Genetic engineering of a bacteriophage is a promising way to develop a highly functional biosensor. Almost countless configurational degree of freedom in the manipulation, considerable uncertainty and cost involved with the approach, however, have been huddles for the objective. In this paper, we demonstrate rapidly responding optical biosensor with high selectivity toward gaseous explosives with genetically engineered phages. The sensors are equipped with peptide sequences in phages optimally interacting with the volatile organic compounds (VOCs) in visible light regime. To overcome the conventional issues, we use extensive utilization of empirical calculations to construct a large database of 8000 tripeptides and screen the best for electronic nose sensing performance toward nine VOCs belonging to three chemical classes. First-principles density functional theory (DFT) calculations unveil underlying correlations between the chemical affinity and optical property change on an electronic band structure level. The computational outcomes are validated by in vitro experimental design and testing of multiarray sensors using genetically modified phage implemented with five selected tripeptide sequences and wild-type phages. The classification success rates estimated from hierarchical cluster analysis are shown to be very consistent with the calculations. Our optical biosensor demonstrates a 1 ppb level of sensing resolution for explosive VOCs, which is a substantial improvement over conventional biosensor. The systematic interplay of big data-based computational prediction and in situ experimental validation can provide smart design principles for unconventionally outstanding biosensors.
Bibliographical noteFunding Information:
This research was supported by the Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( NRF-2017M3D1A1039287 ) and the Global Frontier Program through the Global Frontier Hybrid Interface Materials ( GFHIM ) of National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Project No. 2013M3A6B1078882 ).
© 2021 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering