Nature Biotechnology22, 1302 - 1306 (2004)
Published online: 12 September 2004; | doi:10.1038/nbt1012
Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
Ana-Maria Fernandez-Escamilla1, Frederic Rousseau2, Joost Schymkowitz2
& Luis Serrano1
1
European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg D-69117, Germany.
2
SWITCH Laboratory, Flemish Institute for Biotechnology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
Correspondence should be addressed to Luis Serrano serrano@embl.de
We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles of -sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the aggregation of a data set of 179 peptides compiled from the literature as well as of a new set of 71 peptides derived from human disease-related proteins, including prion protein, lysozyme and 2-microglobulin. TANGO also correctly predicts pathogenic as well as protective mutations of the Alzheimer -peptide, human lysozyme and transthyretin, and discriminates between -sheet propensity and aggregation. Our results confirm the model of intermolecular -sheet formation as a widespread underlying mechanism of protein aggregation. Furthermore, the algorithm opens the door to a fully automated, sequence-based design strategy to improve the aggregation properties of proteins of scientific or industrial interest.
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-sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the aggregation of a data set of 179 peptides compiled from the literature as well as of a new set of 71 peptides derived from human disease-related proteins, including prion protein, lysozyme and 
