Structural basis of pesticide detection by enzymatic biosensing: a molecular docking and MD simulation study

Abstract

Designing of rapid, facile, selective, and cost-effective biosensor technology is a growing area for the detection of various classes of pesticides. The biosensor with these features can be achieved only through the various bio-components using different transducers. This study, therefore, focuses on the usage of molecular docking, specificity tendencies, and capabilities of proteins for the detection of pesticides. Accordingly, the four transducers, acetylcholinesterase (ACH), cytochromes P450 (CYP), glutathione S-transferase (GST), and protein kinase C (PKC) were selected based on their applications including neurotransmitter, metabolism, detoxification enzyme, and protein phosphorylation. Then after molecular docking of the pesticides, fenobucarb, dichlorodiphenyltrichloroethane (DDT), and parathion onto each enzyme, the conformational behavior of the most stable complexes was further analyzed using 50 ns Molecular Dynamics (MD) simulations carried out under explicit water conditions. In the case of protein kinase C (PKC) and cytochrome P450 3A4 enzyme (CYP), the fenobucarb complex showed the most suitable combination of free energy of binding and inhibition constant −4.42 kcal/mol (573.73 μM) and −5.1 kcal/mol (183.49 μM), respectively. Parathion dominated for acetylcholinesterase (ACH) with −4.57 kcal/mol (448.09 μM) and lastly dichlorodiphenyltrichloroethane for glutathione S-transferase (GST), −5.43 kcal/mol (103.88 μM). The RMSD variations were critical for understanding the impact of pesticides as they distinctively influence the energetic attributes of the proteins. Overall, the outcomes from the extensive analysis provide an insight into the structural features of the proteins studied, thereby highlighting their potential use as a substrate in biorecognition sensing of pesticide compounds.

Keywords:

• pesticides
• molecular docking
• molecular dynamics
• biosensors

Acknowledgments

The authors gratefully acknowledge the computational facility of Center for High Performance Computing (CHPC), Cape Town, South Africa and Durban University of Technology, Durban, South Africa.