Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

Abstract

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.

Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an integer programming framework

“…The IP programs remains a promising direction for RNA structure determination due to the flexibility of their formulation allowing to go above the nearest neighbor model. Expending to more complex conserved structures, as groups of interacting and conserved loops containing pseudoknots described in Carnaval ( Reinharz et al 2018 , Soulé et al 2021 ) would allow to take fully advantage of the IP formulation and extend the notion of pseudoknots prediction to all noncanonical interactions. This flexible formulation will also allow to give specific rules to help incorporate chemical modifications and other features that are absent from the nearest neighbor model.…”

“…In this paper, we expand on our IP framework RNAMoIP ( Reinharz et al 2012 ) to achieve the simultaneous prediction of secondary structure with pseudoknots and structural motifs insertion with or without alignments incorporating ideas from IPknot ( Sato et al 2011 ), using a newly designed local structural modules dataset computed from Soulé et al (2021 ).…”

Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an Integer Programming framework

“…The IP programs remains a promising direction for RNA structure determination due to the flexibility of their formulation allowing to go above the nearest neighbour model. Expending to more complex conserved structures, as groups of interacting and conserved loops containing pseudoknots described in Carnaval [21, 25] would allow to take fully advantage of the IP formulation and extend the notion of pseudoknots prediction to all non-canonical interactions. This flexible formulation will also allow to give specific rules to help incorporate chemical modifications and other features that are absent from the nearest neighbour model.…”

“…Previous work used conserved structural motifs to select an optimal secondary structure and ease all-atoms 3D reconstruction [20, 21]. In this paper we expend on that IP framework to achieve the simultaneous prediction of secondary structure with pseudoknots and structural motifs insertion incorporating ideas from IPknot [14] and using a newly designed local structural modules dataset computed from [22].…”

“…In this paper we expand on our IP framework RNAMoIP [22] to achieve the simultaneous prediction of secondary structure with pseudoknots and structural motifs insertion with or without alignments incorporating ideas from IPknot [15], using a newly designed local structural modules dataset computed from [25].…”