title: SeqRate: sequence-based protein folding type classification and rates prediction.
abstract: BACKGROUND
  Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines.
  
  
  RESULTS
  We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs.
  
  
  CONCLUSIONS
  Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html.
authors.affiliation: Informatics Institute, University of Missouri, Columbia, 65211, USA. guanlin@mail.missouri.edu
authors.forename: Guan Ning
authors.initials: GN
authors.lastname: Lin
authors.forename: Zheng
authors.initials: Z
authors.lastname: Wang
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D020407:Internet; D007700:Kinetics; D016014:Linear Models; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins