RCPred: RNA complex prediction as a constrained maximum weight clique problem

Abstract : Background: RNAs can interact and form complexes, which have various biological roles. The secondary structure prediction of those complexes is a first step towards the identification of their 3D structure. We propose an original approach that takes advantage of the high number of RNA secondary structure and RNA-RNA interaction prediction tools. We formulate the problem of RNA complex prediction as the determination of the best combination (according to the free energy) of predicted RNA secondary structures and RNA-RNA interactions. Results: We model those predicted structures and interactions as a graph in order to have a combinatorial optimization problem that is a constrained maximum weight clique problem. We propose an heuristic based on Breakout Local Search to solve this problem and a tool, called RCPred, that returns several solutions, including motifs like internal and external pseudoknots. On a large number of complexes, RCPred gives competitive results compared to the methods of the state of the art. Conclusions: We propose in this paper a method called RCPred for the prediction of several secondary structures of RNA complexes, including internal and external pseudoknots. As further works we will propose an improved computation of the global energy and the insertion of 3D motifs in the RNA complexes.
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Contributor : Frédéric Davesne <>
Submitted on : Thursday, December 19, 2019 - 10:29:06 PM
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  • HAL Id : hal-02420493, version 1


Audrey Legendre, Eric Angel, Fariza Tahi. RCPred: RNA complex prediction as a constrained maximum weight clique problem. 17th Asia Pacific Bioinformatics Conference (APBC 2019), Jan 2019, Wuhan,, China. ⟨hal-02420493⟩



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