Prédiction de structure tridimensionnelle de molécules d’ARN par minimisation de regret

Abstract : The functions of RNA molecules in cellular processes are related very closely to its three dimensional structure. It is thus essential to predict the structure for understanding RNA functions. This folding can be seen as a two-step process: the formation of a secondary structure and the formation of three-dimensional structure. This first step is the results of strong interactions between nucleotides, and the second one is obtain by the tertiary interactions. Predicting the secondary structure is well-known and results in numerous advances since thirty years. However, predicting the three-dimensional structure is a more difficult problem due to the high number of possibility. To overcome this problem, we decided to see the folding of the RNA structure as a game. The secondary structure of the RNA is represented as a graph: its corresponds to a coarse-grained modeling of this structure. This modeling allows us to fold the RNA molecule in a discrete space. Our hypothesis is to understand the 3D structure like an equilibrium in game theory. To find this equilibrium, we will use regret minimization algorithms. We also study different formalizations of the game, based on biological statistics. The objective of this work is to develop a method of RNA folding which will work on all types of secondary structures and results more accurate than current approaches. Our method, called GARN, reached the expected objectives and allowed us to deepen the interesting factors for coarse-grained structure prediction on molecules.
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Mélanie Boudard. Prédiction de structure tridimensionnelle de molécules d’ARN par minimisation de regret. Informatique et théorie des jeux [cs.GT]. Université Paris-Saclay, 2016. Français. ⟨NNT : 2016SACLV026⟩. ⟨tel-01315684⟩

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