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Protein-RNA Complexes and Efficient Automatic Docking: Expanding RosettaDock Possibilities.

Adrien Guilhot-Gaudeffroy 1, 2 Christine Froidevaux 1, 2 Jérôme Azé 1, 2, 3 Julie Bernauer 1, 4
1 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
Abstract : Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.
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Adrien Guilhot-Gaudeffroy, Christine Froidevaux, Jérôme Azé, Julie Bernauer. Protein-RNA Complexes and Efficient Automatic Docking: Expanding RosettaDock Possibilities.. PLoS ONE, Public Library of Science, 2014, 9 (9), pp.e108928. ⟨10.1371/journal.pone.0108928⟩. ⟨hal-01071876⟩

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