Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins

Nat Biotechnol. 2004 Oct;22(10):1302-6. doi: 10.1038/nbt1012. Epub 2004 Sep 12.


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.

Publication types

  • Comparative Study
  • Evaluation Study
  • Letter
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Amino Acid Substitution
  • Binding Sites
  • Computer Simulation
  • Dimerization
  • Models, Chemical*
  • Models, Molecular*
  • Models, Statistical
  • Multiprotein Complexes / chemistry*
  • Mutagenesis, Site-Directed
  • Mutation
  • Peptides / chemistry
  • Protein Binding
  • Protein Conformation
  • Proteins / chemistry*
  • Structure-Activity Relationship


  • Multiprotein Complexes
  • Peptides
  • Proteins