Virtual Screening Peptide Selection for a Peptide Based Gas Sensors Array

Abstract

A strategy for peptides selection, suitable for gas sensors arrays, is presented. Peptide selection was accomplished by using a virtual screening approach. In a first step, a complete tripeptide library (8000 elements) was generated. For each peptide, the geometry was optimized, and a set of their most representative conformers was considered. Docking simulations versus 160 different aroma molecules, representing different chemical classes with different shapes and dimensions, were carried out. Starting from the tri-peptide library results, the highest scoring 120 tripeptides selective for natural or synthetic strawberry aroma compounds (identified by GC-MS analysis) were selected for generating a 9300 tetrapeptide library in order to discriminate between the two different aromas (natural vs synthetic). The presence of unselective aminoacids, the ability of the peptides to give different scores with most natural/synthetic discriminating molecules, and the presence of aminoacids that can interfere during piezoelectric sensors functionalization were all taken into account in the selection process. Four tetrapeptides were then proposed as possible candidates for a gas sensor arrays to obtain a natural vs synthetic aroma molecules pattern recognition.