Analysis of aptamer-target binding and molecular mechanisms by thermofluorimetric analysis and molecular dynamics simulation

Front Chem. 2023 May 9:11:1144347. doi: 10.3389/fchem.2023.1144347. eCollection 2023.

Abstract

Introduction: Aptamers are valuable for bioassays, but aptamer-target binding is susceptible to reaction conditions. In this study, we combined thermofluorimetric analysis (TFA) and molecular dynamics (MD) simulations to optimize aptamer-target binding, explore underlying mechanisms and select preferred aptamer. Methods: Alpha-fetoprotein (AFP) aptamer AP273 (as the model) was incubated with AFP under various experimental conditions, and melting curves were measured in a real-time PCR system to select the optimal binding conditions. The intermolecular interactions of AP273-AFP were analysed by MD simulations with these conditions to reveal the underlying mechanisms. A comparative study between AP273 and control aptamer AP-L3-4 was performed to validate the value of combined TFA and MD simulation in selecting preferred aptamers. Results: The optimal aptamer concentration and buffer system were easily determined from the dF/dT peak characteristics and the melting temperature (Tm) values on the melting curves of related TFA experiments, respectively. A high Tm value was found in TFA experiments performed in buffer systems with low metal ion strength. The molecular docking and MD simulation analyses revealed the underlying mechanisms of the TFA results, i.e., the binding force and stability of AP273 to AFP were affected by the number of binding sites, frequency and distance of hydrogen bonds, and binding free energies; these factors varied in different buffer and metal ion conditions. The comparative study showed that AP273 was superior to the homologous aptamer AP-L3-4. Conclusion: Combining TFA and MD simulation is efficient for optimizing the reaction conditions, exploring underlying mechanisms, and selecting aptamers in aptamer-target bioassays.

Keywords: aptamer selection; aptamers; molecular dynamics simulation; reaction condition optimization; thermofluorimetric analysis.

Grants and funding

This study is supported by the National Natural Science Foundation of China (82160444) and the Science and Technology Project of Jiangxi Province, China (20192BBG70048).