Toward an Aptamer-Based Biosensor for Cross-Serotype Detection of Botulinum Neurotoxin: Binding Mechanism from a Comprehensive Computational Investigation
Year: 2026
Type: article
Abstract: , which can be found in a wide variety of foods. The treatment of this illness requires antitoxin drugs given to the patients within a 48 h window to be effective. Therefore, there is a strong need for the development of rapid and sensitive biosensors for the early detection of the toxins in foods. In this work, by using comprehensive atomistic computer modeling and simulation, we investigate aptamers that bind BoNT serotypes A, B, and C to serve as recognition elements in the biosensor design for cross-serotype detection. Known aptamers targeting BoNT/A and BoNT/C are investigated in this work to understand whether they can cross-react with other serotypes. Our results highlight the importance of structural relaxation of the aptamers, which the standard docking procedure cannot adequately capture. During molecular dynamics, most of the aptamer structural relaxation correlates with binding to the toxins. Among the aptamers chosen, we found two aptamers A4 and A5 that bind equally strongly to all serotypes. C5 binds equally strongly to its designated target of serotype C, but it binds significantly weaker to other serotypes. These most promising aptamers, based on the computational analyses, are all ssDNA aptamers and share a common feature of a DNA double helix of 5-6 nucleotide base pairs. For all aptamers investigated, there are two binding sites for an aptamer on BoNT, which are located on opposite sides of the protein. This suggests an experimental setup of a sandwich array to allow aptamers to bind to both binding sites for optimal sensitivities. Our computational results provide molecular mechanisms of binding and assist in further optimization and design of new aptamers for these botulinum neurotoxins.
Source: ACS Omega
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,935
Award: G1237294521
OpenAlex ID: W7203791563
