A Highly Selective Biosensor Based on Peptide Directly Derived from the HarmOBP7 Aldehyde Binding Site - Publication - MOST Wiedzy

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A Highly Selective Biosensor Based on Peptide Directly Derived from the HarmOBP7 Aldehyde Binding Site

Abstract

This paper presents the results of research on determining the optimal length of a peptide chain to eectively bind octanal molecules. Peptides that map the aldehyde binding site in HarmOBP7 were immobilized on piezoelectric transducers. Based on computational studies, four Odorant Binding Protein-derived Peptides (OBPPs) with dierent sequences were selected. Molecular modelling results of ligand docking with selected peptides were correlated with experimental results. The use of low-molecular synthetic peptides, instead of the whole protein, enabled the construction OBPPs-based biosensors. This work aims at developing a biomimetic piezoelectric OBPPs sensor for selective detection of octanal. Moreover, the research is concerned with the ligand binding anity depending on dierent peptides’ chain lengths. The authors believe that the chain length can have a substantial influence on the type and eectiveness of peptide–ligand interaction. A confirmation of in silico investigation results is the correlation with the experimental results, which shows that the highest anity to octanal is exhibited by the longest peptide (OBPP4 – KLLFDSLTDLKKKMSEC-NH2). We hypothesized that the binding of long chain aldehydes to the peptide, mimicking the binding site of HarmOBP7, induced a conformational change in the peptide deposited on a selected transducer. The constructed OBPP4-based biosensors were able to selectively bind octanal in the gas phase. It was also shown that the sensors were characterized by high selectivity with respect to octanal, as well as to acetaldehyde and benzaldehyde. The results indicate that the OBPP4 peptide, mimicking the binding domain in the Odorant Binding Protein, can provide new opportunities for the development of biomimicking materials in the field of odor biosensors.

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Details

Category:
Magazine publication
Type:
artykuły w czasopismach dostępnych w wersji elektronicznej [także online]
Published in:
SENSORS pages 1 - 13,
ISSN: 1424-8220
Language:
English
Publication year:
2019
DOI:
Digital Object Identifier (open in new tab) 10.3390/s19194284
Verification:
Gdańsk University of Technology

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