Understanding the Xylooligosaccharides Utilization Mechanism of Lactobacillus brevis and Bifidobacterium adolescentis: Proteins Involved and Their Conformational Stabilities for Effectual Binding.

Xylooligosaccharides having various degrees of polymerization such as xylobiose, xylotriose, and xylotetraose positively affect human health by interacting with gut proteins. The present study aimed to identify proteins present in gut microflora, such as xylosidase, xylulokinase, etc., with the help of retrieved whole-genome annotations and find out the mechanistic interactions of those with the above substrates. The 3D structures of proteins, namely Endo-1,4-beta-xylanase B (XynB) from Lactobacillus brevis and beta-D-xylosidase (Xyl3) from Bifidobacterium adolescentis, were computationally predicted and validated with the help of various bioinformatics tools. Molecular docking studies identified the effectual binding of these proteins to the xylooligosaccharides, and the stabilities of the best-docked complexes were analyzed by molecular dynamic simulation. The present study demonstrated that XynB and Xyl3 showed better effectual binding toward Xylobiose with the binding energies of - 5.96 kcal/mol and - 4.2 kcal/mol, respectively. The interactions were stabilized by several hydrogen bonding having desolvation energy (- 6.59 and - 7.91). The conformational stabilities of the docked complexes were observed in the four selected complexes of XynB-xylotriose, XynB-xylotetraose, Xyl3-xylobiose, and Xyn3-xylotriose by MD simulations. This study showed that the interactions of these four complexes are stable, which means they have complex metabolic activities among each other. Extending these studies of understanding, the interaction between specific probiotics enzymes and their ligands can explore the detailed design of synbiotics in the future.

Structural insights on the interaction potential of natural leads against major protein targets of SARS-CoV-2: Molecular modelling, docking and dynamic simulation studies.

Though significant efforts are in progress for developing drugs and vaccines against COVID-19, limited therapeutic agents are available currently. Thus, it is essential to undertake COVID-19 research and to identify therapeutic interventions in which computational modeling and virtual screening of lead molecules provide significant insights. The present study aimed to predict the interaction potential of natural lead molecules against prospective protein targets of SARS-CoV-2 by molecular modeling, docking, and dynamic simulation. Based on the literature survey and database search, fourteen molecular targets were selected and the three targets which lack the native structures were computationally modeled. The drug-likeliness and pharmacokinetic features of ninety-two natural molecules were predicted. Four lead molecules with ideal drug-likeliness and pharmacokinetic properties were selected and docked against fourteen targets, and their binding energies were compared with the binding energy of the interaction between Chloroquine and Hydroxychloroquine to their usual targets. The stabilities of selected docked complexes were confirmed by MD simulation and energy calculations. Four natural molecules demonstrated profound binding to most of the prioritized targets, especially, Hyoscyamine and Tamaridone to spike glycoprotein and Rotiorinol-C and Scutifoliamide-A to replicase polyprotein-1ab main protease of SARS-CoV-2 showed better binding energy, conformational and dynamic stabilities compared to the binding energy of Chloroquine and its usual target glutathione-S-transferase. The aforementioned lead molecules can be used to develop novel therapeutic agents towards the protein targets of SARS-CoV-2, and the study provides significant insight for structure-based drug development against COVID-19.

Carbon fullerene acts as potential lead molecule against prospective molecular targets of biofilm-producing multidrug-resistant Acinetobacter baumanni and Pseudomonas aerugenosa: computational modeling and MD simulation studies.

