{'fraction_non_alphanumeric': 0.02066892145809846, 'fraction_numerical': 0.0018789928598271326, 'mean_word_length': 5.383693045563549, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Nouvelles méthodes de calcul pour la prédiction des interactions protéine-protéine au niveau structural. (Novel computational methods to predict protein-protein interactions on the structural level)

Drug discovery is a multidisciplinary field which includes molecular biology, biophysics, biochemistry, and pharmacology. It usually starts with the identification of a biological target which is known to play a critical role in a particular disease. Therein, computational methods are increasingly used in the structure-based drug design from target identification and validation to the designing of new molecules. To identify molecules that inhibit desirable activity hundreds of thousands candidates generated with docking protocols are virtually screened to filter out top-scoring hits. The latter are then tested in biological environment and many cycles of optimization are performed to obtain the candidates for further clinical trials. The first algorithm dedicated to the docking of small molecules was applied to find new candidates against HIV-1 protease in 1990. Since then, using of docking pipelines has become a standard practice in drug discovery. Typically, a docking protocol comprises different phases. It starts with the exhaustive sampling of the binding site upon rigid-body approximation of monomers. Then, clustering algorithms are used in order to group similar binding candidates. Different refinement methods are applied in order to take into account flexibility of a molecular complex or to get rid of possible docking artefacts. Finally, binding candidates are scored with energy functions and top-ranked predictions are selected. The Thesis presents novel algorithms for docking protocols to facilitate structure prediction of protein complexes, which belong to one of the most important target class in the structure-based drug design. First, DockTrina a new algorithm to predict conformations of triangular protein trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) is presented. The method takes as input pair-wise contact predictions from a rigid-body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation test. Finally, it ranks the predictions using a scoring function which combines triples of pair-wise contact terms and a geometric clash penalty term. Being fast and efficient, DockTrina outperforms state-of-the-art computational methods dedicated to predict structure of protein oligomers on collected benchmark of protein trimers. Second, RigidRMSD a C++ library which provides fast way to compute root mean