Pacific Symposium on Biocomputing 15:216-227(2010) PREDICTING RNA STRUCTURE BY MULTIPLE TEMPLATE HOMOLOGY MODELING

87 citations recorded by the source corpus.

Despite the importance of 3D structure to understand the myriad functions of RNAs in cells, most RNA molecules remain out of reach of crystallographic and NMR methods. However, certain structural information such as base pairing and some tertiary contacts can be determined readily for many RNAs by bioinformatics or relatively low cost experiments. Further, because RNA structure is highly modular, it is possible to deduce local 3D structure from the solved structures of evolutionarily related RNAs or even unrelated RNAs that share the same module. RNABuilder is a software package that generates model RNA structures by treating the kinematics and forces at separate, multiple levels of resolution. Kinematically, bonds in bases, certain stretches of residues, and some entire molecules are rigid while other bonds remain flexible. Forces act on the rigid bases and selected individual atoms. Here we use RNABuilder to predict the structure of the 200-nucleotide Azoarcus group I intron by homology modeling against fragments of the distantly-related Twort and Tetrahymena group I introns and by incorporating base pairing forces where necessary. In the absence of any information from the solved Azoarcus intron crystal structure, the model accurately depicts the global topology, secondary and tertiary connections, and gives an overall RMSD value of 4.6 Å relative to the crystal structure. The accuracy of the model is even higher in the intron core (RMSD = 3.5 Å), whereas deviations are modestly larger for peripheral regions that differ more substantially between the different introns. These results lay the groundwork for using this approach for larger and more diverse group I introns, as well for still larger RNAs and RNA-protein complexes such as group II introns and the ribosomal subunits.

Subfields: Molecular Biology

Citations in the corpus

• Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, neutral): “MSAs have been shown to provide additional information helpful for protein modeling and this may be similarly true for RNAs. template-based modeling, such as ModeRNA 8 and RNAbuilder 9 , which are constrained by limited template libraries, and de novo prediction approaches, including FARFAR2 (ref.”
• Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “For example, methods such as ModeRNA 2 and RNABuilder 3 extract structure information from previously solved homologous structural templates.”
• Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, methodology): “Homology modeling such as RNABuilder 5 and ModeRNA 6 attempts to predict the structure of a target RNA by mapping the query sequence onto the template structure(s) of its homologs.”
• SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction (2015, background): “, by comparative modeling (6,54) or by sampling of helical topologies (55,56), and the conformational space to be sampled may be restricted by the use of additional restraints (57).”
• Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, methodology): “The minimization is done in MacroMoleculeBuilder (MMB), a general-purpose internal coordinate mechanics code also known for RNA folding (Flores and Altman 2010), homology modeling (Flores et al. 2010), morphing (Tek et al.”
• ModeRNA: a tool for comparative modeling of RNA 3D structure (2011, methodology): “In the publication of the RNABuilder tool, Altman and coworkers (51) defined four criteria that a comparative modeling program should fulfill: (i) allowing the user to specify correspondences between residues in the target sequence and residues in the template structure, (ii) allowing to use more th”
• RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. (2012, methodology): “These features have been used for RNA threading (Flores et al. 2010) and for generating an all-atoms trajectory of ribosomal hybridization using structural and biochemical information (Flores and Altman 2011).”