L'analyse conjointe des réseaux corrélés dynamiquement et des réseaux coévolués de résidus: analyse à grande échelle et méthodes pour prédire les effets des mutations associées à des maladies génétiques

Informations générales
Détails de la thèse/HDR
Marianne Rooman
Jacques Chomilier
Richard Lavery
Sonia Longhi
Directeur (pour les thèses)
Alessandra Carbone
Elodie LAINE
Résumé en anglais
Joint analysis of dynamically correlated networks and coevolved residue clusters: large-scale analysis and methods for predicting the effects of genetic disease associated mutations.

We presented COMMA, a method to describe and compare the dynamical architectures of different proteins or different variants of the same protein. COMMA extracts dynamical properties from conformational ensembles to identify 'communication pathways', chains of residues linked by stable interactions that move together, and 'independent cliques', clusters of residues that fluctuate in a concerted way. Pathways and cliques are used to define 'communication blocks'. The term 'communication' refers to the way information is transmitted across the protein structure. The originality of the method lies in the fact that it accounts for two different modes of communication, through the use of pathways and cliques. Consequently, it enables to contrast the different types of communication occurring between residues and to hierarchise the different regions of a protein depending on their communication efficiency. COMMA provides a description of the infostery of a protein or protein complex that goes beyond the notions of chain, domain and secondary structure element/motif, and beyond classical measures of how a protein moves and/or changes its shape. We showed the efficiency of our approach in providing mechanistic insights on the effects of deleterious mutations by pinpointing residues playing key roles in the propagation of these effects, through different case studies. In addition, we proposed an original approach to predict mutational effects based on protein sequence and defined a score derived from sequence analysis and structural information to predict the phenotypic outcomes of the mutations. The predictive power of the score is equivalent to or higher than more sophisticated state-of-the-art methods for predicting mutational outcome. Consequently, our work contributed to better understanding the sequence-structure-dynamics relationship as it provides means to predict the phenotypic outcomes of mutations in a systematic way. It has to be emphasized that in the case of PDZ domain, we were able to extract pertinent information from relatively short MD simulations and we demonstrated that the wild-type complex contained all information to identify most of the positions that 'matter'. Our proposed method to study the dynamics of proteins, can detect protein regions that are prone to disorder or substantial conformational rearrangements, without requiring the input MD trajectory to actually sample the unfolded states of these regions. Moreover, COMMA analysis of disordered coiled-coils, enabled us to suggest mutations that regulate the stability of the coiled-coils.