In silico docking studies of bioactive natural plant products as putative DHFR antagonists

Unveiling the Efficacy of Sesquiterpenes from Marine Sponge Dactylospongia elegans in Inhibiting Dihydrofolate Reductase Using Docking and Molecular Dynamic Studies

Dihydrofolate reductase (DHFR) is a crucial enzyme that maintains the levels of 5,6,7,8-tetrahydrofolate (THF) required for the biological synthesis of the building blocks of DNA, RNA, and proteins. Over-activation of DHFR results in the progression of multiple pathological conditions such as cancer, bacterial infection, and inflammation. Therefore, DHFR inhibition plays a major role in treating these illnesses. Sesquiterpenes of various types are prime metabolites derived from the marine sponge Dactylospongia elegans and have demonstrated antitumor, anti-inflammation, and antibacterial capacities. Here, we investigated the in silico potential inhibitory effects of 87 D. elegans metabolites on DHFR and predicted their ADMET properties. Compounds were prepared computationally for molecular docking into the selected crystal structure of DHFR (PDB: 1KMV). The docking scores of metabolites 34, 28, and 44 were the highest among this series (gscore values of -12.431, -11.502, and -10.62 kcal/mol, respectively), even above the co-crystallized inhibitor SRI-9662 score (-10.432 kcal/mol). The binding affinity and protein stability of these top three scored compounds were further estimated using molecular dynamic simulation. Compounds 34, 28, and 44 revealed high binding affinity to the enzyme and could be possible leads for DHFR inhibitors; however, further in vitro and in vivo investigations are required to validate their potential.

A molecular dynamics simulation study of the ACE2 receptor with screened natural inhibitors to identify novel drug candidate against COVID-19

Background & Objectives

The massive outbreak of Novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has turned out to be a serious global health issue worldwide. Currently, no drugs or vaccines are available for the treatment of COVID-19. The current computational study was attempted to identify a novel therapeutic inhibitor against novel SARS-CoV-2 using in silico drug discovery pipeline.

Methods

In the present study, the human angiotensin-converting enzyme 2 (ACE2) receptor was the target for the designing of drugs against the deadly virus. The 3D structure of the receptor was modeled & validated using a Swiss-model, Procheck & Errat server. A molecular docking study was performed between a group of natural & synthetic compounds having proven anti-viral activity with ACE2 receptor using Autodock tool 1.5.6. The molecular dynamics simulation study was performed using Desmond v 12 to evaluate the stability and interaction of the ACE2 receptor with a ligand.

Results

Based on the lowest binding energy, confirmation, and H-bond interaction, cinnamic acid (−5.20 kcal/mol), thymoquinone (−4.71 kcal/mol), and andrographolide (Kalmegh) (−4.00 kcal/mol) were screened out showing strong binding affinity to the active site of ACE2 receptor. MD simulations suggest that cinnamic acid, thymoquinone, and andrographolide (Kalmegh) could efficiently activate the biological pathway without changing the conformation in the binding site of the ACE2 receptor. The bioactivity and drug-likeness properties of compounds show their better pharmacological property and safer to use.

Interpretation & Conclusions

The study concludes the high potential of cinnamic acid, thymoquinone, and andrographolide against the SARS-CoV-2 ACE2 receptor protein. Thus, the molecular docking and MD simulation study will aid in understanding the molecular interaction between ligand and receptor binding site, thereby leading to novel therapeutic intervention.

Herb-target virtual screening and network pharmacology for prediction of molecular mechanism of Danggui Beimu Kushen Wan for prostate cancer

Prostate cancer (PCa) is a cancer that occurs in the prostate with high morbidity and mortality. Danggui Beimu Kushen Wan (DBKW) is a classic formula for patients with difficult urination including PCa. This study aimed to investigate the molecular mechanisms of DBKW for PCa. We obtained DBKW compounds from our previous reviews. We identified potential targets for PCa from literature search, currently approved drugs and Open Targets database and filtered them by protein-protein interaction network analysis. We selected 26 targets to predict three cancer-related pathways. A total of 621 compounds were screened via molecular docking using PyRx and AutoDock Vina against 21 targets for PCa, producing 13041 docking results. The binding patterns and positions showed that a relatively small number of tight-binding compounds from DBKW were predicted to interact strongly and selectively with three targets. The top five high-binding-affinity compounds were selected to generate a network, indicating that compounds from all three herbs had high binding affinity against the 21 targets and may have potential biological activities with the targets. DBKW contains multi-targeting agents that could act on more than one pathway of PCa simultaneously. Further studies could focus on validating the computational results via experimental studies.

Inhibitory effect of a weight-loss Chinese herbal formula RCM-107 on pancreatic α-amylase activity: Enzymatic and in silico approaches

Reducing carbohydrates digestion by having a low glycaemic index (GI) foods has been linked to weight loss. Inhibiting related enzymes is an alternative way to decrease carbohydrate digestion. RCM-107 (Slimming Plus), an eight-herb formula that is modified from RCM-104, indicated significant weight-loss action in clinical trials. However, no published research has studied its mechanism of action on reducing carbohydrate absorption via suppressing the activities of porcine pancreatic alpha-amylase (PPA). In this paper, we used fluorescence PPA inhibition assay to investigate the inhibitory effects of RCM-107 and the individual herbs present in this herbal mixture on amylase activity. Subsequently, molecular docking predicted the key active compounds that may be responsible for the enzyme inhibition. According to our results, both the RCM-107 formula and several individual herbs displayed α-amylase inhibitory effects. Also, marginal synergistic effects of RCM-107 were detected. In addition, alisol B, (-)-epigallocatechin-3-gallate (EGCG) and plantagoside have been predicted as the key active compounds that may be responsible for the α-amylase inhibition effect of RCM-107 according to inter-residue contact analysis. Finally, Glu233, Gln63, His305, Asp300 and Tyr151 are predicted to be markers of important areas with which potential amylase inhibitors would interact. Therefore, our data has provided new knowledge on the mechanisms of action of the RCM-107 formula and its individual herbal ingredients for weight loss, in terms of decreasing carbohydrate digestion via the inhibition of pancreatic alpha-amylase.

The Potential of Secondary Metabolites from Plants as Drugs or Leads against Protozoan Neglected Diseases-Part III: In-Silico Molecular Docking Investigations

Malaria, leishmaniasis, Chagas disease, and human African trypanosomiasis continue to cause considerable suffering and death in developing countries. Current treatment options for these parasitic protozoal diseases generally have severe side effects, may be ineffective or unavailable, and resistance is emerging. There is a constant need to discover new chemotherapeutic agents for these parasitic infections, and natural products continue to serve as a potential source. This review presents molecular docking studies of potential phytochemicals that target key protein targets in Leishmania spp., Trypanosoma spp., and Plasmodium spp.

A multilayer screening approach toward the discovery of novel Pf-DHFR inhibitors

A small yet diverse xanthone library was build and computationally docked against wild type Pf-DHFR by Molegro Virtual Docker (MolDock). For analysis of results an integrated approach based on re-ranking, scaling (based on heavy atom counts), pose clustering and visual inspection was implemented. Standard methods such as self-docking (for docking), EF analysis, average rank determinations (for size normalization), and cluster quality indices (for pose clustering) were used for validation of results. Three compounds X5, X113A and X164B displayed contact footprints similar to the known inhibitors with good scores. Finally, 16 compounds were extracted from ZINC data base by similarity based screening, docking score and drug/lead likeness. Out of these 16 compounds, 11 displayed very close contact footprints to experimentally known inhibitors, indicating there potential utility in further drug discovery efforts.