Computational analysis of drug like candidates against Neuraminidase of Human Influenza A virus subtypes

Discovery of novel potent drugs for influenza by inhibiting the vital function of neuraminidase via fragment-based drug design (FBDD) and molecular dynamics simulation strategies

The current work describes a fragment linking methodology to generate new neuraminidase inhibitors. A total number of 28,977 fragments from Zinc 20 have been obtained and screened for neuraminidase receptor affinity. Using Schrödinger software, the highest-scoring 270 fragment hits (with scores greater than -7.6) were subjected to fragment combining to create 100 new molecules. These 100 novel compounds were studied using XP docking to evaluate the molecular interaction modes and their binding affinity to neuraminidase receptor. The top ten molecules were selected, for ADMET, drug-likeness features. Based on these characteristics, the best four developed molecules and Zanamivir were submitted to a molecular dynamics simulation investigation to estimate their dynamics within the neuraminidase receptor using Gromacs software. All MD simulation findings show that the generated complexes are very stable when compared to the clinical inhibitor (Zanamivir). In addition, the four designed neuraminidase inhibitors formed very stable complexes with neuraminidase receptor (with total binding energies ranging from -83.50 to -107.85 Kj/mol) according to the total binding energy calculated by MM-PBSA. For the objective of developing new influenza medications, these novel molecules have the potential to be further evaluated in vitro and in vivo for influenza drug discovery.Communicated by Ramaswamy H. Sarma.

In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR (R train 2 =0.8414, Q2 = 0.7680) and GFA-ANN (R train ​ 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES (R train ​ 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA (R train 2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

The effects of mutation on the drug binding affinity of Neuraminidase: case study of Capsaicin using steered molecular dynamics simulation

The influenza virus is an important respiratory pathogen that causes many incidences of diseases and even death each year. One of the primary factors of this virus is the Neuraminidase surface protein, which causes the virus to leave the host cell and spread to new target cells. The main antiviral medication for influenza is designed as a protein inhibitor ligand that prevents further spread of the disease, and eventually relieves the emerged symptoms. The effectiveness of such inhibitory drugs is highly associated with their binding affinity. In this paper, the binding affinity of an herbal ligand of Capsaicin bound to Neuraminidase of the influenza virus is investigated using steered molecular dynamics (SMD) simulation. Since mutations of the virus directly impact the binding affinity of the inhibitory drugs, different mutations were generated by using Mutagenesis module. The rapid spread of infection during the avian influenza A/H5N1 epidemic has raised concerns about far more dangerous consequences if the virus becomes resistant to current drugs. Currently, oseltamivir (Tamiflu), zanamivir (Relenza), pramivir (Rapivab), and laninamivir (Inavir) are increasingly used to treat the flu. However, with the rapid evolution of the virus, some drug-resistant strains are emerging. Therefore, it is very important to seek alternative therapies and identify the roots of drug resistance. Obtained results demonstrated a reduced binding affinity for the applied mutations. This reduction in binding affinity will cause the virus mutation to become resistant to the drug, which will spread the disease and make it more difficult to treat. From a molecular prospect, this decrease in binding affinity is due to the loss of a number of effective bonds between the ligand and the receptor, which occurs with mutations of the wild-type (WT) species. The results of the present study can be used in the rational design of novel drugs that are compatible with specific mutations.

Role of Probiotics in the Management of COVID-19: A Computational Perspective

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.