rRNA Platform Technology for Drug Discovery Methods for Identifying Ligands That Target Plasmodium RNA Structural Motifs

Determining the structure of the P. falciparum40s leads to better understanding of the structural basis for its protein-synthesizing roles in the cell. This enables researchers in the field of drug development to run in silico ligand screening experiments using the solved P. falciparum 40S structure as a target against a library of potential anti-malarial compounds. Drug leads identified through this method can lead to further biochemical and In vitro binding studies with the ultimate goal of developing new class of anti-malarial drugs. The use of structure prediction and modeling technologies in this study dramatically reduces the time it takes from target identification to drug lead determination. Furthermore, very many compounds that were previously incapable of being experimentally tested can now be tested in silico against the generated structure. Owing to the increasing utility of bioinformatics and three dimensional structural modeling software, one can accurately build physical models solely from sequence data by unwrapping the information therein on probable motif sites capable of being anchored onto available compounds or aptamers

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

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.Les fonctions d'une molécule d'ARN dans les processus cellulaires sont très étroitement liées à sa structure tridimensionnelle. Il est donc essentiel de pouvoir prédire cette structure pour étudier sa fonction. Le repliement de l'ARN peut être vu comme un processus en deux étapes : le repliement en structure secondaire, grâce à des interactions fortes, puis le repliement en structure tridimensionnelle par des interactions tertiaires. Prédire la structure secondaire a donné lieu à de nombreuses avancées depuis plus de trente ans. Toutefois, la prédiction de la structure tridimensionnelle est un problème bien plus difficile. Nous nous intéressons ici au problème de prédiction de la structure 3D d'ARN sous la forme d'un jeu. Nous représentons la structure secondaire de l'ARN comme un graphe : cela correspond à une modélisation à gros grain de cette structure. Cette modélisation permet de réaliser un jeu de repliement dans l'espace. Notre hypothèse consiste à voir la structure 3D comme un équilibre en théorie des jeux. Pour atteindre cet équilibre, nous utiliserons des algorithmes de minimisation de regret. Nous étudierons aussi différentes formalisations du jeu, basées sur des statistiques biologiques. L'objectif de ce travail est de développer une méthode de repliement d'ARN fonctionnant sur tous les types de molécule d'ARN et obtenant des structures similaires aux molécules réelles. Notre méthode, nommée GARN, a atteint les objectifs attendus et nous a permis d'approfondir l'impact de certains paramètres pour la prédiction de structure à gros grain des molécules