Project description:Despite having been tagged as safe and beneficial, recent evidence remains inconclusive regarding the status of artificial sweeteners and their putative effects on gut microbiota. Gut microorganisms are essential for the normal metabolic functions of their host. These microorganisms communicate within their community and regulate group behaviors via a molecular system termed quorum sensing (QS). In the present study, we aimed to study the effects of artificial sweeteners on this bacterial communication system. Using biosensor assays, biophysical protein characterization methods, microscale thermophoresis, swarming motility assays, growth assays, as well as molecular docking, we show that aspartame, sucralose, and saccharin have significant inhibitory actions on the Gram-negative bacteria N-acyl homoserine lactone-based (AHL) communication system. Our studies indicate that these three artificial sweeteners are not bactericidal. Protein-ligand docking and interaction profiling, using LasR as a representative participating receptor for AHL, suggest that the artificial sweeteners bind to the ligand-binding pocket of the protein, possibly interfering with the proper housing of the native ligand and thus impeding protein folding. Our findings suggest that these artificial sweeteners may affect the balance of the gut microbial community via QS-inhibition. We, therefore, infer an effect of these artificial sweeteners on numerous molecular events that are at the core of intestinal microbial function, and by extension on the host metabolism.
Project description:The emergence of multidrug-resistant bacteria stimulates the search for new substitutes to traditional antimicrobial agents, especially molecules with antivirulence properties, such as those that interfere with quorum sensing (QS). This study aimed to evaluate the potential of phenolic compounds for QS inhibition in a QS biosensor strain (Chromobacterium violaceum) and three foodborne bacterial species (Aeromonas hydrophila, Salmonella enterica serovar Montevideo, and Serratia marcescens). Initially, an in silico molecular docking study was performed to select the compounds with the greatest potential for QS inhibition, using structural variants of the CviR QS regulator of C. violaceum as target. Curcumin, capsaicin, resveratrol, gallic acid, and phloridizin presented good affinity to at least four CviR structural variants. These phenolic compounds were tested for antimicrobial activity, inhibition of biofilm formation, and anti-QS activity. The antimicrobial activity when combined with kanamycin was also assessed. Curcumin, capsaicin, and resveratrol inhibited up to 50% of violacein production by C. violaceum. Biofilm formation was inhibited by resveratrol up to 80% in A. hydrophila, by capsaicin and curcumin up to 40% in S. Montevideo and by resveratrol and capsaicin up to 60% in S. marcescens. Curcumin completely inhibited swarming motility in S. marcescens. Additionally, curcumin and resveratrol increased the sensitivity of the tested bacteria to kanamycin. These results indicate that curcumin and resveratrol at concentrations as low as 6μM are potential quorum sensing inhibitors besides having antimicrobial properties at higher concentrations, encouraging applications in the food and pharmaceutical industries.
Project description:The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski's rules and have a structure analogous to ellipticine, was performed using the quantitative structure activity relationship (QSAR) technique, followed by a detailed docking study on three different proteins significantly involved in this disease (PDB IDs: SYK, PI3K and BTK). As a result, a model with only four descriptors (HOMO, softness, AC1RABAMBID, and TS1KFABMID) was found to be robust enough for prediction of the antileukemia activity of the compounds studied in this work, with an R2 of 0.899 and Q2 of 0.730. A favorable interaction between the compounds and their target proteins was found in all cases; in particular, compounds 9 and 22 showed high activity and binding free energy values of around -10 kcal/mol. Theses compounds were evaluated in detail based on their molecular structure, and some modifications are suggested herein to enhance their biological activity. In particular, compounds 22_1, 22_2, 9_1, and 9_2 are indicated as possible new, potent ellipticine derivatives to be synthesized and biologically tested.
Project description:The main protease (Mpro) of SARS-associated coronavirus (SARS-CoV) had caused a high rate of mortality in 2003. Current events (2019-2020) substantiate important challenges for society due to coronaviruses. Consequently, advancing models for the antiviral activity of therapeutic agents is a necessary component of the fast development of treatment for the virus. An analogy between anti-SARS agents suggested in 2017 and anti-coronavirus COVID-19 agents are quite probable. Quantitative structure-activity relationships for SARS-CoV are developed and proposed in this study. The statistical quality of these models is quite good. Mechanistic interpretation of developed models is based on the statistical and probability quality of molecular alerts extracted from SMILES. The novel, designed structures of molecules able to possess anti-SARS activities are suggested. For the final assessment of the designed molecules inhibitory potential, developed from the obtained QSAR model, molecular docking studies were applied. Results obtained from molecular docking studies were in a good correlation with the results obtained from QSAR modeling.
