title: A method for probing the mutational landscape of amyloid structure.
abstract: BACKGROUND
  Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods.
  
  
  RESULTS
  We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic 'Iowa' mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments.
  
  
  BACKGROUND
  Our tool is publically available on the web at http://amyloid.csail.mit.edu/.
  
  
  BACKGROUND
  lindquist_admin@wi.mit.edu; bab@csail.mit.edu.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.
authors.forename: Charles W
authors.initials: CW
authors.lastname: O'Donnell
authors.forename: Jérôme
authors.initials: J
authors.lastname: Waldispühl
authors.forename: Mieszko
authors.initials: M
authors.lastname: Lis
authors.forename: Randal
authors.initials: R
authors.lastname: Halfmann
authors.forename: Srinivas
authors.initials: S
authors.lastname: Devadas
authors.forename: Susan
authors.initials: S
authors.lastname: Lindquist
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000378:metabolism / Q000473:pathology; D000682:Amyloid / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D016564:Amyloid beta-Protein Precursor / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D006801:Humans; D009154:Mutation; D017433:Protein Structure, Secondary; D015003:Yeasts / Q000737:chemistry / Q000378:metabolism
chemical_list: C545487:APP protein, human; D000682:Amyloid; D016564:Amyloid beta-Protein Precursor