Molecular dynamics simulations on interactions of five antibiotics with luciferase of Vibrio Qinghaiensis sp.-Q67

A large number of antibiotics have been used in the medical industry, agriculture, and animal husbandry industry in recent years. It may cause pollution to the aquatic environment and ultimately threaten to human health due to their prolonged exposure to the environment. We aim to study the toxicity mechanism of enrofloxacin (ENR), chlortetracycline hydrochloride (CTC), trimethoprim (TMP), chloramphenicol (CMP), and erythromycin (ETM) to luciferase of Vibrio Qinghaiensis sp.-Q67 (Q67) by using toxicity testing combined with molecular docking, molecular dynamics, and binding free energy analysis. The curve categories for ENR were different from the other four antibiotics, with ENR being J-type and the rest being S-type, and the toxicity of these five antibiotics (pEC50) followed the order of ENR (7.281) > ETM (6.814) > CMP (6.672) > CTC (6.400) > TMP (6.123), the order of toxicity value is consistent with the the magnitude of the binding free energy (ENR (-47.759 kcal/mol), ETM (-46.821 kcal/mol), CMP (-42.905 kcal/mol), CTC (-40.946 kcal/mol), TMP (-28.251 kcal/mol)). The van der Waals force provided the most important contribution to the binding free energy of the five antibiotics in the binding system with Q67 luciferase. Therefore, the dominant factor for the binding of antibiotics to luciferase was shape compensation. The face-to-face π-π stacking interaction between the diazohexane structure outside the active pocket region and the indoles structure of Phe194 and Phe250 in the molecular structure was the main reason for the highest toxicity value of antibiotic ENR. The hormesis effect of ENR has a competitive binding relationship with the α and β subunits of luciferase. Homology modeling, molecular docking, molecular dynamics simulations and binding free energy calculations were used to derive the toxicity magnitude of different antibiotics against Q67, and insights at the molecular level. The conclusion of toxicological experiments verified the correctness of the simulation results. This study contributes to the understanding of toxicity mechanisms of five antibiotics and facilitates risk assessment of antibiotic contaminants in the aquatic environment.

In-silico study of the interactions between acylated glucagon like-peptide-1 analogues and the native receptor

We have performed a series of multiple molecular dynamics (MD) simulations of glucagon-like peptide-1 (GLP-1) and acylated GLP-1 analogues in complex with the endogenous receptor (GLP-1R) to obtain a molecular understanding of how fatty acid (FA) chain structure, acylation position on the peptide, and presence of a linker affect the binding. MD simulations were analysed to extract heatmaps of receptor-peptide interaction patterns and to determine the free energy of binding using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach. The extracted free energies from MM-PBSA calculations are in qualitative agreement with experimentally determined potencies. Furthermore, the interaction patterns seen in the receptor-GLP-1 complex simulations resemble previously reported binding interactions validating the simulations. Analysing the receptor-GLP-1 analogue complex simulations, we found that the major differences between the systems stem from FA interactions and positioning of acylation in the peptide. Hydrophobic interactions between the FA chain and a hydrophobic patch on the extracellular domain contribute significantly to the binding affinity. Acylation on Lys26 resulted in noticeably more interactions between the FA chain and the extracellular domain hydrophobic patch than found for acylation on Lys34 and Lys38, respectively. The presence of a charged linker between the peptide and FA chain can potentially stabilise the complex by forming hydrogen bonds to arginine residues in the linker region between the extracellular domain and the transmembrane domain. A molecular understanding of the fatty acid structure and its effect on binding provides important insights into designing acylated agonists for GLP-1R.Communicated by Ramaswamy H. Sarma.

Antioxidant defence system is responsible for the toxicological interactions of mixtures: A case study on PFOS and PFOA in Daphnia magna

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are two types of perfluorinated compounds (PFCs) frequently studied in recent years due to their potential for bioaccumulation and toxicity to humans. Usually, PFCs can co-exist in various environment. Therefore, over- or under-estimated risk assessments would result if antagonism or synergism occurred in mixture toxicity. In the present study, the acute and chronic toxicities of single and mixtures of PFOA and PFOS to Daphnia magna were investigated. PFOS was more toxic than PFOA, both in 48-h acute toxicity and 21-d chronic toxicity. In acute toxicity tests, mixture toxicities showed strong synergistic effects on mortality. The experimental EC₅₀ of the mixture is 4.44 × 10^-5 mol/L, whereas the predicted EC₅₀ is 8.19 × 10^-5 mol/L by Concentration Addition Model and 9.73 × 10^-5 mol/L by Independent Action Model. In chronic toxicity tests, synergistic effects were also found in the aspects of offspring. The offspring rate is reduced significantly to 39.8% at the 9.61 × 10^-7 mol/L of mixture, while, PFOS and PFOA do not have effects when they are tested individually at corresponding concentrations. To explore the potential mechanism of the synergistic effect, the interactions between PFCs and proteins, including acetylcholinesterase, superoxide dismutase, catalase, ecdysone receptor and glutathione-S-transferase, were investigated by the Molecular Docking. The docking results revealed that the driving forces for the binding of PFCs with proteins were predominantly hydrophobic and hydrogen-bonding interactions. Based on the binding models, we deduced that the potential mechanism of synergism is that PFOS and PFOA have similar binding modes with catalase and have different binding modes with superoxide dismutase. Overall, these data provide experimental evidence that there is strong synergism in acute and chronic toxicity of mixtures to D. magna and demonstrate that molecular structure of some components of the antioxidant defence system contributes to the synergistic interaction.

