Dataset Information

Structural prediction of RNA switches using conditional base-pair probabilities.

ABSTRACT: An RNA switch triggers biological functions by toggling between two conformations. RNA switches include bacterial riboswitches, where ligand binding can stabilize a bound structure. For RNAs with only one stable structure, structural prediction usually just requires a straightforward free energy minimization, but for an RNA switch, the prediction of a less stable alternative structure is often computationally costly and even problematic. The current sampling-clustering method predicts stable and alternative structures by partitioning structures sampled from the energy landscape into two clusters, but it is very time-consuming. Instead, we predict the alternative structure of an RNA switch from conditional probability calculations within the energy landscape. First, our method excludes base pairs related to the most stable structure in the energy landscape. Then, it detects stable stems ("seeds") in the remaining landscape. Finally, it folds an alternative structure prediction around a seed. While having comparable riboswitch classification performance, the conditional-probability computations had fewer adjustable parameters, offered greater predictive flexibility, and were more than one thousand times faster than the sampling step alone in sampling-clustering predictions, the competing standard. Overall, the described approach helps traverse thermodynamically improbable energy landscapes to find biologically significant substructures and structures rapidly and effectively.

SUBMITTER: Manzourolajdad A

PROVIDER: S-EPMC6561571 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Structural prediction of RNA switches using conditional base-pair probabilities.

Manzourolajdad Amirhossein A Spouge John L JL

PloS one 20190612 6

An RNA switch triggers biological functions by toggling between two conformations. RNA switches include bacterial riboswitches, where ligand binding can stabilize a bound structure. For RNAs with only one stable structure, structural prediction usually just requires a straightforward free energy minimization, but for an RNA switch, the prediction of a less stable alternative structure is often computationally costly and even problematic. The current sampling-clustering method predicts stable and ...[more]

PMID: 31188853

Similar Datasets

Project description:BACKGROUND:To uncover molecular functions and networks in biological cellular systems, it is important to dissect interactions between proteins and RNAs. Many studies have been performed to investigate and analyze interactions between protein amino acid residues and RNA bases. In terms of interactions between residues in proteins, it is generally accepted that an amino acid residue at interacting sites has coevolved together with the partner residue in order to keep the interaction between residues in proteins. Based on this hypothesis, in our previous study to identify residue-residue contact pairs in interacting proteins, we made calculations of mutual information (M I) between amino acid residues from some multiple sequence alignment of homologous proteins, and combined it with a discriminative random field (DRF) approach, which is a special type of conditional random fields (CRFs) and has been proved useful for the purpose of extracting distinguishing areas from a photograph in the image processing field. Recently, the evolutionary correlation of interactions between residues and DNA bases has also been found in certain transcription factors and the DNA-binding sites. RESULTS:In this paper, we employ more generic two-dimensional CRFs than such DRFs to predict interactions between protein amino acid residues and RNA bases. In addition, we introduce labels representing kinds of amino acids and bases as local features of a CRF. Furthermore, we examine the utility of L1-norm regularization (lasso) for the CRF. For evaluation of our method, we use residue-base interactions between several Pfam domains and Rfam entries, conduct cross-validation, and calculate the average AUC (Area under ROC Curve) score. The results suggest that our CRF-based method using mutual information and labels with the lasso is useful for further improving the performance, especially provided that the features of CRF are successfully reduced by the lasso approach. CONCLUSIONS:We propose simple and generic two-dimensional CRF models using labels and mutual information with the lasso. Use of the CRF-based method in combination with the lasso is particularly useful for predicting the residue-base contacts in protein-RNA interactions.

Dataset Information

Structural prediction of RNA switches using conditional base-pair probabilities.

Publications

Structural prediction of RNA switches using conditional base-pair probabilities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets