NR ARPE
AU Fernandez-Escamilla,A.M.; Rousseau,F.; Schymkowitz,J.; Serrano,L.
TI Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
QU Nature Biotechnology 2004 Oct; 22(10): 1302-6
KI Nat Biotechnol. 2004 Oct;22(10):1240-1. PMID: 15470460
PT evaluation studies; letter; validation studies
AB We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles of beta-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 beta2-microglobulin. TANGO also correctly predicts pathogenic as well as protective mutations of the Alzheimer beta-peptide, human lysozyme and transthyretin, and discriminates between beta-sheet propensity and aggregation. Our results confirm the model of intermolecular beta-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.
MH *Algorithms; Amino Acid Substitution; Binding Sites; Comparative Study; Computer Simulation; Dimerization; *Models, Chemical; *Models, Molecular; Models, Statistical; Multiprotein Complexes/*chemistry; Mutagenesis, Site-Directed; Mutation; Peptides/chemistry; Protein Binding; Protein Conformation; Proteins/*chemistry; Research Support, Non-U.S. Gov't; Structure-Activity Relationship
SP englisch
PO USA