Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper presents SARNA-Predict-pk, an algorithm for pseudoknotted RNA secondary structure prediction based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and incorporates a new thermodynamic model into the algorithm, effectively enabling it to predict pseudo-knotted RNA structures. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms. We measured the sensitivity and specificity using five prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. The other two are heuristic algorithms: SARNA-Predict-pk and HotKnots algorithms. An evaluation for the performance of SARNA-Predict-pk in terms of prediction accuracy was verified with native structures. Experiments on ten individual known structures from six RNA classes (tRNA, viral RNA, anti-genomic HDV, telomerase RNA, tmRNA, and RNaseP) were performed. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy.
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