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Algorithmes pour la prédiction in silico d'interactions par similarité entre macromolécules biologiques

Abstract : The action of a drug, or another small biomolecule, is induced by chemical interactions with other macromolecules such as proteins regulating the cell functions. The determination of the set of targets, the macromolecules that could bind the same small molecule, is essential in order to understand molecular mechanisms responsible for the effects of a drug. Indeed, this knowledge could help the drug design process so as to avoid side effects or to find new applications for known drugs. The advances of structural biology provides us with three-dimensional representations of many proteins involved in these interactions, motivating the use of in silico tools to complement or guide further in vitro or in vivo experiments which are both more expansive and time consuming.This research is conducted as part of a collaboration between the DAVID laboratory of the Versailles-Saint-Quentin University, and Bionext SA which offers a software suite to visualize and analyze chemical interactions between biological molecules. The objective is to design an algorithm to predict these interactions for a given compound, using the structures of potential targets. More precisely, starting from a known interaction between a drug and a protein, a new interaction can be inferred with another sufficiently similar protein. This approach consists in the search of a given pattern, the known binding site, across a collection of macromolecules.An algorithm was implemented, BioBind, which rely on a topological representation of the surface of the macromolecules based on the alpha shapes theory. Our surface representation allows to define a concept of region of any shape on the surface. In order to tackle the search of a given pattern region, a heuristic has been developed, consisting in the definition of regular region which is an approximation of a geodesic disk. This circular shape allows for an exhaustive sampling and fast comparison, and any circular region can then be extended to the actual pattern to provide a similarity evaluation with the query binding site.The target prediction problem is formalized as a binary classification problem, where a set of macromolecules is being separated between those predicted to interact and the others, based on their local similarity with the known target. With this point of view, classic metrics can be used to assess performance, and compare our approach with others. Three datasets were used, two of which were extracted from the literature and the other one was designed specifically for our problem emphasizing the pharmacological relevance of the chosen molecules. Our algorithm proves to be more efficient than another state-of-the-art similarity based approach, and our analysis confirms that docking software are not relevant for our target prediction problem when a first target is known, according to our metric.
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Mathieu Voland. Algorithmes pour la prédiction in silico d'interactions par similarité entre macromolécules biologiques. Algorithme et structure de données [cs.DS]. Université Paris-Saclay, 2017. Français. ⟨NNT : 2017SACLV014⟩. ⟨tel-01692802⟩

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