Knowledge-based instantiation of full atomic detail into coarse-grain RNA 3D structural models

Motivation: The recent development of methods for modeling RNA 3D structures using coarse-grain approaches creates a need to bridge low- and high-resolution modeling methods. Although they contain topological information, coarse-grain models lack atomic detail, which limits their utility for some applications

Triple Helical Structure and Stabilization of Collagen-like Molecules with 4(R)-Hydroxyproline in the Xaa Position

In this study, we examine the relationships between the structure and stability of five related collagen-like molecules that have hydroxyproline residues occupying positions not observed in vertebrate collagen. Two of the molecules contain valine or threonine and form stable triple helices in water. Three of the molecules contain allo-threonine (an enantiomer of threonine), serine, or alanine, and are not stable. Using molecular dynamics simulation methods, we examine possible explanations for the stability difference, including considering the possibility that differences in solvent shielding of the essential interchain hydrogen bonds may result in differences in stability. By comparing the structures of threonine- and allo-threonine-containing molecules in six polar and nonpolar solvation conditions, we find that solvent shielding is not an adequate explanation for the stability difference. A closer examination of the peptides shows that the structures of the unstable molecules are looser, having weaker intermolecular hydrogen bonds. The weakened hydrogen bonds result from extended Yaa residue Ψ-angles that prevent optimal geometry. The Φ−Ψ-maps of the relevant residues suggest that each residue's most favorable Ψ-angle determines the corresponding collagen-like molecule's stability. Additionally, we propose that these molecules illustrate a more general feature of triple-helical structures: interchain hydrogen bonds are always longer and weaker than ideal, so they are sensitive to relatively small changes in molecular structure. This sensitivity to small changes may explain why large stability differences often result from seemingly small changes in residue sequence

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

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 Å and 16.3 ± 1.0 Å 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