LCS-TA to identify similar fragments in RNA 3D structures

Jakub Wiedemann,Tomasz Zok,Maciej Milostan,Marta Szachniuk

doi:10.1186/s12859-017-1867-6

Abstract

BackgroundIn modern structural bioinformatics, comparison of molecular structures aimed to identify and assess similarities and differences between them is one of the most commonly performed procedures. It gives the basis for evaluation of in silico predicted models. It constitutes the preliminary step in searching for structural motifs. In particular, it supports tracing the molecular evolution. Faced with an ever-increasing amount of available structural data, researchers need a range of methods enabling comparative analysis of the structures from either global or local perspective.ResultsHerein, we present a new, superposition-independent method which processes pairs of RNA 3D structures to identify their local similarities. The similarity is considered in the context of structure bending and bonds’ rotation which are described by torsion angles. In the analyzed RNA structures, the method finds the longest continuous segments that show similar torsion within a user-defined threshold. The length of the segment is provided as local similarity measure. The method has been implemented as LCS-TA algorithm (Longest Continuous Segments in Torsion Angle space) and is incorporated into our MCQ4Structures application, freely available for download from http://www.cs.put.poznan.pl/tzok/mcq/.ConclusionsThe presented approach ties torsion-angle-based method of structure analysis with the idea of local similarity identification by handling continuous 3D structure segments. The first method, implemented in MCQ4Structures, has been successfully utilized in RNA-Puzzles initiative. The second one, originally applied in Euclidean space, is a component of LGA (Local-Global Alignment) algorithm commonly used in assessing protein models submitted to CASP. This unique combination of concepts implemented in LCS-TA provides a new perspective on structure quality assessment in local and quantitative aspect. A series of computational experiments show the first results of applying our method to comparison of RNA 3D models. LCS-TA can be used for identifying strengths and weaknesses in the prediction of RNA tertiary structures.

Highlights

In modern structural bioinformatics, comparison of molecular structures aimed to identify and assess similarities and differences between them is one of the most commonly performed procedures
The method has been implemented as LCS-TA algorithm (Longest Continuous Segments in Torsion Angle space) and incorporated into MCQ4Structures software
Results and discussion we present the results of LCS-TA experimental runs over selected RNA 3D structures

Summary

Introduction

Comparison of molecular structures aimed to identify and assess similarities and differences between them is one of the most commonly performed procedures It gives the basis for evaluation of in silico predicted models. A comparison of contents stored in NCBI Reference Sequence Database (RefSeq) [1] and Protein Data Bank (PDB) [2] brings to a conclusion that there is a large, ever-widening gap between the numbers of known sequences and structures of biomolecules Today, this gap is being filled with the use of computational methods that address the problem of RNA and protein 3D. When analyzing individual structures, finding their strengths and weaknesses, comparing substructures, or identifying motifs, a local assessment is necessary In such cases, local evaluation of the 3D model complements global analysis and significantly enhances our knowledge of the structure

Methods

Results

Conclusion