Computational modelling and optimization studies of electropentamer for molecular imprinting of DJ-1

Abstract

Parkinson's disease (PD) is the most prevalent type of incurable movement disorder. Recent research findings propose that the familial PD-associated molecule DJ-1 exists in cerebrospinal fluid (CSF) and that its levels may be altered as Parkinson's disease advances. By using a molecularly imprinted polymer (MIP) as an artificial receptor, it becomes possible to create a functional MIP with predetermined selectivity for various templates, particularly for the DJ-1 biomarker associated with Parkinson's disease. It mostly depends on molecular recognition via interactions between functional monomers and template molecules. So, the computational methods for the appropriate choice of functional monomers for creating molecular imprinting electropolymers (MIEPs) with particular recognition for the detection of DJ-1, a pivotal biomarker involved in PD, are undertaken in this study. Here, molecular docking, molecular dynamics simulations (MD), molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods, and quantum mechanical calculation have been applied to investigate the intermolecular interaction between DJ-1 and several functional electropentamers, viz., polypyrrole (PPy), poly(3,4-ethylenedioxythiophene) (PEDOT), poly(o-aminophenol) (POAP), and polythiophene (PTS). In this context, the electropentamers were selected to mimic the imprinted electropolymer system. We analyzed the most stable configurations of the formed complexes involving DJ-1 and electropentamers as a model system for MIEPs. Among these, PEDOT exhibited a more uniform arrangement around DJ-1, engaging in numerous van der Waals, H-bond, electrostatic, and hydrophobic interactions. Hence, it can be regarded as a preferable choice for synthesizing a MIP for DJ-1 recognition. Thus, it will aid in selecting a suitable functional monomer, which is of greater significance in the design and development of selective DJ-1/MIP sensors.

Keywords: AutoDock; MIPs; Molecular dynamic simulation; Neuronal disease biomarker; Oniom calculation.