Prediction of RNA Pseudoknots Using Heuristic Modeling with Mapping and Sequential Folding

Abstract

Predicting RNA secondary structure is often the first step to determining the structure of RNA. Prediction approaches have historically avoided searching for pseudoknots because of the extreme combinatorial and time complexity of the problem. Yet neglecting pseudoknots limits the utility of such approaches. Here, an algorithm utilizing structure mapping and thermodynamics is introduced for RNA pseudoknot prediction that finds the minimum free energy and identifies information about the flexibility of the RNA. The heuristic approach takes advantage of the 5′ to 3′ folding direction of many biological RNA molecules and is consistent with the hierarchical folding hypothesis and the contact order model. Mapping methods are used to build and analyze the folded structure for pseudoknots and to add important 3D structural considerations. The program can predict some well known pseudoknot structures correctly. The results of this study suggest that many functional RNA sequences are optimized for proper folding. They also suggest directions we can proceed in the future to achieve even better results.

A Local Structural Prediction Algorithm for RNA Triple Helix Structure

“…In the second experiment, we try the whole pipeline for triple helix prediction on RNA sequences. Given an RNA sequence, the pseudoknotted structure will first be predicted by vsfold5 [19]. Then for those pseudoknotted regions reported by the tools, our tool predicts the triple helix structure.…”

Sense overlapping transcripts in IS1341-type transposase genes are functional non-coding RNAs in archaea

“…RNA secondary structure computational analysis RNA sequences of all 10 sotRNAs and 2 intergenic ncRNAs were used for structural predictions using 5 different methods: RNAfold-MFE, which predicts the Minimum Free Energy (MFE) structure; RNAfold using thermodynamic partition function centroids algorithm 34 ; ContextFold, which uses a very large number of parameters in a machine learning approach 35 ; Vsfold5, which allow the prediction of pseudoknots by using sequential (5 0 to 3 0 ) folding and thermodynamically most-probable folding pathways 36 and Cylofold, which simulates the 3D folding process in a coarse-grain model. 37 All predictions are shown in extended dot-bracket format to include pseudoknots.…”

A new entropy model for RNA: part IV, The Minimum Free Energy (mFE) and the thermodynamically most-probable folding pathway (TMPFP)

“…Familiar examples are mfold [6,7] and RNAfold [8,9]. In this series, we have been exploring the prediction behavior of an entropy model that attempts to address the collective elastic response of multiple contact points in a folded RNA molecule, called the cross linking entropy (CLE) model [10,11]. All implementations of the CLE model also utilize a form of the DPA to compute either the optimal structure (vsfold5) or suboptimal structures (vs_subopt).…”

“…To understand this discussion, the reader should be familiar with the concept of the cross linking entropy (CLE) discussed in Parts I through III and explained in the literature in Refs [10,11], and particularly in relation to pseudoknots [10]. In particular, it is important to understand the definition of an "effective mer", the Kuhn length (or, in other parlance, the persistence length), and the general equations used to describe this entropy.…”

“…Effective mers are used here because the global entropy and therefore the folding is a function that is largely dependent on the Kuhn length where the individual behavior of the monomers is grouped into these effective mers. The reader also needs to be familiar with the programming issues of applying optimization algorithms in the context of RNA or protein folding [10,31]. (A basic introduction to the concepts behind the CLE model can be found in Section 2 and its implementation using the DPA in Section 3.…”