An insilico design of a peptide bioreceptor for cortisol using molecular modelling techniques

Abstract

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8-140ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic methodology and semi-automated high -throughput screening tool for candidate bioreceptors from protein databases, via molecular docking, ranking them according to their binding affinities by conducting automated AutoDock Vina scoring simulations and finally validation via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS. These explicit molecular dynamic calculations are carried out in eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a continuous sequence of amino acids favourably interacting with the target ligand i.e., cortisol from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.