Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein. (October 2015)

• Record Type: Journal Article
• Authors: Ellenbecker, Mary; St. Goddard, Jeremy; Sundet, Alec; Lanchy, Jean-Marc; Raiford, Douglas; Lodmell, J.Stephen
• Is Part Of: Computational biology and chemistry. Volume 58(2015)
• Page Start: 120
• Page End: 125
• Publication Date: 2015-10
• Subjects: RNA structure prediction -- Aptamers -- Rift Valley fever virus -- Nucleocapsid protein -- Viral inhibition; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.06.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A family of RNA aptamers selected for their ability to bind to a viral protein, but whose secondary structures were uncharacterized, was folded and numerically scored in silico . The predicted structural characteristics of randomly generated RNA sequences were pairwise compared to those of the bona fide aptamers. The novel in silico aptamers that clustered structurally using multidimensional scaling with the bona fide aptamers were synthesized in the laboratory for further investigation. The in silico aptamers displayed good binding characteristics to Rift Valley fever virus nucleocapsid protein in vitro . Several of the in silico aptamers exhibited antiviral activity when transfected into cells prior to infection with RVFV. Abstract: Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications.