Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences

The reliable identification of beta-aggregating stretches in protein sequences is essential for the development of therapeutic agents for Alzheimer's and Parkinson's diseases, as well as other pathological conditions associated with protein deposition. Here, a model based on physicochemical properties and computational design of beta-aggregating peptide sequences is shown to be able to predict the aggregation rate over a large set of natural polypeptide sequences. Furthermore, the model identifies aggregation-prone fragments within proteins and predicts the parallel or anti-parallel beta-sheet organization in fibrils. The model recognizes different beta-aggregating segments in mammalian and nonmammalian priori proteins, providing insights into the species barrier for the transmission of the priori disease.