LCS-TA to identify similar fragments in RNA 3D structures
PBN-AR
Instytucja
Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
Informacje podstawowe
Główny język publikacji
en
Czasopismo
BMC BIOINFORMATICS (35pkt w roku publikacji)
ISSN
1471-2105
EISSN
Wydawca
BIOMED CENTRAL LTD
DOI
URL
Rok publikacji
2017
Numer zeszytu
Strony od-do
Article Number: 456
Numer tomu
18
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Pozostali autorzy
+ 3
Słowa kluczowe
en
RNA 3D structure
Structure comparison
Local similarity
Torsion angles
Open access
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Creative Commons — Uznanie autorstwa-Niekomercyjne-Na tych samych warunkach
Czas opublikowania w otwartym dostępie
Razem z publikacją
Data udostępnienia w sposób otwarty
2017-10-23
Streszczenia
Język
en
Treść
Background: 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. 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. Results: Herein, 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/. Conclusions: The 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.
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System-identifier
PX-5a13fb7ad5de05f7f497f976
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