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

Abstract

The reliable identification of b-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 b-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 b-sheet organization in fibrils. The model recognizes different b-aggregating segments in mammalian and nonmammalian prion proteins, providing insights into the species barrier for the transmission of the prion disease.Keywords: Alzheimer's disease; amyloid; protein aggregation rate; prion protein; species barrier; genetic algorithm; molecular dynamics Amyloid fibrils are associated with a number of pathologies including Alzheimer's, Parkinson's, Huntington's, prion disease, and type II diabetes (Horwich and Weissman 1997;Kelly 1998;Dobson 1999;Rochet and Lansbury 2000). Therefore it is of fundamental medical interest to understand the mechanisms of fibrillogenesis, with the ultimate goal of designing inhibitors. One important and still unanswered question regarding amyloid fibril formation is the specificity with which the amino acid sequence determines b-aggregation propensity and the atomic details of the fibril structure. Because of the difficulties in obtaining detailed structural information by X-ray crystallography or solution phase NMR spectroscopy, computational approaches are needed to guide experiments, e.g., to determine short segments of amyloid-like proteins that share the same biophysical properties of the full-length proteins (Balbirnie et al. 2001) and identify those elements which are essential for the formation of protein fibrils (Tenidis et al. 2000;von Bergen et al. 2000). As aggregation conditions vary sensibly with the composition and especially the sequence of the polypeptide, single amino acid substitutions have been used to investigate the fibril formation (Chiti et al. 1999), and complementary theoretical studies proposed relative rate equations to predict the change of aggregation rate upon mutation (Chiti et al. 2003;Tartaglia et al. 2004). Although the application of relative rate equations shows high correlation with experimental data, these models require the a priori knowledge of wild-type aggregation rates.We report here an absolute rate equation derived from both first principles and analysis of aggregating sequences designed by a computational approach. The latter is based on a genetic algorithm optimization in sequence space and molecular dynamics sampling of conformation space. The equation does not need any information except the amino acid sequence and two environmental factors (i.e., temperature and concentration). Our model gives both the aggregation rate and the ''amyloid spectrum'' of a p...

Neurodegenerative diseases: Quantitative predictions of protein–RNA interactions

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A polymer physics perspective on driving forces and mechanisms for protein aggregation

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Boosting antibody developability through rational sequence optimization

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