This study aimed to screen putative drug targets associated with biofilm formation of multidrug-resistant Acinetobacter baumannii and Pseudomonas areugenosa and prioritize carbon nano-fullerene as potential lead molecule by structure-based virtual screening. Based on the functional role, 36 and 83 genes that are involved in biofilm formation of A. baumannii and P. areugenosa respectively were selected and metabolic network was computationally constructed. The genes that lack three-dimensional structures were predicted and validated. Carbon nano-fullerene selected as lead molecule and their drug-likeliness and pharmacokinetics properties were computationally predicted. The binding potential of carbon nano-fullerene toward selected drug targets was modeled and compared with the binding of conventional drugs, doripenem, and polymyxin-B with their usual targets. The stabilities of four best-docked complexes were confirmed by molecular dynamic (MD) simulation. This study suggested that selected genes demonstrated relevant interactions in the constructed metabolic pathways. Carbon fullerene exhibited significant binding abilities to most of the prioritized targets in comparison with the binding of last-resort antibiotics and their usual target. The four best ligand-receptor interactions predicted by molecular docking revealed that stability throughout MD simulation. Notably, carbon fullerene exhibited profound binding with outer membrane protein (OmpA) and ribonuclease-HII (rnhB) of A. baumannii and 2-heptyl-4(1H)-quinolone synthase (pqsBC) and chemotaxis protein (wspA) of P. aeruginosa. Thus, the current study suggested that carbon fullerene was probably used as potential lead molecules toward selected targets of A. baumannii and P. aeruginosa and the applied aspects probably scaled up to design promising lead molecules toward these pathogens. Communicated by Ramaswamy H. Sarma.

Structural and molecular basis of the interaction mechanism of selected drugs towards multiple targets of SARS-CoV-2 by molecular docking and dynamic simulation studies- deciphering the scope of repurposed drugs.

The repurposing of FDA approved drugs is presently receiving attention for COVID-19 drug discovery. Previous studies revealed the binding potential of several FDA-approved drugs towards specific targets of SARS-CoV-2; however, limited studies are focused on the structural and molecular basis of interaction of these drugs towards multiple targets of SARS-CoV-2. The present study aimed to predict the binding potential of six FDA drugs towards fifteen protein targets of SARS-CoV-2 and propose the structural and molecular basis of the interaction by molecular docking and dynamic simulation. Based on the literature survey, fifteen potential targets of SARS-CoV-2, and six FDA drugs (Chloroquine, Hydroxychloroquine, Favipiravir, Lopinavir, Remdesivir, and Ritonavir) were selected. The binding potential of individual drug towards the selected targets was predicted by molecular docking in comparison with the binding of the same drugs with their usual targets. The stabilities of the best-docked conformations were confirmed by molecular dynamic simulation and energy calculations. Among the selected drugs, Ritonavir and Lopinavir showed better binding towards the prioritized targets with minimum binding energy (kcal/mol), cluster-RMS, number of interacting residues, and stabilizing forces when compared with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, later drugs demonstrated better binding when compared to the binding with their usual targets. Remdesvir showed better binding to the prioritized targets in comparison with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, but showed lesser binding potential when compared to the interaction between Ritonavir and Lopinavir and the prioritized targets. The structural and molecular basis of interactions suggest that the FDA drugs can be repurposed towards multiple targets of SARS-CoV-2, and the present computational models provide insights on the scope of repurposed drugs against COVID-19.

Response regulator GacA and transcriptional activator RhlR proteins involved in biofilm formation of Pseudomonas aeruginosa are prospective targets for natural lead molecules: Computational modelling, molecular docking and dynamic simulation studies.

Pseudomonas aeruginosa has become a global concern due to its extreme resistance to most of the last resort antibiotics. Present study focuses on the screening of potential molecular targets involved in regulation of biofilm formation in P. aeruginosa and identification of potential natural lead molecules against these targets by molecular modelling, docking and simulation studies. Response regulator (GacA) and transcriptional activator (RhlR) involved in biofilm formation in P. aeruginosa were identified as molecular targets by metabolic pathway analysis and the three dimensional structures of these proteins were predicted by homology modelling and validated. By thorough literature survey, 78 lead molecules were screened and their pharmacokinetic profiles were determined and best two of them selected. The binding potential of selected lead molecules against GacA and RhlR were predicted by molecular docking and their binding energy was compared with the interaction of meropenem and its usual target penicillin binding protein-3. The stabilities of best docked complex were studied by molecular dynamic (MD) simulation. This study showed that Celastrol present in Celastrus paniculatus and Rotiorinol present in Chaetomium cupreum showed better binding affinities with GacA (binding energy -7.2 kcal/mol) and RhlR (binding energy -8.0 kcal/mol) respectively in comparison with the binding of Meropenem and its target (binding energy -6.2 kcal/mol). MD simulation studies showed that GacA-Celastrol and RhlR-Rotiorinol complexes demonstrated conformational stability throughout the simulation. This study highlights the application of GacA and RhlR as prospective targets and Celastrol and Rotiorinol are the potential lead molecules towards biofilm producing drug resistant P. aeruginosa.

