Using sequence signatures and kink-turn motifs in knowledge-based statistical potentials for RNA structure prediction

Cigdem Sevim Bayrak, Namhee Kim, Tamar Schlick

Research output: Contribution to journalArticle

Abstract

Kink turns are widely occurring motifs in RNA, located in internal loops and associated with many biological functions including translation, regulation and splicing. The associated sequence pattern, a 3-nt bulge and G-A, A-G base-pairs, generates an angle of 50 along the helical axis due to Aminor interactions. The conserved sequence and distinct secondary structures of kink-turns (k-turn) suggest computational folding rules to predict kturn- like topologies from sequence. Here, we annotate observed k-turn motifs within a non-redundant RNA dataset based on sequence signatures and geometrical features, analyze bending and torsion angles, and determine distinct knowledge-based potentials with and without k-turn motifs. We apply these scoring potentials to our RAGTOP (RNA-As-Graph- Topologies) graph sampling protocol to construct and sample coarse-grained graph representations of RNAs from a given secondary structure. We present graph-sampling results for 35 RNAs, including 12 k-turn and 23 non k-turn internal loops, and compare the results to solved structures and to RAGTOP results without special k-turn potentials. Significant improvements are observed with the updated scoring potentials compared to the k-turn-free potentials. Because k-turns represent a classic example of sequence/structure motif, our study suggests that other such motifs with sequence signatures and unique geometrical features can similarly be utilized for RNA structure prediction and design.

Original languageEnglish (US)
Pages (from-to)5414-5422
Number of pages9
JournalNucleic acids research
Volume45
Issue number9
DOIs
StatePublished - May 19 2017

ASJC Scopus subject areas

  • Genetics

Fingerprint Dive into the research topics of 'Using sequence signatures and kink-turn motifs in knowledge-based statistical potentials for RNA structure prediction'. Together they form a unique fingerprint.

  • Cite this