2-Substituted Benzoxazoles as Potent Anti-Inflammatory Agents: Synthesis, Molecular Docking and In Vivo Anti-Ulcerogenic Studies

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are the commonly used therapeutic interventions of inflammation and pain that competitively inhibit the cyclooxygenase (COX) enzymes. Several side effects like gastrointestinal and renal toxicities are associated with the use of these drugs. The therapeutic anti-inflammatory benefits of NSAIDs are produced by the inhibition of COX-2 enzymes, while undesirable side effects arise from the inhibition of COX-1 enzymes. Objectives: In the present study, a new series of 2-substituted benzoxazole derivatives 2(a-f) and 3(a-e) were synthesized in our lab as potent anti-inflammatory agents with outstanding gastro-protective potential. The new analogs 2(a-f) and 3(a-e) were designed depending upon the literature review to serve as ligands for the development of selective COX-2 inhibitors. Methods: The synthesized analogs were characterized using different spectroscopic techniques (FTIR, 1HNMR, 13CNMR) and elemental analysis. All synthesized compounds were screened for their binding potential in the protein pocket of COX-2 and evaluated for their anti-inflammatory potential in animals using the carrageenan-induced paw edema method. Further 5 compounds were selected to assess the in vivo anti-ulcerogenic activity in an ethanol-induced anti-ulcer rat model. Results: Five compounds (2a, 2b, 3a, 3b and 3c) exhibited potent anti-inflammatory activity and significant binding potential in the COX-2 protein pocket. Similarly, these five compounds demonstrated a significant gastro-protective effect (p<0.01) in comparison to the standard drug, Omeprazole. Conclusion: Depending upon our results, we hypothesize that 2-substituted benzoxazole derivatives have excellent potential to serve as candidates for the development of selective anti-inflammatory agents (COX-2 inhibitors). However, further assessments are required to delineate their underlying mechanisms.

Photoinduced isomerization sampling of retinal in bacteriorhodopsin

Photoisomerization of retinoids inside a confined protein pocket represents a critical chemical event in many important biological processes from animal vision, non-visual light effects, to bacterial light sensing and harvesting. Light driven proton pumping in bacteriorhodopsin entails exquisite electronic and conformational reconfigurations during its photocycle. However, it has been a major challenge to delineate transient molecular events preceding and following the photoisomerization of the retinal from noisy electron density maps when varying populations of intermediates coexist and evolve as a function of time. Here I report several distinct early photoproducts deconvoluted from the recently observed mixtures in time-resolved serial crystallography. This deconvolution substantially improves the quality of the electron density maps hence demonstrates that the all-trans retinal undergoes extensive isomerization sampling before it proceeds to the productive 13-cis configuration. Upon light absorption, the chromophore attempts to perform trans-to-cis isomerization at every double bond coupled with the stalled anti-to-syn rotations at multiple single bonds along its polyene chain. Such isomerization sampling pushes all seven transmembrane helices to bend outward, resulting in a transient expansion of the retinal binding pocket, and later, a contraction due to recoiling. These ultrafast responses observed at the atomic resolution support that the productive photoreaction in bacteriorhodopsin is initiated by light-induced charge separation in the prosthetic chromophore yet governed by stereoselectivity of its protein pocket. The method of a numerical resolution of concurrent events from mixed observations is also generally applicable.

Photoinduced isomerization sampling of retinal in bacteriorhodopsin

Photoisomerization of retinoids inside a confined protein pocket represents a critical chemical event in many important biological processes from animal vision, non-visual light effects, to bacterial light sensing and harvesting. Light driven proton pumping in bacteriorhodopsin entails exquisite electronic and conformational reconfigurations during its photocycle. However, it has been a major challenge to delineate transient molecular events preceding and following the photoisomerization of the retinal from noisy electron density maps when varying populations of intermediates coexist and evolve as a function of time. Here I report several distinct early photoproducts deconvoluted from the recently observed mixtures in time-resolved serial crystallography. This deconvolution substantially improves the quality of the electron density maps hence demonstrates that the all-trans retinal undergoes extensive isomerization sampling before it proceeds to the productive 13-cis configuration. Upon light absorption, the chromophore attempts to perform trans-to-cis isomerization at every double bond coupled with the stalled anti-to-syn rotations at multiple single bonds along its polyene chain. Such isomerization sampling pushes all seven transmembrane helices to bend outward, resulting in a transient expansion of the retinal binding pocket, and later, a contraction due to recoiling. These ultrafast responses observed at the atomic resolution support that the productive photoreaction in bacteriorhodopsin is initiated by light-induced charge separation in the prosthetic chromophore yet governed by stereoselectivity of its protein pocket. The method of a numerical resolution of concurrent events from mixed observations is also generally applicable.

Evaluation of COVID-19 protease and HIV inhibitors interactions

The epidemic of the novel coronavirus disease (COVID-19) that started in 2019 has evoked an urgent demand for finding new potential therapeutic agents. In this study, we performed a molecular docking of anti-HIV drugs to refine HIV protease inhibitors and nucleotide analogues to target COVID-19. The evaluation was based on docking scores calculated by AutoDock Vina and top binding poses were analyzed. Our results suggested that lopinavir, darunavir, atazanavir, remdesivir, and tipranavir have the best binding affinity for the 3-chymotrypsin-like protease of COVID-19. The comparison of the binding sites of three drugs, namely, darunavir, atazanavir and remdesivir, showed an overlap region of the protein pocket. Our study showed a strong affinity between lopinavir, darunavir, atazanavir, tipranavir and COVID-19 protease. However, their efficacy should be confirmed by in vitro studies since there are concerns related to interference with their active sites.

