(95h) Computational Modeling of RNA Aptamers: Structure Prediction of the Ligand-Free State

Yan, S. - Presenter, Iowa State University
Ilgu, M., Middle East Technical University
Nilsen-Hamilton, M., Ames National Laboratory
Lamm, M. H., Iowa State University
Nucleic acid aptamers are single-stranded oligonucleotides that bind to specific molecular targets with high RNA affinity. Aptamers are widely known as potential substitutes for antibodies and have great potential in therapeutic and diagnostic applications1,2. In order to design aptamers for new applications, it is important to understand the mechanisms by which aptamers bind molecular targets or ligands. Ideally, this is achieved by studying the structure and dynamics of ligand-bound and ligand-free states. The problem is that most NMR or crystal structures of RNA aptamers deposited in the Protein Data Bank are for aptamers in the ligand-bound state. There are very few instances of aptamers in the ligand-free state available. Hence, a method for obtaining valid ligand-free aptamer structures is of great importance for advancing our understanding of the binding process for aptamers. Predicting an RNA structure from its primary sequence is a desired route to achieve this goal. Previous studies have shown that presenting an ensemble of molecular conformations, that adhere to certain experimental constraints, provides a means to characterize the inherent flexibility of biomolecules that cannot be achieved by simply examining the conformation for a single structure.3,4 Inspired by this previous work, we performed a clustering procedure on aptamer structures that were predicted from the MC-Fold | MC-Sym pipeline5 for a given RNA aptamer sequence. The structures in the pool were clustered based on root mean-square deviation cutoffs. Using this procedure, we obtained representative conformation ensembles of ligand-bound and ligand-free states for the aptamer. Within each ensemble, we assigned relative population weight to each conformation based on experimentally determined base stacking for each aptamer state. The experimental data was obtained using 2-aminopurine fluorescence detection. To validate this approach for predicting an ensemble of aptamer structures, we compared the predicted ensembles with structures that were obtained from molecular dynamics simulation trajectories. The molecular dynamics simulations for the ligand-free state started from available NMR structures (of the ligand-bound state for the aptamer) with the ligand removed. Our results indicate that the ensemble constructed from MC-Sym prediction broadly sampled the conformation space and delineated the fluctuations and correlations of the residues in the aptamer. In summary, we present a way to generate an ensemble of conformations for the ligand-free state of an RNA aptamer. Looking forward, this approach for structure prediction of the ligand-free state has potential to provide insight about binding mechanisms for aptamers.

1. Germer, K., Leonard, M. & Zhang, X. RNA aptamers and their therapeutic and diagnostic applications. Int. J. Biochem. Mol. Biol. 4, 27–40 (2013).

2. Ilgu, M. et al. An adaptable pentaloop defines a robust neomycin-B RNA aptamer with conditional ligand-bound structures. RNA 20, 815–824 (2014).

3. Vammi, V., Lin, T.-L. & Song, G. Enhancing the quality of protein conformation ensembles with relative populations. J. Biomol. NMR 58, 209–225 (2014).

4. Zhu, G. et al. Investigating energy‐based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1. Proteins Struct. Funct. Bioinforma. (2018). doi:10.1002/prot.25468

5. Parisien, M. & Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452, 51–55 (2008).