Natural epiestriol-16 act as potential lead molecule against prospective molecular targets of multidrug resistant Acinetobacter baumannii-Insight from in silico modelling and in vitro investigations.

The current study aimed to identify putative drug targets of multidrug resistant Acinetobacter baumannii (MDRAb) and study the therapeutic potential of natural epiestriol-16 by computer aided virtual screening and in vitro studies. The clinical isolates (n = 5) showed extreme dug resistance to carbapenems and colistins (p ≤ .05). Computational screening suggested that out of 236 natural molecules selected, 06 leads were qualified for drug likeliness, pharmacokinetic features and one potential molecule namely natural epiestriol-16 (16b-Hydroxy-17a-estradiol) exhibited significant binding potential towards four prioritised drug targets in comparison with the binding of faropenem to their usual target. Natural epiestriol demonstrated profound binding to the outer membrane protein (Omp38), protein RecA (RecA), orotate phosphoribosyltransferase (PyrE) and orotidine 5'-phosphate decarboxylase (PyrF) with binding energy of -6.0, -7.3, -7.3 and -8.0 kcal/mol respectively. MD simulations suggested that 16-epiestriol-receptor complexes demonstrated stability throughout the simulation. The growth curve and time kill assays revealed that MDRAb showed resistance to faropenem and polymyxin-B and the pure epiestriol-16 showed significant inhibitory properties at a concentration of 200 µg/mL (p ≤ .5). Thus, natural epiestriol-16 can be used as potential inhibitor against the prioritised targets of MDRAb and this study provide insight for drug development against carbapenem and colistin resistant A. baumannii.

Mitogen activated protein kinase-1 and cell division control protein-42 are putative targets for the binding of novel natural lead molecules: a therapeutic intervention against Candida albicans.

Candida albicans, fungal yeast causes several lethal infections in immune-suppressed patients and recently emerged as drug-resistant pathogens worldwide. The present study aimed to screen putative drug targets of Candia albicans and to study the binding potential of novel natural lead compounds towards these targets by computational virtual screening and molecular dynamic (MD) simulation. Through extensive analysis of mitogen-activated protein kinase (MAPK) signalling pathways, mitogen-activated protein kinase-1 (HOG1) and cell division control protein-42 (CDC42) genes were prioritized as putative targets based on their virulent functions. The three-dimensional structures of these genes, not available in their native forms, were computationally modeled and validated. 76 lead molecules from various natural sources were screened and their drug likeliness and pharmacokinetic features were predicted. Among these ligands, two lead molecules that demonstrated ideal drug-likeliness and pharmacokinetic features were docked against HOG1 and CDC42 and their binding potential was compared with the binding of conventional drug Fluconazole with their usual target. The prediction was computationally validated by MD simulation. The current study revealed that Cudraxanthone-S present in Cudrania cochinchinensis and Scutifoliamide-B present in Piper scutifolium exhibited ideal drug likeliness, pharmacokinetics and binding potential to the prioritized targets in comparison with the binding of Fluconazole and their usual target. MD simulation showed that CDC42-Cudraxanthone-S and HOG1-Scutifoliamide-B complexes were exhibited stability throughout MD simulation. Thus, the study provides significant insight into employing HOG1 and CDC42 of MAPK as putative drug targets of C. albicans and Cudraxanthone-S and Scutifoliamide-B as potential inhibitors for drug discovery.Communicated by Ramaswamy H. Sarma.