Computational analysis on the effect of post-translational modifications on amyloid interactions

Protein aggregation is a common pathological hallmark of a large number of neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, as well as many metabolic diseases and cancer. This phenomenon results from the self-assembly of short regions of mainly hydrophobic amino acids (APRs) into stacked structured sheets called steric zippers. Mounting evidence suggest that protein post-translational modifications (PTMs) can often increase or decrease aggregation propensity, although the exact mechanisms are still largely unknown. In this project, my aims are (1) to analyse the effects of major PTMs on protein aggregation in silico, (2) to develop an algorithm that captures the effect of PTMs on intrinsic aggregation and, (3) to understand the impact of PTMs on protein aggregation in human disease cases, as well as in protein biotechnology. To this aim I will rely mainly on computational approaches (multivariate statistical analysis, Machine Learning, database mining, evolutionary analysis) but also include experimental validation. Ultimately, the proposed methodology and software can be exploited to unravel the principles that dictate the effects of PTMs on protein aggregation propensity. Therefore, we can move closer towards a foundation for diagnostics and drug design for protein aggregation diseases.