All-atom knowledge-based potential for RNA structure prediction and assessment

Motivation: 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...

• Type: Journal
• Year: 2011
• Where: BIOINFORMATICS
• Authors: Emidio Capriotti, Tomás Norambuena, Marc A. Martí-Renom, Francisco Melo
• Keywords: Artificial Intelligence, BIOINFORMATICS 2011, Near-native Rna Structures, RASP, Rna Structure