Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins.
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.
Fernandez-Escamilla
Ana-Maria
AM
Rousseau
Frederic
F
Schymkowitz
Joost
J
Serrano
Luis
L
D000465:Algorithms; D019943:Amino Acid Substitution; D001665:Binding Sites; D003198:Computer Simulation; D019281:Dimerization; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D046912:Multiprotein Complexes / Q000737:chemistry*; D016297:Mutagenesis, Site-Directed; D009154:Mutation; D010455:Peptides / Q000737:chemistry; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013329:Structure-Activity Relationship
D046912:Multiprotein Complexes; D010455:Peptides; D011506:Proteins