title: All-atom knowledge-based potential for RNA structure prediction and assessment.
abstract: BACKGROUND
  Over the recent years, the vision that RNA simply serves as information transfer molecule has dramatically changed. The study of the sequence/structure/function relationships in RNA is becoming more important. As a direct consequence, the total number of experimentally solved RNA structures has dramatically increased and new computer tools for predicting RNA structure from sequence are rapidly emerging. Therefore, new and accurate methods for assessing the accuracy of RNA structure models are clearly needed.
  
  
  RESULTS
  Here, we introduce an all-atom knowledge-based potential for the assessment of RNA three-dimensional (3D) structures. We have benchmarked our new potential, called Ribonucleic Acids Statistical Potential (RASP), with two different decoy datasets composed of near-native RNA structures. In one of the benchmark sets, RASP was able to rank the closest model to the X-ray structure as the best and within the top 10 models for ∼93 and ∼95% of decoys, respectively. The average correlation coefficient between model accuracy, calculated as the root mean square deviation and global distance test-total score (GDT-TS) measures of C3' atoms, and the RASP score was 0.85 and 0.89, respectively. Based on a recently released benchmark dataset that contains hundreds of 3D models for 32 RNA motifs with non-canonical base pairs, RASP scoring function compared favorably to ROSETTA FARFAR force field in the selection of accurate models. Finally, using the self-splicing group I intron and the stem-loop IIIc from hepatitis C virus internal ribosome entry site as test cases, we show that RASP is able to discriminate between known structure-destabilizing mutations and compensatory mutations.
  
  
  BACKGROUND
  RASP can be readily applied to assess all-atom or coarse-grained RNA structures and thus should be of interest to both developers and end-users of RNA structure prediction methods. The computer software and knowledge-based potentials are freely available at http://melolab.org/supmat.html.
  
  
  BACKGROUND
  fmelo@bio.puc.cl; mmarti@cipf.es
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Principe Felipe, 46012 Valencia, Spain.
authors.forename: Emidio
authors.initials: E
authors.lastname: Capriotti
authors.forename: Tomas
authors.initials: T
authors.lastname: Norambuena
authors.forename: Marc A
authors.initials: MA
authors.lastname: Marti-Renom
authors.forename: Francisco
authors.initials: F
authors.lastname: Melo
mesh_terms: D003627:Data Interpretation, Statistical; D051188:Knowledge Bases; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D012984:Software
chemical_list: D012313:RNA