Quantification de la relation séquence-activité de l’ARN par prédiction de structure tridimensionnelle

Initially, we modeled the structure of an RNA family with a graph grammar to identify sequences that correspond to it. Several other modeling approaches have been developed to derive sequences, such as stochastic context-free grammars, covariance models, secondary structures profiles and constraint networks. These modeling methods are based on secondary structure compared to our graph grammars which are based on the nucleotide cyclic motifs. To exemplify our graph grammar model, we used the loop E of the ribosome that contains the Sarcin-Ricin motif that has been widely studied since its discovery by X-ray crystallography in the early 90s.  We built a graph grammar for the structure of the Sarcin-Ricin motif and derived the sequences that correspond to it. The biological relevance of these sequences is supported by an alignment of 800 bacterial ribosomal sequences. This comparison raised alternative alignments for some of the sequences that we supported by predictions of secondary and tertiary structures. According to a new tertiary structure, those alternative alignments accommodate the new derived sequences.   The nucleotide cyclic motifs used in the grammar were observed by members of our laboratory in RNA tertiary structures that were solved experimentally. We study the sequences and tertiary structures of the nucleotide cyclic motifs of the Sarcin-Ricin motif. This study suggests that the space of sequences depends heavily on interactions between all nucleotides in the nearby three-dimensional space and not only between two adjacent base pairs. We compare the number of sequences generated by the graph grammar with non contextual methods and our graph grammar generates less sequences. This suggests the importance of context for the relationship between sequence and structure, hence the use of a contextual graph grammar is more expressive than context-free grammars.  The graph grammars we used include the tertiary structure but neglect the interactions with extra-molecular factors, such as other macromolecules or ligands. In a second stage and to take into account these interactions, we developed a model incorporating the positioning of chemical groups on the surface of the tertiary structures. The assumption being that the chemical groups that are conserved on the surface of the RNA in active sequences are more likely to be involved in interactions with extra-molecular factors. Continuing with the example of the loop E, we searched the groups that could be involved its interactions with elongation factors. Knowledge of the groups involved in the important interactions serves to predict by three-dimensional modeling new sequences that have potentials to realize these interactions and thus the same function. There are few models that have been developed to address this problem: molecular descriptors, nucleotide adjacency matrices and others based on thermodynamics. These models use an oversimplified representation of the RNA structure, which limits their applicability.  We applied our model to the tertiary structures of a set of variants of a sequence of one instance of the Sarcin-Ricin motif from a bacterial ribosome. Wool and coworkers at the University of Chicago studied this proceeding experimentally by testing the viability of twelve variants. They identified four viable variants and eight lethal. We used this set of twelve sequences for training our model and we identified a set of essential properties to their biological function. For each variant of the training set we built models of tertiary structures. We then measured the partial charges of exposed atoms on the surface and we encoded this information into vectors. We used principal component analysis to transform the vectors into a set of uncorrelated variables, called principal components. Using the weighted Euclidean distance and a nearest neighbor algorithm, we applied the technique of "Leave-One-Out Cross-Validation" to choose the best parameters to predict the activity of a new sequence to match these principal components. Finally, we validated the predictive model using a new set of eight variants whose viability has been verified experimentally in our laboratory.  In conclusion, graph grammars are used to model the relationship between sequence and structure of an RNA structural element, such as the ribosomal loop E containing the Sarcin-Ricin motif. Applications range from the correction of sequence alignment to sequence design with a predetermined structure. We also developed a model to take into account the specific interactions related to a specific biological function. Our model is based on the retention of the exposure of chemical groups that are involved in these interactions. This model has allowed us to predict the biological activity of a set of variants of the loop E that binds to elongation factors.

Keyphrases: Structure, QSAR, Sarcin-Ricin, RNA, Grammar, Sequence, Function