As the rate of functional RNA sequence discovery escalates, high-throughput techniques for reliable structural determination are becoming crucial for revealing the essential features of these RNAs in a timely fashion. Computational predictions of RNA secondary structure quickly generate reasonable models but suffer from several approximations, including overly simplified models and incomplete knowledge of significant interactions. Similar problems limit the accuracy of predictions for other self-folding polymers, including DNA and peptide nucleic acid (PNA). The work presented here demonstrates that incorporating unassigned data from simple nuclear magnetic resonance (NMR) experiments into a dynamic folding algorithm greatly reduces the potential folding space of a given RNA and therefore increases the confidence and accuracy of modeling. This procedure has been packaged into an NMR-assisted prediction of secondary structure (NAPSS) algorithm that can produce pseudoknotted as well as non-pseudoknotted secondary structures. The method reveals a probable pseudoknot in the part of the coding region of the R2 retrotransposon from Bombyx mori that orchestrates second-strand DNA cleavage during insertion into the genome.