of the potent phytochemicals obtained from AYUSH (Ayurveda, Yoga, Naturopathy, Unani, Siddha, Sowa-Rigpa, and Homeopathy)-listed medicinal plants against SARS-CoV-2 proteins using computational techniques.

Materials and methods

The potential SARS-CoV-2 protein targets were utilized to study the ligand-protein binding characteristics. The bioactive agents were obtained from ashwagandha, liquorice, amla, neem, tinospora, pepper, and stevia. Ivermectin was utilized as a reference agent to compare its efficacy with phytochemicals.

Results

The computational analysis suggested that all the bioactive components from the selected plants possessed negative docking scores (ranging from -6.24 to -10.53). The phytoconstituents were well absorbed, distributed in the body except for the CNS, metabolized by liver enzymes, well cleared from the body, and well tolerated. The data suggest that AYUSH-recommended plants demonstrated therapeutic efficacy against SARS CoV-2 virus infection with significantly reduced toxicity.

Conclusion

The phytoconstituents were found to hinder the early stages of infection, such as absorption and penetration, while ivermectin prevented the passage of genetic material from the cytoplasm to the nucleus. Additional research involving living tissues and clinical trials are suggested to corroborate the computational findings.

An ensemble docking-based virtual screening according to different TRPV1 pore states toward identifying phytochemical activators

Identifying phytochemical activators for TRPV1 using ensemble-based virtual screening, machine learning, and MD simulation.

Prophylactic treatment of Glycyrrhiza glabra mitigates COVID-19 pathology through inhibition of pro-inflammatory cytokines in the hamster model and NETosis

Severe coronavirus disease (COVID-19) is accompanied by acute respiratory distress syndrome and pulmonary pathology, and is presented mostly with an inflammatory cytokine release, a dysregulated immune response, a skewed neutrophil/lymphocyte ratio, and a hypercoagulable state. Though vaccinations have proved effective in reducing the COVID-19-related mortality, the limitation of the use of vaccine against immunocompromised individuals, those with comorbidity, and emerging variants remains a concern. In the current study, we investigate for the first time the efficacy of the Glycyrrhiza glabra (GG) extract, a potent immunomodulator, against SARS-CoV-2 infection in hamsters. Prophylactic treatment with GG showed protection against loss in body weight and a 35%–40% decrease in lung viral load along with reduced lung pathology in the hamster model. Remarkably, GG reduced the mRNA expression of pro-inflammatory cytokines and plasminogen activator inhibitor-1 (PAI-1). In vitro, GG acted as a potent immunomodulator by reducing Th2 and Th17 differentiation and IL-4 and IL-17A cytokine production. In addition, GG also showed robust potential to suppress ROS, mtROS, and NET generation in a concentration-dependent manner in both human polymorphonuclear neutrophils (PMNs) and murine bone marrow-derived neutrophils (BMDNs). Taken together, we provide evidence for the protective efficacy of GG against COVID-19 and its putative mechanistic insight through its immunomodulatory properties. Our study provides the proof of concept for GG efficacy against SARS-CoV-2 using a hamster model and opens the path for further studies aimed at identifying the active ingredients of GG and its efficacy in COVID-19 clinical cases.

Multi-stage virtual screening of natural products against p38α mitogen-activated protein kinase: predictive modeling by machine learning, docking study and molecular dynamics simulation

p38α is a mitogen-activated protein kinase (MAPK), and the signaling pathways involved are closely related to the inflammation, apoptosis and differentiation of cells, which also makes it an attractive target for drug discovery. With the high efficiency and low cost, virtual screening technology is becoming an indispensable part of drug development. In this study, a novel multi-stage virtual screening method based on machine learning, molecular docking and molecular dynamics simulation was developed to identify p38α MAPK inhibitors from natural products in ZINC database, which improves the prediction accuracy by considering and utilizing both ligand and receptor information compared to any individual approach. Ultimately, we screened out two candidate inhibitors with acceptable ADMET properties (ZINC4260400 and ZINC8300300). Among the generated machine learning models, Random Forest (RF) and Support Vector Machine (SVM) performed better, with the area under the receiver operating characteristic curve (AUC) values of 0.932 and 0.931 on the test set, as well as 0.834 and 0.850 on the external validation set. In addition, the results of molecular docking and ADMET prediction showed that two compounds with appropriate pharmacokinetic properties had binding free energies less than −8.0 kcal/mol for the target protein, and the results of molecular dynamics simulations further confirmed that they were stable during the process of inhibition.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Disease-Ligand Identification Based on Flexible Neural Tree

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

Screening of indole derivatives as the potent anticancer agents on dihydrofolate reductase: pharmaco-informatics and molecular dynamics simulation

Dihydrofolate reductase (DHFR) is a ubiquitous cellular enzyme involved in the biosynthesis of nucleotide and protein precursors, thus, the inhibition of human DHFR can be a promising strategy in cancer treatment. The design of effective anticancer drugs is an urgent need today according to the high spread of cancer. The indole molecule with diverse mechanisms of action and anticancer properties is one of the efficient pharmacophores in drug design. Hence, a virtual library of indole derivatives as a scaffold was selected for designing safer and more effective anticancer drugs against DHFR in this work. All indole derivatives utilized in the library design were selected regarding appreciable tumor growth inhibition. Structure-activity relationship (SAR), docking energy, ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, and effective non-covalent interactions were used to identify potential anticancer with indole scaffold. Results showed a higher number of indole moieties provide a strong attachment to the DHFR binding pocket and therefore more effective anticancer activity. The indole scaffold in combination with dichlorobenzene improves DHFR inhibition whereas barbituric acid weakens inhibition activity. In the following to validate the docking results, Molecular dynamics (MD) simulation and molecular mechanics generalized-Born surface area (MM-GBSA) indicated the permanent stability of the selected ligands into the DHFR binding pocket and the key amino acids. Therefore, promising pharmacophores based on indole-DHFR interactions were discovered, and the outcome could be useful in guiding future in vitro and in vivo drug discovery in cancer medicine.Communicated by Ramaswamy H. Sarma.

Computational study of quinoline-based thiadiazole compounds as potential antileishmanial inhibitors

Leishmaniasis is a severe disease caused by protozoan parasites of the genus Leishmania and it is accountable for sizable morbidity and mortality worldwide.

Natural Products for the Prevention and Control of the COVID-19 Pandemic: Sustainable Bioresources

Background: The world has been unprecedentedly hit by a global pandemic which broke the record of deadly pandemics that faced humanity ever since its existence. Even kids are well-versed in the terminologies and basics of the SARS-CoV-2 virus and COVID-19 now. The vaccination program has been successfully launched in various countries, given that the huge global population of concern is still far behind to be vaccinated. Furthermore, the scarcity of any potential drug against the COVID-19-causing virus forces scientists and clinicians to search for alternative and complementary medicines on a war-footing basis. Aims and Objectives: The present review aims to cover and analyze the etiology and epidemiology of COVID-19, the role of intestinal microbiota and pro-inflammatory markers, and most importantly, the natural products to combat this deadly SARS-CoV-2 virus. Methods: A primary literature search was conducted through PubMed and Google Scholar using relevant keywords. Natural products were searched from January 2020 to November 2020. No timeline limit has been imposed on the search for the biological sources of those phytochemicals. Interactive mapping has been done to analyze the multi-modal and multi-target sources. Results and Discussion: The intestinal microbiota and the pro-inflammatory markers that can serve the prognosis, diagnosis, and treatment of COVID-19 were discussed. The literature search resulted in yielding 70 phytochemicals and ten polyherbal formulations which were scientifically analyzed against the SARS-CoV-2 virus and its targets and found significant. Retrospective analyses led to provide information about 165 biological sources that can also be screened if not done earlier. Conclusion: The interactive analysis mapping of biological sources with phytochemicals and targets as well as that of phytochemical class with phytochemicals and COVID-19 targets yielded insights into the multitarget and multimodal evidence-based complementary medicines.