GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization.

Mélanie Boudard (1) , Dominique Barth (2) , Julie Bernauer (3, 4) , Alain Denise (1, 5) , Johanne Cohen (1)

1 LRI - Laboratoire de Recherche en Informatique
2 PRISM - Parallélisme, Réseaux, Systèmes, Modélisation
3 AMIB - Algorithms and Models for Integrative Biology
4 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
5 I2BC - Institut de Biologie Intégrative de la Cellule

We developed a complete method for sampling 3D RNA structure at a coarse-grained model, taking a secondary structure as input. One of the novelties of our method is that a second step extracts two best possible structures close to the native, from a set of possible structures. Although our method benefits from the first version of GARN, some of the main features on GARN2 are very different. GARN2 is much faster than the previous version and than the well-known methods of the state-of-art. Our experiments show that GARN2 can also provide better structures than the other state-of-the-art methods.

Domaines: Informatique

Mélanie Boudard, Dominique Barth, Julie Bernauer, Alain Denise, Johanne Cohen. GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization.. Bioinformatics, 2017, 16, pp.2479-2486. ⟨10.1093/bioinformatics/btx175⟩. ⟨hal-01589347⟩