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Prediction of Aptamer-Small-Molecule Interactions Using Metastable States from Multiple Independent Molecular Dynamics Simulations

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Understanding aptamer-ligand interactions is necessary to rationally design aptamer-based systems. Commonly used in silico tools have proven to be accurate to predict RNA and DNA oligonucleotide tertiary structures. However, given… Click to show full abstract

Understanding aptamer-ligand interactions is necessary to rationally design aptamer-based systems. Commonly used in silico tools have proven to be accurate to predict RNA and DNA oligonucleotide tertiary structures. However, given the complexity of nucleic acids, the most thermodynamically stable conformation is not necessarily the one with the highest affinity for a specific ligand. Because many metastable states may coexist, it remains challenging to predict binding sites through molecular docking simulations using available computational pipelines. In this study, we used independent simulations to broaden the conformational diversity sampled from DNA initial models of distinct stability and assessed the binding affinity of selected metastable representative structures. In our results, utilizing multiple metastable conformations for molecular docking analysis helped identify structures favorable for ligand binding and accurately predict the binding sites. Our workflow was able to correctly identify the binding sites of the characterized adenosine monophosphate and l-argininamide aptamers. Additionally, we demonstrated that our pipeline can be used to aid the design of competition assays that are conducive to aptasensing strategies using an uncharacterized aflatoxin B1 aptamer. We foresee that this approach may help rationally design effective and truncated aptamer sequences interacting with protein biomarkers or small molecules of interest for drug design and sensor applications.

Keywords: small molecule; metastable states; aptamer small; prediction aptamer; binding sites; aptamer

Journal Title: Journal of chemical information and modeling
Year Published: 2022

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