The aim of this work was to optimise biomimetic-based strategies for possible pathogen detection. The use of new affinity ligands such as the oligopeptides computationally designed is particularly suitable for large-scale synthesis and overcomes the disadvantages of antibodies or enzymes which are often unstable and expensive. The pathogen prototype system chosen was Listeria bacterium because of the large amount of X ray and NMR structures associated to that pathogen available from the literature. 13 oligopeptides libraries (298 oligopeptides in total) mimicking the binding pocket of the listeria target, the protein cadherin, were designed. The contribution of individual peptide to binding was investigated using Hex a protein-ligand docking program. Four peptides different in length and binding energy were selected for experimental part. High-density colorimetric microarray was used to assess the effective binding and the selectivity of the receptors towards the target. The concentration of pathogen cells in solution ranged from 105 to 109 cells/ml. The raw signals were read using a conventional scanner and elaborated with an image software. The pixel intensity decrease of the signal was interpreted proportional to the probe-target binding. With gasket facilities, cross section data approach was applied detecting up to 512 entities simultaneously. In this way the main analytical parameters were investigated. This study was finalised to gain an understanding of how the parameters calculated by computational modelling could be helpful to select biomimetic receptors to bind the target in experimental conditions.
展开▼
