3dRNAscore: A distance and torsion angle dependent evaluation function of 3D RNA structures

Jian Wang, Yunjie Zhao, Chunyan Zhu, Yi Xiao

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

Original languageEnglish (US)
Article numbere63
JournalNucleic acids research
Volume43
Issue number10
DOIs
StatePublished - Feb 6 2015

All Science Journal Classification (ASJC) codes

  • Genetics

Fingerprint

Dive into the research topics of '3dRNAscore: A distance and torsion angle dependent evaluation function of 3D RNA structures'. Together they form a unique fingerprint.

Cite this