Prediction of "hot spots" of aggregation in disease-linked polypeptides

Background: The polypeptides involved in amyloidogenesis may be globular proteins with a defined 3D-structure or natively unfolded proteins. The first class includes polypeptides such as β2-microglobulin, lysozyme, transthyretin or the prion protein, whereas β-amyloid peptide, amylin or α-synuclein all belong to the second class. Recent studies suggest that specific regions in the proteins act as "hot spots" driving aggregation. This should be especially relevant for natively unfolded proteins or unfolded states of globular proteins as they lack significant secondary and tertiary structure and specific intra-chain interactions that can mask these aggregation-prone regions. Prediction of such sequence stretches is important since they are potential therapeutic targets. Results: In this study we exploited the experimental data obtained in an in vivo system using β-amyloid peptide as a model to derive the individual aggregation propensities of natural amino acids. These data are used to generate aggregation profiles for different disease-related polypeptides. The approach detects the presence of "hot spots" which have been already validated experimentally in the literature and provides insights into the effect of disease-linked mutations in these polypeptides. Conclusion: The proposed method might become a useful tool for the future development of sequence-targeted anti-aggregation pharmaceuticals