Project description:This study investigated the quantitative structure-activity relationship (QSAR) of imidazole derivatives of 4,7-disubstituted coumarins as inhibitors of aromatase, a potential therapeutic protein target for the treatment of breast cancer. Herein, a series of 3,7- and 4,7-disubstituted coumarin derivatives (1-34) with R1 and R2 substituents bearing aromatase inhibitory activity were modeled as a function of molecular and quantum chemical descriptors derived from low-energy conformer geometrically optimized at B3LYP/6-31G(d) level of theory. Insights on origins of aromatase inhibitory activity was afforded by the computed set of 7 descriptors comprising of F10[N-O], Inflammat-50, Psychotic-80, H-047, BELe1, B10[C-O] and MAXDP. Such significant descriptors were used for QSAR model construction and results indicated that model 4 afforded the best statistical performance. Good predictive performance were achieved as verified from the internal (comprising the training and the leave-one-out cross-validation (LOO-CV) sets) and external sets affording the following statistical parameters: R (2) Tr = 0.9576 and RMSETr = 0.0958 for the training set; Q (2) CV = 0.9239 and RMSECV = 0.1304 for the LOO-CV set as well as Q (2) Ext = 0.7268 and RMSEExt = 0.2927 for the external set. Significant descriptors showed correlation with functional substituents, particularly, R1 in governing high potency as aromatase inhibitor. Molecular docking calculations suggest that key residues interacting with the coumarins were predominantly lipophilic or non-polar while a few were polar and positively-charged. Findings illuminated herein serve as the impetus that can be used to rationally guide the design of new aromatase inhibitors.
Project description:Quorum sensing is a communication system among bacteria to sense the proper time to express their virulence factors. Quorum sensing inhibition is a therapeutic strategy to block bacterial mechanisms of virulence. The aim of this study was to synthesize and evaluate new bioisosteres of N-acyl homoserine lactones as Quorum sensing inhibitors in Chromobacterium violaceum CV026 by quantifying the specific production of violacein. Five series of compounds with different heterocyclic scaffolds were synthesized in good yields: thiazoles, 16a-c, thiazolines 17a-c, benzimidazoles 18a-c, pyridines 19a-c and imidazolines 32a-c. All 15 compounds showed activity as Quorum sensing inhibitors except 16a. Compounds 16b, 17a-c, 18a, 18c, 19c and 32b exhibited activity at concentrations of 10 µM and 100 µM, highlighting the activity of benzimidazole 18a (IC50 = 36.67 µM) and 32b (IC50 = 85.03 µM). Pyridine 19c displayed the best quorum sensing inhibition activity (IC50 = 9.66 µM). Molecular docking simulations were conducted for all test compounds on the Chromobacterium violaceum CviR protein to gain insight into the process of quorum sensing inhibition. The in-silico data reveal that all 15 the compounds have higher affinity for the protein than the native AHL ligand (1). A strong correlation was found between the theoretical and experimental results.
Project description:Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q(2) = 0.823, r(2) = 0.979; CoMSIA model: q(2) = 0.804, r(2) = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors.
Project description:Quantitative structure-activity relationships (QSAR) provides a model that link biological activities of compounds to thier chemical stuctures and molecular docking study reveals the interaction between drug and its target enzyme. These studies were conducted on 1,3-dioxoisoindoline-4-aminoquinolines with the aim of producing a model that could be used to design highly potent antiplasmodium. The compounds were first optimized using Density Functional Theory (DFT) with basis set B3LYP/6-31G∗ then their descriptors calculated. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the four models generated was found to be the best having internal and external squared correlation coefficient (R 2) of 0.9459 and 0.7015 respectively, adjusted squared correlation coefficient (R adj) of 0.9278, leave-one-out (LOO) cross-validation coefficient (Q 2 cv) of 0.8882. The model shows that antiplasmodial activities of 1,3-dioxoisoindoline-4-aminoquinolines depend on ATSC5i, GATS8p, minHBint3, minHBint5, MLFER_A and topoShape descriptors. The model was validated to be predictive, robust and reliable. Hence, it can predict the antiplasmodium activities of new 1,3-dioxoisoindoline-4-aminoquinolines.The docking result indicates strong binding between 1,3-dioxoisoindoline-4-aminoquinolines and Plasmodium falciparum lactate dehydrogenase (pfLDH), and revealed the important of the morpholinyl substituent and amide linker in inhibiting pfLDH. These results could serve as a model for designing novel 1,3-dioxoisoindoline-4-aminoquinolines as inhibitors of PfLDH with higher antiplasmodial activities.
Project description:Developing broad-spectrum anti-coronavirus drugs is greatly important, since the novel SARS-CoV-2 has rapidly become a threat to the public health and economy worldwide. SARS-CoV 3-chymotrypsin-like protease (3CLpro), as highly conserved in betacoronavirus, is a viable target for anti-SARS drugs. A quantitative structure–activity relationship (QSAR) for inhibitory constants (pKi) of 89 compounds against SARS-CoV 3CLpro enzyme was developed by using support vector machine (SVM) and genetic algorithm. The optimal SVM model (C = 90.2339 and γ = 1.19826 × 10−5) based on six molecular descriptors has determination coefficients of 0.839 for the training set (65 compounds) and 0.747 for test set (24 compounds), and rms errors of 0.435 and 0.525, respectively. These results are accurate and acceptable compared with that in other models reported, although our SVM model deals with more samples in the dada set. The SVM model could be beneficial for search of novel 3CLpro enzyme inhibitors against SARS-CoV.
Project description:Isolation, genomic and metabolomic characterization of Streptomyces tendae VITAKN with quorum sensing inhibitory activity from southern India