Combined Toxicity of 2,4-Dichlorophenoxyacetic Acid and Its Metabolites 2,4-Dichlorophenol (2,4-DCP) on Two Nontarget Organisms

2,4-Dichlorophenoxyacetic acid (2,4-D), a phenoxyalkanoic acid herbicide, is among the most widely distributed pollutants in the environment. 2,4-Dichlorophenol (2,4-DCP), as the main metabolite of 2,4-D, always accompanies 2,4-D. In this paper, we did research on the combined toxicities of 2,4-D and 2,4-DCP to Vibrio qinghaiensis sp.-Q67 (Q67) and Caenorhabditis elegans. It was found that the toxicity of 2,4-DCP is more severe than that of its parent 2,4-D at any concentration levels whether to Q67 or to C. elegans. Furthermore, 2,4-DCP to Q67 has the time-dependent toxicity. The toxicity of the mixture of 2,4-D and 2,4-DCP to Q67 is increasing with the exposure time, but that to C. elegans does not change over time. There is a good linear relationship between the pEC₅₀/pLC₅₀ value of binary mixture ray of 2,4-D and 2,4-DCP and the mixture ratio of 2,4-DCP, which implies the predictability of mixture toxicity of 2,4-D and 2,4-DCP. The toxicological interactions of the binary mixtures to Q67 are basically additive actions whether at 0.25 or at 12 h. However, most mixtures have antagonistic interactions against C. elegans.

Toxicological interaction of multi-component mixtures to Vibrio qinghaiensis sp.-Q67 induced by at least three components

It has been stated by researchers that the antibiotic polymyxin B sulfate (POL) is a key component inducing time-dependent antagonism in freshwater luminescent bacteria, Vibrio qinghaiensis sp.-Q67, exposed in the ternary mixture system of the ionic liquids, pesticide and antibiotics. However, the previous statement is limited to ternary and quaternary mixtures without considering situations such as the binary system. In order to prove the direct inducing of antagonism by POL in a more complete and systematic way, two categories of experiments (adding POL in non-antagonistic ternary system and decomposing antagonistic ternary system with POL into the binary system) have been conducted in this paper. The results showed that quaternary mixture systems (adding POL to non-antagonism ternary mixture, up verification) exhibit antagonistic action in a majority of rays, at some point in the experiment. However, by decomposing the antagonistic ternary mixtures with POL into binary mixtures (down verification), the combined toxicities of binary mixtures at all time points in the experiment are additive. Obviously, the POL has a significant contribution to antagonism only in the ternary and quaternary mixtures, but not in the binary mixtures. We can draw a new conclusion that the antagonism of the multi-component mixtures is induced by at least three components (including POL), with complex chemical interactions. Therefore, considering POL's influence of antagonism as an example, future environmental protection practitioners and academic researchers should construct more scenarios of mixtures when assessing the influences and reactions of certain chemicals causing pollutions.

Two-Stage Prediction on Effects of Mixtures Containing Phenolic Compounds and Heavy Metals on Vibrio qinghaiensis sp. Q67

Two-stage prediction (TSP) model had been developed to predict toxicities of mixtures containing complex components, but its prediction power need to be further validated. Six phenolic compounds and six heavy metals were selected as mixture components. One mixture (M1) was built with equivalent-effect concentration ratio and four mixtures (M2-M5) were designed with fixed concentration ratio. In M1-M5, the toxicities were well predicted by TSP model, while CA overestimated and IA underestimated the toxicities. In M1-M5, compared with the actual mixture EC50 value, the prediction errors of TSP model (13.9%, 17.9%, 19.2%, and 17.3% and 15.8%, respectively) were significantly lower than those in the CA (higher than 30%) and IA models (20.9%, 33.0%, 20.6%, 21.8% and 12.5%, respectively). Thus, the TSP model performed better than the CA and IA model.

Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns

The MIEC-GBDT model can be used as a powerful tool to identify potential interference compounds in luciferase-based high-throughput screening.

Hormesis of some organic solvents on Vibrio qinghaiensis sp.-Q67 from first binding to the β subunit of luciferase

Hormesis is a biphasic concentration–response relationship. During the luminescence inhibition test ofVibrio qinghaiensissp.-Q67 (Q67), some organic solvents display the hormesis phenomenon.

Predictability of the time-dependent toxicities of aminoglycoside antibiotic mixtures to Vibrio qinghaiensis sp.-Q67

The combined toxicities of all binary mixtures constructed by four aminoglycoside (AG) antibiotics are concentration additive, which has nothing to do with exposure time, mixture ratio, and concentration level.