Poster abstracts
Poster number 33 submitted by Carlos Owusu Ansah
Indel-Creating Mutations Modulate the Location of Protein-Binding sites and Observed Sequence Variants on RNA Molecules
Carlos Owusu Ansah (Biophysics)
Abstract:
There is a growing list of tools and approaches designed to observe, predict and characterize RNA-protein interactions and their physiological significance [1-2]. However, there have been fewer efforts dedicated to describing the selective pressures that govern the location of protein binding sites on RNA molecules or the location of sequence variants in RNA molecules across a population. Using a simple model of protein-RNA binding interactions, we show that indels outside of a binding site can significantly alter protein-RNA interactions at the binding site by modifying secondary structure at the binding site. We demonstrate that the binding sites of the single-stranded RNA binding protein HuR (Human antigen R) tend to be located in regions that keep HuR-RNA interactions from being significantly altered by indel-inducing mutations outside the binding site. Furthermore, we show that indels that are commonly observed across the human species tend to be located in regions that reduce their impact on protein-RNA interactions. Together, these results suggest that the location of binding sites and commonly observed indels are, to large extent, driven by the need to minimize disruptions created by indels. These observations are consistent with evolutionary theory — The observed bias in the location of naturally observed indels probably occurs because indels that create significant differences in binding affinity between the two alleles are more likely to create a relative adaptive advantage for one allele, causing it to outcompete and eventually replace the other. Similarly, the observed bias in the distribution of binding sites may be because RNA-protein interactions at physiologically significant binding sites that are easily disrupted by nearby mutations are less likely to provide a fitness advantage in the long term.
References:
Pan, Xiaoyong, Yang Yang, Chun-Qiu Xia, Aashiq H. Mirza, and Hong-Bin Shen. “Recent Methodology Progress of Deep Learning for RNA–Protein Interaction Prediction.” *WIREs RNA* 10, no. 6 (2019): e1544. [https://doi.org/10.1002/wrna.1544](https://doi.org/10.1002/wrna.1544).
Ramanathan, Muthukumar, Douglas F. Porter, and Paul A. Khavari. “Methods to Study RNA–Protein Interactions.” *Nature Methods* 16, no. 3 (March 2019): 225–34. [https://doi.org/10.1038/s41592-019-0330-1](https://doi.org/10.1038/s41592-019-0330-1).
Keywords: Protein, RNA, Natural Selection