Predicting RNA 3D structure using a coarse-grain helix-centered model

Abstract

A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a finegrain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures.

Sequence, Structure and Context Preferences of Human RNA Binding Proteins

“…As particular RNA structures are known to affect RBP binding in pre-mRNA splicing (Warf and Berglund, 2010), mRNA decay (Goodarzi et al, 2012), and mRNA localization (Rabani et al, 2008), we classified each RNA base as being part of a stem, hairpin loop, interior loop, or multiloop (Kerpedjiev et al, 2015). Averaging over all 78 proteins, we found that in pulldown sequences, top RBNS 6mers were present in hairpin loops about twice as often as all 6mers and in stems approximately half as often ( Fig.…”

smallrnaseq: short non coding RNA-Seq analysis with Python

“…Characteristics of the hairpin structure were encoded as features using the forgi package 22 . The features used in our algorithm are largely the same as those used in miRanalyzer and listed in Table 3 of the original paper 15 .…”

Progress and Current Challenges in Modeling Large RNAs

“…For example, NAST [51] and YUP [52] represent each nucleotide as a single pseudo atom, Vfold [53], SimRNA [54] and iFoldRNA [55] define each nucleotide using three pseudo atoms, CG [56] represents each nucleotide with five pseudo atoms, and HiRE-RNA [57] uses six pseudo atoms to represent purines and seven for pyrimidines. In addition, there are methods that represent RNA as graphs, which further reduces the sampling space by representing each helix as a stick or a cylinder [58, 59]. The accuracy of coarse-grained methods depends on the choice of representation and the scoring function.…”