Aggregation of amyloid beta (Aβ) protein considered as one of contributors in development of Alzheimer's disease (AD). Several investigations have identified the importance of non-steroidal anti-inflammatory drugs (NSAIDs) as Aβ aggregation inhibitors. Here, we have examined the binding interactions of 24 NSAIDs belonging to eight different classes, with Aβ fibrils by exploiting docking and molecular dynamics studies. Minimum energy conformation of the docked NSAIDs were further optimized by density functional theory (DFT) employing Becke's three-parameter hybrid model, Lee-Yang-Parr (B3LYP) correlation functional method. DFT-based global reactivity descriptors, such as electron affinity, hardness, softness, chemical potential, electronegativity, and electrophilicity index were calculated to inspect the expediency of these descriptors for understanding the reactive nature and sites of the molecules. Few selected NSAID-Aβ fibrils complexes were subjected to molecular dynamics simulation to illustrate the stability of these complexes and the most prominent interactions during the simulated trajectory. All of the NSAIDs exhibited potential activity against Aβ fibrils in terms of predicted binding affinity. Sulindac was found to be the most active compound underscoring the contribution of indene methylene substitution, whereas acetaminophen was observed as least active NSAID. General structural requirements for interaction of NSAIDs with Aβ fibril include: aryl/heteroaryl aromatic moiety connected through a linker of 1-2 atoms to a distal aromatic group. Considering these structural requirements and electronic features, new potent agents can be designed and developed as potential Aβ fibril inhibitors for the treatment of AD.

Ginger (Zingiber officinale), despite being a common dietary adjunct that contributes to the taste and flavor of foods, is well known to contain a number of potentially bioactive phytochemicals having valuable medicinal properties. Although recent studies have emphasized their benefits in Alzheimer’s disease, limited information is available on the possible mechanism by which it renders anti-Alzheimer activity. Therefore, the present study seeks to employ molecular docking studies to investigate the binding interactions between active ginger components and various anti-Alzheimer drug targets. Lamarckian genetic algorithm methodology was employed for docking of 12 ligands with 13 different target proteins using AutoDock 4.2 program. Docking protocol was validated by re-docking of all native co-crystallized ligands into their original binding cavities exhibiting a strong correlation coefficient value (r2=0.931) between experimentally reported and docking predicted activities. This value suggests that the approach could be a promising computational tool to aid optimization of lead compounds obtained from ginger. Analysis of binding energy, predicted inhibition constant, and hydrophobic/hydrophilic interactions of ligands with target receptors revealed acetylcholinesterase as most promising, while c-Jun N-terminal kinase was recognized as the least favorable anti-Alzheimer’s drug target. Common structural requirements include hydrogen bond donor/acceptor area, hydrophobic domain, carbon spacer, and distal hydrophobic domain flanked by hydrogen bond donor/acceptor moieties. In addition, drug-likeness score and molecular properties responsible for a good pharmacokinetic profile were calculated by Osiris property explorer and Molinspiration online toolkit, respectively. None of the compounds violated Lipinski’s rule of five, making them potentially promising drug candidates for the treatment of Alzheimer’s disease.

Streptomyces smyrnaeus UKAQ_23, isolated from the mangrove-sediment, collected from Jubail,Saudi Arabia, exhibited substantial antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), including non-MRSA Gram-positive test bacteria. The novel isolate, under laboratory-scale conditions, produced the highest yield (561.3 ± 0.3 mg/kg fermented agar) of antimicrobial compounds in modified ISP-4 agar at pH 6.5, temperature 35 °C, inoculum 5% v/w, agar 1.5% w/v, and an incubation period of 7 days. The two major compounds, K1 and K2, were isolated from fermented medium and identified as Actinomycin X2 and Actinomycin D, respectively, based on their structural analysis. The antimicrobial screening showed that Actinomycin X2 had the highest antimicrobial activity compared to Actinomycin D, and the actinomycins-mixture (X2:D, 1:1, w/w) against MRSA and non-MRSA Gram-positive test bacteria, at 5 µg/disc concentrations. The MIC of Actinomycin X2 ranged from 1.56–12.5 µg/ml for non-MRSA and 3.125–12.5 µg/ml for MRSA test bacteria. An in-silico molecular docking demonstrated isoleucyl tRNA synthetase as the most-favored antimicrobial protein target for both actinomycins, X2 and D, while the penicillin-binding protein-1a, was the least-favorable target-protein. In conclusion, Streptomyces smyrnaeus UKAQ_23 emerged as a promising source of Actinomycin X2 with the potential to be scaled up for industrial production, which could benefit the pharmaceutical industry.

The spread of novel virus SARS‐CoV‐2, well known as COVID‐19 has become a major health issue currently which has turned up to a pandemic worldwide. The treatment recommendations are variable. Lack of appropriate medication has worsened the disease. On the basis of prior research, scientists are testing drugs based on medical therapies for SARS and MERS. Many drugs which include lopinavir, ritonavir and thalidomide are listed in the new recommendations. A topological index is a type of molecular descriptor that simply defines numerical values associated with the molecular structure of a compound that is effectively used in modeling many physicochemical properties in numerous quantitative structure–property/activity relationship (QSPR/QSAR) studies. In this study, several degree‐based and neighborhood degree sum‐based topological indices for several antiviral drugs were investigated by using a M ‐polynomial and neighborhood M ‐polynomial methods. In addition, a QSPR was established between the various topological indices and various physicochemical properties of these antiviral drugs along with remdesivir, chloroquine, hydroxychloroquine and theaflavin was performed in order to assess the efficacy of the calculated topological indices. The obtained results reveal that topological indices under study have strong correlation with the physicochemical characteristics of the potential antiviral drugs. A biological activity (pIC50) of these compounds were also investigated by using multiple linear regressions (MLR) analysis.

Ivermectin (IVM) is a broad-spectrum antiparasitic agent, having inhibitory potential against wide range of viral infections. It has also been found to hamper SARS-CoV-2 replication in vitro, and its precise mechanism of action against SARS-CoV-2 is yet to be understood. IVM is known to interact with host importin (IMP)α directly and averts interaction with IMPβ1, leading to the prevention of nuclear localization signal (NLS) recognition. Therefore, the current study seeks to employ molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) analysis and molecular dynamics simulation studies for decrypting the binding mode, key interacting residues as well as mechanistic insights on IVM interaction with 15 potential drug targets associated with COVID-19 as well as IMPα. Among all COVID-19 targets, the non-structural protein 9 (Nsp9) exhibited the strongest affinity to IVM showing −5.30 kcal/mol and −84.85 kcal/mol binding energies estimated by AutoDock Vina and MM-GBSA, respectively. However, moderate affinity was accounted for IMPα amounting −6.9 kcal/mol and −66.04 kcal/mol. Stability of the protein-ligand complexes of Nsp9-IVM and IMPα-IVM was ascertained by 100 ns trajectory of all-atom molecular dynamics simulation. Structural conformation of protein in complex with docked IVM exhibited stable root mean square deviation while root mean square fluctuations were also found to be consistent. In silico exploration of the potential targets and their interaction profile with IVM can assist experimental studies as well as designing of COVID-19 drugs. Communicated by Ramaswamy H. Sarma