Dataset Information

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models.

ABSTRACT: RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.

SUBMITTER: Kulkarni M

PROVIDER: S-EPMC10473517 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models.

Kulkarni Mandar M Thangappan Jayaraman J Deb Indrajit I Wu Sangwook S

PloS one 20230901 9

RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with ...[more]

PMID: 37656749

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Project description:BACKGROUND: Since RNA molecules regulate genes and control alternative splicing by allostery, it is important to develop algorithms to predict RNA conformational switches. Some tools, such as paRNAss, RNAshapes and RNAbor, can be used to predict potential conformational switches; nevertheless, no existent tool can detect general (i.e., not family specific) entire riboswitches (both aptamer and expression platform) with accuracy. Thus, the development of additional algorithms to detect conformational switches seems important, especially since the difference in free energy between the two metastable secondary structures may be as large as 15-20 kcal/mol. It has recently emerged that RNA secondary structure can be more accurately predicted by computing the maximum expected accuracy (MEA) structure, rather than the minimum free energy (MFE) structure. RESULTS: Given an arbitrary RNA secondary structure S? for an RNA nucleotide sequence a = a?,..., a(n), we say that another secondary structure S of a is a k-neighbor of S?, if the base pair distance between S? and S is k. In this paper, we prove that the Boltzmann probability of all k-neighbors of the minimum free energy structure S? can be approximated with accuracy ? and confidence 1 - p, simultaneously for all 0 ? k < K, by a relative frequency count over N sampled structures, provided that N>N(?,p,K)=??¹(p/2K)²/4?², where ?(z) is the cumulative distribution function (CDF) for the standard normal distribution. We go on to describe the algorithm RNAborMEA, which for an arbitrary initial structure S? and for all values 0 ? k < K, computes the secondary structure MEA(k), having maximum expected accuracy over all k-neighbors of S?. Computation time is O(n³ · K²), and memory requirements are O(n² · K). We analyze a sample TPP riboswitch, and apply our algorithm to the class of purine riboswitches. CONCLUSIONS: The approximation of RNAbor by sampling, with rigorous bound on accuracy, together with the computation of maximum expected accuracy k-neighbors by RNAborMEA, provide additional tools toward conformational switch detection. Results from RNAborMEA are quite distinct from other tools, such as RNAbor, RNAshapes and paRNAss, hence may provide orthogonal information when looking for suboptimal structures or conformational switches. Source code for RNAborMEA can be downloaded from http://sourceforge.net/projects/rnabormea/ or http://bioinformatics.bc.edu/clotelab/RNAborMEA/.

Dataset Information

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models.

Publications

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models.

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