Abstract
BackgroundComputational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. The goal of the refinement is not only to improve the local quality of the model but to bring it globally closer to the true structure.ResultsWe present QRNAS, a software tool for fine-grained refinement of nucleic acid structures, which is an extension of the AMBER simulation method with additional restraints. QRNAS is capable of handling RNA, DNA, chimeras, and hybrids thereof, and enables modeling of nucleic acids containing modified residues.ConclusionsWe demonstrate the ability of QRNAS to improve the quality of models generated with different methods. QRNAS was able to improve MolProbity scores of NMR structures, as well as of computational models generated in the course of the RNA-Puzzles experiment. The overall geometry improvement may be associated with increased model accuracy, especially on the level of correctly modeled base-pairs, but the systematic improvement of root mean square deviation to the reference structure should not be expected. The method has been integrated into a computational modeling workflow, enabling improved RNA 3D structure prediction.
Highlights
Computational models of Ribonucleic acid (RNA) 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations
We present QRNAS, a new software tool for fine-grained refinement of nucleic acid structures, dedicated to improving the quality of models generated by low- to medium-resolution methods commonly used, e.g., for RNA 3D structure modeling
QRNAS is capable of handling RNA, DNA or chimeras and hybrids thereof, and enables modeling of nucleic acids containing modified residues
Summary
Computational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. With the very rapid discovery of new classes of RNA molecules, new functions beyond storing genetic information are being discovered. The functions of RNA molecules and interactions of proteins, RNAs, and their complexes, often depend on their structure, which in turn is encoded in the linear sequence of ribonucleotide residues. Since the historically first prediction of tRNA 3D structure in 1969 [4], throughout the decades, numerous computational methods were developed to generate RNA 3D structure from sequence. The field of research on RNA structure prediction is quite advanced, and the advantages and limitations of different methods are known, in particular from the assessment within the RNA-Puzzles community-wide experiment [5,6,7], which has been inspired by the CASP experiment for protein structure prediction [8]
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