Prediction of the Effects of Mutations on the Stability and Interactions of Proteins

In this study we investigated the application of algorithm models employed to predict the effect of rare protein coding variants related to human health, both at the genetic level, and in protein structures, in particular the amino acid sequence change, stability and function. For the analyses of genomic variants we applied computational tools, such as, PROVEAN, SIFT, MetaSVM, VEST3, CADD, GERP++_RS, GERP++_NR, MetaLR and calculated the AUC-ROC curves. Furthemore we selected two Variants of uncertain significance (VUS), ATOH1 with a mutation in R161G and NOTCH3 with a mutation in G289C and applied Homology Modeling in order to study the mutation arrangement and protein structures. In the second part of the work, we developed a protocol in order to calculate the thermodynamic stability of proteins with amino acid mutations. We applied Molecular Dynamics simulations in GBSA implicit solvent for a benchmark of 134 mutations in the microbial Ribonuclease Barnase. Morover, we developed PDB2ENTROPY, a tool used for the calculation of entropy contributions. Our results suggest that the accuracy of the method is not very high, mostly due to the treatment of electrostatics in the implicit solvent model. Scaling different contributions improves the accuracy and suggest that intrasolute van der Waals interactions and non-polar solvation energy are overestimated in the implicit solvent model. Taking into account entropy improves the quality of the predictions.