Predicting Helical Topologies in RNA Junctions as Tree Graphs

Abstract

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires knowledge of their tertiary structures. Though computational RNA folding approaches exist, they often require manual manipulation and expert intuition; predicting global long-range tertiary contacts remains challenging. Here we develop a computational approach and associated program module (RNAJAG) to predict helical arrangements/topologies in RNA junctions. Our method has two components: junction topology prediction and graph modeling. First, junction topologies are determined by a data mining approach from a given secondary structure of the target RNAs; second, the predicted topology is used to construct a tree graph consistent with geometric preferences analyzed from solved RNAs. The predicted graphs, which model the helical arrangements of RNA junctions for a large set of 200 junctions using a cross validation procedure, yield fairly good representations compared to the helical configurations in native RNAs, and can be further used to develop all-atom models as we show for two examples. Because junctions are among the most complex structural elements in RNA, this work advances folding structure prediction methods of large RNAs. The RNAJAG module is available to academic users upon request.

Graph-based sampling for approximating global helical topologies of RNA

“…These measures, along with our junction data-mining approach (RNAJAG) (23), are combined to develop a RAG-based graph sampling method for structure assembly (Fig. 1).…”

“…Given a 2D structure as input, we extend RAG tree graphs to represent all helical arrangements (e.g., parallel, antiparallel, perpendicular orientations) by adding vertices to helical ends. We scale each edge to represent helix lengths and sizes of unpaired regions and lock junction parts of initial graphs, if present, by predicting the three-way and four-way junction families by RNAJAG (23). The three-and four-way junctions are classified into families-A, B, and C for three-way, and H, cH, cL, cK, π, cW, ψ, cX, and X for four-way junctions-according to resulting topologies (SI Appendix, Fig.…”

“…The three-and four-way junctions are classified into families-A, B, and C for three-way, and H, cH, cL, cK, π, cW, ψ, cX, and X for four-way junctions-according to resulting topologies (SI Appendix, Fig. S2) (23). We then sample RNA 3D space by MC/SA guided by our knowledge-based statistical potentials (SI Appendix, Figs.…”

“…2B); setup of initial tree graphs based on size measures and junction topology predictions by RNAJAG (Fig. 1A) (23); Monte Carlo/Simulated Annealing (MC/SA) sampling of graphs (Fig. 1B); and candidate assessment-comparison of final graphs to translated graphs obtained from experiment or ensemble graphs generated in the absence of experimental references (Fig.…”

RNA structure prediction: Progress and perspective

“…However, due to the many degrees of freedom, it is still difficult to fold RNA 3D structures with these all-atomistic force fields even with modern computational facilities. Consequently, the models with the all-atomistic fragments assembling have been developed based on the known fragments or secondary structures, [68][69][70][71][72][73][74][75][76] such as MC-fold/Mc-Sym pipeline [68] , FARNA/FARFAR [69,70] , RSIM [71] and RNAnbds [73,74] .…”