Reducing folding scenario candidates in pseudoknots detection using PLMM_DPSS algorithm integrated with energy filters

Pseudoknots are important functional structures in RNA. Despite of numerous pseudoknot prediction tools, biologists still need a pseudoknot detection method with much higher sensitivity. We have previously developed the Pseudoknot Local Motif Model and Dynamic Partner Sequence Stacking (PLMM_DPSS) algorithm which predicts short loop 2 pseudoknots with high sensitivity. Integrated with Mfold, PLMM_DPSS is capable of predicting both H-type and complicated type pseudoknots. In this study, we have developed PLMM_DPSS_SF_Mfold_FF with the following modifications: the extension of the PLM model to include long loop 2 pseudoknots, the incorporation of overall folding energy calculation, recombination of non-overlapping pseudoknots within one sequence and two filters for higher specificity, The prediction results have shown that PLMM_DPSS_SF_Mfold_FF is more sensitive than other leading pseudoknot prediction tools and results in a low alternative folding number, and results also support the notion that some kinetic barriers may trap RNA folding in local minimums.