Computational study on the molecular interactions of tramadol with selected antioxidant and detoxification enzymes in Drosophila melanogaster

Tramadol is a potent analgesic medication prescribed worldwide for treatment of acute and chronic pains. Its relative tendency to be abused has become a public health concern. This study was designed to evaluate the molecular interactions of tramadol with selected antioxidant and detoxification enzymes of Drosophila melanogaster. The structures of CYP2D6, catalase and defense repressor 1 were retrieved from protein database (PubMed, Swiss model) while tramadol 2D structure was obtained from PubChem repository and prepared using LigPrep scripts as implemented in Small-Molecule Drug Discovery Suite of Schrödinger. 2D structure of tramadol was docked into the protein model binding site using Glide software from Schrodinger. The result revealed that, on the binding pocket of CYP2D6, tramadol and CYP2D6 inhibitor bind the protein pocket via hydrogen bond. The hydroxyl group of tramadol and the inhibitor interacts with the C=O groups of the residues Glu 128 and Lys 264 on the protein pocket respectively. Tramadol binds with higher affinity with a docking score of -4.581 kcal/mol when compared with the inhibitor which gave a docking score of -1.865 kcal/mol. With catalase, tramadol and the crystalized ligands were observed to exhibit hydrogen bonding with Ala 64 residue on the protein binding pocket with docking score of -2.348 kcal/mol and -3.431 kcal/mol respectively. The ability of tramadol to interact with these enzymes, strongly suggests that tramadol treatment may induce oxidative stress and at high dose might result in cellular toxicity. Therefore, the toxic effects of tramadol should be of concern despite the important role it plays in pain management.

Homology modeling and functional characterization of multidrug effluxor Mta protein from Bacillus Atrophaeus: An explanatory insilico approach

Phenotypically similar to B. subtilis, Bacillus atrophaeus is a Gram-positive, aerobic, spore-forming bacteria. It is a black-pigmented bacterial genus. Therefore, it is of interest to study the uncharacterized proteins in the genome. For a detailed computational sequence-structure-function analysis using available data and resources, an uncharacterized protein Mta (AKL87074.1) in the genome was selected. In this study, attempts were made to study the physicochemical properties, predict secondary structure, modeling the 3-D protein, pocket identification, protein-protein interaction and phylogenetic analysis of Mta protein. The predicted active site using CASTp is analyzed for understanding their multidrug resistance function. Because Mta is a MerR family member, these investigations on these functional aspects could lead us for better understanding of antibiotic resistance phenomenon.

Early combination treatment with existing HIV antivirals: an effective treatment for COVID-19?

Since no effective therapy exists, we aimed to test existing HIV antivirals for combination treatment of Coronavirus disease 19 (COVID-19). Our molecular docking findings suggest that lopinavir, ritonavir, darunavir, and atazanavir activated interactions with the key binding sites of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) protease with a better Ki for lopinavir, ritonavir, and darunavir. Furthermore, we evidenced the ability of remdesivir, tenofovir, emtricitabine, and lamivudine to be incorporated in SARS-CoV-2 RNA-dependent RNA polymerase in the same protein pocket where poses the corresponding natural nucleoside substrates with comparable Ki and activating similar interactions. In principle, the four antiviral nucleotides might be used effectively against SARS-CoV-2. The combination of a protease inhibitor and two nucleoside analogues should be evaluated in clinical trials for the treatment of COVID-19.

Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking

The outbreak of novel coronavirus (COVID-19) infections occurring in 2019 is in dire need of finding potential therapeutic agents. In this study, we used molecular docking strategies to repurpose HIV protease inhibitors and nucleotide analogues for COVID-19. The evaluation was made on docking scores calculated by AutoDock Vina and RosettaCommons. Preliminary results suggested that Indinavir and Remdesivir have the best docking scores and the comparison of the docking sites of these two drugs shows a near perfect dock in the overlap region of the protein pocket. However, the active sites inferred from the proteins of SARS coronavirus are not compatible with the docking site of COVID-19, which may give rise to concern in the efficacy of drugs.

Evaluation of Binding Site Comparison Algorithms and Proteometric Machine Learning Models in the Detection of Protein Pockets Capable of Binding the Same Ligand

<div> <div> <div> <p>Non linearities of biological networks present ample opportunity for synergistic protein targeting combinations. Yet, to date, our ability to design multi-target inhibitors and predict polypharmacology binding profiles remains limited. Herein, we present a systematic benchmarking of protein pocket comparison algorithms from the literature, as well as novel machine learning models developed to predict whether two proteins will bind the same ligand. The results demonstrate that previously reported performance metrics from the literature could be inflated due to a bias towards proteins of similar folds when identifying protein capable of binding the same ligand. This observation motivated a more in-depth evaluation of the methods against two subsets of same and cross protein fold comparisons. In a head to head comparison using the cross protein fold subset, we found that the proteometric machine learning models were the best performing models overall. </p> </div> </div> </div>