Assessment of triclosan induced histopathological and biochemical alterations, and molecular docking simulation analysis of acetylcholinesterase enzyme in the gills of fish, Cyprinus carpio.

Triclosan (TCS), an antimicrobial additive in various personal and health care products, has been widely detected in aquatic environment around the world. The present study investigated the impacts of TCS in the gills of the fish, Cyprinus carpio employing histopathological, biochemical, molecular docking and simulation analysis. The 96 h LC50 value of TCS in C. carpio was found to be 0.968 mg/L. Fish were exposed to 1/1000th (1 µg/L), 1/100th (10 µg/L), and 1/10th (100 µg/L) of 96 h LC50 value for a period of 28 days. The histopathological alterations observed in the gills were hypertrophy, hyperplasia, edematous swellings, and fusion of secondary lamellae in TCS exposed groups. The severity of these alterations increased with both the concentration as well as the duration of exposure. The present study revealed that the activity of antioxidant enzymes such as superoxide dismutase, catalase, glutathione-S-transferase, glutathione reductase, glutathione peroxidase, and reduced glutathione content decreased significantly (p < 0.05) in both concentration and duration dependent manner. However, a significant (p < 0.05) increase in the activity of the metabolic enzymes such as acid phosphatase and alkaline phosphatase was observed in all three exposure concentrations of TCS from 7 to 28 days. The activity of acetylcholinesterase declined significantly (p < 0.05) from 7 to 28 days whereas the content of acetylcholine increased significantly at the end of 28 day. The experimental results were further confirmed by molecular docking and simulation analysis that showed strong binding of TCS with acetylcholinesterase enzyme. The study revealed that long-term exposure to sublethal concentrations of TCS can lead to severe physiological and histopathological alterations in the fish.

Differentiating stable and unstable protein using convolution neural network and molecular dynamics simulations.

Protein stability is a critical aspect of molecular biology and biochemistry, hinges on an intricate balance of thermodynamic and structural factors. Determining protein stability is crucial for understanding and manipulating biological machineries, as it directly correlated with the protein function. Thus, this study delves into the intricacies of protein stability, highlighting its dependence on various factors, including thermodynamics, thermal conditions, and structural properties. Moreover, a notable focus is placed on the free energy change of unfolding (ΔGunfolding), change in heat capacity (ΔCp) with protein structural transition, melting temperature (Tm) and number of disulfide bonds, which are critical parameters in understanding protein stability. In this study, a machine learning (ML) predictive model was developed to estimate these four parameters using the primary sequence of the protein. The shortfall of available tools for protein stability prediction based on multiple parameters propelled the completion of this study. Convolutional Neural Network (CNN) with multiple layers was adopted to develop a more reliable ML model. Individual predictive models were prepared for each property, and all the prepared models showed results with high accuracy. The R2 (coefficient of determination) of these models were 0.79, 0.78, 0.92 and 0.92, respectively, for ΔG, ΔCp, Tm and disulfide bonds. A case study on stability analysis of two homologous proteins was presented to validate the results predicted through the developed model. The case study included in silico analysis of protein stability using molecular docking and molecular dynamic simulations. This validation study assured the accuracy of each model in predicting the stability associated properties. The alignment of physics-based principles with ML models has provided an opportunity to develop a fast machine learning solution to replace the computationally demanding physics-based calculations used to determine protein stability. Furthermore, this work provided valuable insights into the impact of mutation on protein stability, which has implications for the field of protein engineering. The source codes are available at https://github.com/Growdeatechnology.