Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters

Abstract

Understanding the function of complex RNA molecules depends critically on understanding their structure. However, creating three-dimensional (3D) structural models of RNA remains a significant challenge. We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures. We demonstrate NAST's capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures. Representative structures in the best ranking clusters averaged 8.0 +/- 0.3 A and 16.3 +/- 1.0 A RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively. The coarse-grained resolution allows us to model large molecules such as the 158-residue P4-P6 or the 388-residue T. thermophila group I intron. One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data. We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6. Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel-Westhof model of the T. thermophila group I intron, creating an integrated model of the entire molecule. Our software package is freely available at https://simtk.org/home/nast.

FIGURE 1.

The NAST energy function. (A) NAST uses a coarse-grained representation of one point per residue centered at the C3′ atom (approximately to scale). (B) Distributions of distances, angles, and dihedrals between consecutive C3′ atoms observed in ribosomal RNA (bold) and generated by the NAST energy function (dashed). (C) Illustration of geometric constraints for nonhelical regions in the NAST energy function. (D) Geometric constraints used in helical regions by the NAST energy function.

FIGURE 2.

NAST modeling of tRNA and P4-P6. The tRNA (A) and P4-P6 (B) crystal structures and representative structures from each of the clusters of coarse-grained models generated by NAST.

FIGURE 3.

Sensitivity of P4-P6 modeling to percentage of wrong base pairs in the specified secondary structure. Mean and standard deviation of RMSD (A) and GDT-TS (B) values for structures generated using 0% (crystal structure), 15%, 26% (predicted secondary structure), and 35% wrong base pairs. We observe no significant effect on the quality of structures generated in this range of wrong base-pair percentages.

FIGURE 4.

NAST manipulation of large RNA structures. Coarse-grained addition of missing loops (pink) in Twort (A) and Azoarcus (B) ribozymes. (C) Crystal of structure of T. thermophila group I intron with missing helices. (D) Michel–Westhof model of T. thermophila group I intron. (Teal) Residues for which crystallographic information is available. (E) Incorporation of crystallographic information (gray) into the Michel–Westhof model of T. thermophila group I intron.