Abstract:
Mutations are associated with many diseases (cancer, Alzheimer’s disease etc.). These disease-causing mutations are called deleterious mutations and their effects can be corrected by other mutations, i.e. compensatory mutations. Understanding the underlying dynamics of deleterious and compensatory mutations is of high importance for the treatment of diseases and drug design. To that end, the relationship between evolutionary conserved/reused segments and dynamic domains as well as the dynamic determinants of deleterious and compensatory mutations are investigated in this work. Themes; reused segments (~35-200 amino acids) among proteins with high sequence similarity, are correlated with the dynamic domains unveiled with Gaussian Network Model (GNM) analysis. The correlation between themes and dynamic domains evaluated with Adjusted Mutual Information and Standardized Mutual Information measures is found to be statistically significant. GNM based perturbation analysis revealed that the highest response to perturbations occur at the terminal points of themes. In order to investigate the dynamic properties of deleterious mutations, a dataset consisting of proteins that have been the subject to deep sequencing studies is created. In order to mimic the effect of mutation, a perturbation is placed in the GNM algorithm. The effect of this perturbation on the dynamics of the structures is examined according to the changes in the total fluctuation profiles and eigenvalues. Residues with high mutation sensitivity are found to be the residues that cause distinct change in protein dynamics upon perturbation and this relationship is statistically significant. For compensatory mutation studies, deleterious mutations of tumor suppressor protein p53 and their compensatory mutations are used. The results revealed that deleterious and compensatory mutations are correlated in slow modes of motion and demonstrate the importance of coevolution, hinge points and allosteric interaction in slow modes of motion for these mutations. Furthermore, the dynamic information about deleterious and compensatory mutations reported in this study will be a guide for further studies on the prediction of deleterious and compensatory mutations.
