| Title | Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. |
| Publication Type | Journal Article |
| Year of Publication | 2004 |
| Authors | Fernandez-Escamilla A-M, Rousseau F, Schymkowitz J, Serrano L |
| Journal | Nat Biotechnol |
| Volume | 22 |
| Issue | 10 |
| Pagination | 1302-6 |
| Date Published | 2004 Oct |
| ISSN | 1087-0156 |
| Keywords | Algorithms, Amino Acid Substitution, Binding Sites, Computer Simulation, Dimerization, Models, Chemical, Models, Molecular, Models, Statistical, Multiprotein Complexes, Mutagenesis, Site-Directed, Mutation, Peptides, Protein Binding, Protein Conformation, Proteins, Structure-Activity Relationship |
| Abstract | 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. |
| DOI | 10.1038/nbt1012 |
| Alternate Journal | Nat Biotechnol |
| PubMed ID | 15361882 |
