Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

• DOI: 10.1261/rna.2543711
• Publication Date: 2011-04-27T01:54:27Z
• Authors: Julie Bernauer; Xuhui Huang; Adelene Y.L. Sim; Michael Levitt
• Keywords: Models, Molecular; 570; 0303 health sciences; [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM]; Molecular Biology/Structural Biology [q-bio.BM]; [SDV.BBM.BS] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM]; Knowledge Bases; 612; Molecular Dynamics Simulation; 540; Crystallography, X-Ray; [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry; [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM]; 03 medical and health sciences; Knowledge-based potential; Nucleic Acid Conformation; RNA; RNA structure; Scoring

RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA—in particular the nonhelical regions—is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.