On the computer-aided modelling of analyte–receptor interactions for an efficient sensor design

The fundamental understanding of the interaction between analytes and receptor surface in a sensing chain system is crucial in order to guide the design of appropriate materials for sensor devices. The interaction analyte–receptor generates a signal, which is amplified and correlated with analyte concentration. The platform is usually composed of a chemical sensing and a pattern-recognition system, thus providing sensitivity, specificity, and performances of such sensors. Each analyte captured by the sensing system produces a signature or “fingerprint”. Our goal is to develop computational approaches, in order to improve the design of polymer-receptor in biosensors. In order to investigate the influence of the architecture of the polymeric film used as a receptor, we have conducted simulation employing Monte Carlo ballistic deposition method for an analyte with four types of macromolecules: linear, two brushes configurations and star. The results of the simulations showed that the polymeric architectures play a crucial role in the time response, specificity, sensitivity, and stability of the biosensor.