Sprecher
Beschreibung
The development of rapid, cost-effective, and highly selective biosensing platforms remains a pressing challenge across environmental, medical, and industrial sectors [1-3]. To address this, we introduce a general in silico aptamer discovery strategy designed for small-molecule detection, replacing the laborious SELEX (Systematic Evolution of Ligands by Exponential Enrichment) process with a streamlined computational pipeline. As a case study, we applied this approach to the detection of ammonium in aqueous solutions—an environmentally relevant target with limited aptamer availability [4].
Our method integrates machine learning, bioinformatics, and molecular docking to efficiently screen a large virtual library of RNA aptamer candidates. Predictive modeling identified sequences with high target affinity and structural adaptability. From this pool, five top-ranked aptamers were synthesized and incorporated into an electrochemical sensor array. Experimental evaluation demonstrated that target binding induced distinct conformational changes, producing highly reproducible electrochemical signals. The sensors exhibited strong sensitivity across relevant ammonium concentrations and high specificity, effectively distinguishing ammonium from closely related interferents such as sodium, potassium, and nitrate.
To uncover the molecular mechanisms underlying sensor behavior, we conducted molecular dynamics (MD) simulations on both unbound and ammonium-bound aptamers. The simulations revealed target-induced conformational rearrangements that directly correlated with the observed electrochemical response. These results provided a mechanistic link between aptamer structure and sensing performance, validating the predictive strength of the in-silico selection process.
Overall, this computational–experimental framework enables the rational design of aptamer-based sensors for small molecules. While demonstrated here for ammonium, the approach is broadly applicable, offering a scalable and efficient pathway for developing highly selective biosensors for diverse targets.
Refences:
1. N. A. Ahmad, R. M. Zulkifli, H. Hussin and M. H. Nadri, J. Mol. Graphics Modell., 2021, 105, 107872.
2. A. Stuber, A. Douaki, J. Hengsteler., D. Buckingham, D. Momotenko, D. Garoli and N. Nakatsuka, ACS Nano, 2023, 17, 16168–16179.
3. A. Douaki, A. Stuber, J. Hengsteler., D. Buckingham, D. Momotenko, N. Nakatsuka, D. Garoli and, Chem. Commun., 2023, 59, 14713-14716.
4. A. Douaki, D. Garoli, A. K. M. S. Inam, M. A. C. Angeli, G. Cantarella, W. Rocchia, J. Wang, L. Petti and P. Lugli, Biosensors, 2022, 12, 574.
