title: Prediction of amyloid fibril-forming segments based on a support vector machine.
abstract: BACKGROUND
  Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets.
  
  
  RESULTS
  We propose a method, named Pafig (Prediction of amyloid fibril-forming segments) based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex. Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a result, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids--alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids--aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies.
  
  
  CONCLUSIONS
  The performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.
authors.affiliation: Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China. tianjian3721@163.com
authors.forename: Jian
authors.initials: J
authors.lastname: Tian
authors.forename: Ningfeng
authors.initials: N
authors.lastname: Wu
authors.forename: Jun
authors.initials: J
authors.lastname: Guo
authors.forename: Yunliu
authors.initials: Y
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D000682:Amyloid / Q000737:chemistry*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D010455:Peptides / Q000737:chemistry
chemical_list: D000682:Amyloid; D010455:Peptides