Reducing Folding Scenario Candidates in Pseudoknots Detection Using PLMM DPSS Algorithm Integrated With Energy Filters

摘要

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.