Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction

Abstract

<jats:title>Abstract</jats:title> <jats:p>An RNA global fold can be described at the level of helix orientations and relatively flexible loop conformations that connect the helices. The linkage between the helices plays an essential role in determining the structural topology, which restricts RNA local and global folds, especially for RNA tertiary structures involving cross-linked base pairs. We quantitatively analyze the topological constraints on RNA 3D conformational space, in particular, on the distribution of helix orientations, for pseudoknots and loop-loop kissing structures. The result shows that a viable conformational space is predominantly determined by the motif type, helix size, and loop size, indicating a strong topological coupling between helices and loops in RNA tertiary motifs. Moreover, the analysis indicates that (cross-linked) tertiary contacts can cause much stronger topological constraints on RNA global fold than non-cross-linked base pairs. Furthermore, based on the topological constraints encoded in the 2D structure and the 3D templates, we develop a 3D structure prediction approach. This approach can be further combined with structure probing methods to expand the capability of computational prediction for large RNA folds.</jats:p>

Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement

“…Moreover, the related works from Thirumalai et al also indicated that small crowders can increase the stability of compact structures of human telomerase RNA to a greater extent than larger ones (Denesyuk and Thirumalai, 2011), and the crowders with attractive interactions with RNA bases can stabilize an RNA hairpin more than inert crowding agents of the same size (Pincus et al, 2008). In addition, the interaction between ions (especially multivalent ion) and RNAs can be influenced by crowders (Yu et al, 2016), which is beyond the implicit ion treatment presented here and is required to be addressed in the future work Chen, 2007, 2012;Xu and Chen, 2020).…”

FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures

“…As complementary methods, various computational models have been developed to predict RNA 3D structures in silico. These models can be roughly classified into physics-based ones such as SimRNA [15,16], iFold [17], NAST [18], IsRNA [19,20], HireRNA [21], oxRNA [22], and our model with salt effect [23][24][25][26], and fragment-assembly-based ones such as MC-Fold [27], FARNA/FARFAR [28][29][30], Vfold3D [31][32][33][34], RNAComposer [35,36] and 3dRNA [37][38][39][40][41][42]. The physics-based models are generally based on various coarse-grained (CG) representations and specific force fields, and their predictions generally involve long computational time and are still only reliable for the RNAs of small size and simple topology [6,9].…”

cgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

“…The physics-based models such as SimRNA [26,27], IsRNA [28][29][30], iFold [31], NAST [32], HiRE-RNA [33], and our model of salt effect [34][35][36][37][38][39][40], are generally based on coarsegrained (CG) representations, specified CG force fields, and certain conformation sampling strategies. The knowledgebased models such as MC-fold/MC-sym pipeline, FARNA [25], Vfold3D [41][42][43][44], RNAComposer [45,46], and 3d RNA [47,48], are generally based on various fragment libraries and fragment-assembly strategies. Generally, an RNA 3D structure prediction model generally generates a large number of 3D structure candidates for a target RNA, and consequently, a reliable statistical potential/scoring function is required to identify a structure closest to the native one [49,50].…”