Nanobiosensors can be built via functionalization of atomic force microscopy (AFM) tips with biomolecules capable of interacting with the analyte on a substrate, and the detection being performed by measuring the force between the immobilized biomolecule and the analyte. The optimization of such sensors may require multiple experiments to determine suitable experimental conditions for the immobilization and detection. In this study we employ molecular modeling techniques to assist in the design of nanobiosensors to detect herbicides. As a proof of principle, the properties of acetyl co-enzyme A carboxylase (ACC) were obtained with molecular dynamics simulations, from which the dimeric form in an aqueous solution was found to be more suitable for immobilization owing to a smaller structural fluctuation than the monomeric form. Upon solving the nonlinear Poisson-Boltzmann equation using a finite-difference procedure, we found that the active sites of ACC exhibited a positive surface potential while the remainder of the ACC surface was negatively charged. Therefore, optimized biosensors should be prepared with electrostatic adsorption of ACC onto an AFM tip functionalized with positively charged groups, leaving the active sites exposed to the analyte. The preferential orientation for the herbicides diclofop and atrazine with the ACC active site was determined by molecular docking calculations which displayed an inhibition coefficient of 0.168 μM for diclofop, and 44.11 μM for atrazine. This binding selectivity for the herbicide family of diclofop was confirmed by semiempirical PM6 quantum chemical calculations which revealed that ACC interacts more strongly with the herbicide diclofop than with atrazine, showing binding energies of -119.04 and +8.40 kcal mol(-1), respectively. The initial measurements of the proposed nanobiosensor validated the theoretical calculations and displayed high selectivity for the family of the diclofop herbicides.
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