title: Protease substrate site predictors derived from machine learning on multilevel substrate phage display data.
abstract: BACKGROUND
  Regulatory proteases modulate proteomic dynamics with a spectrum of specificities against substrate proteins. Predictions of the substrate sites in a proteome for the proteases would facilitate understanding the biological functions of the proteases. High-throughput experiments could generate suitable datasets for machine learning to grasp complex relationships between the substrate sequences and the enzymatic specificities. But the capability in predicting protease substrate sites by integrating the machine learning algorithms with the experimental methodology has yet to be demonstrated.
  
  
  RESULTS
  Factor Xa, a key regulatory protease in the blood coagulation system, was used as model system, for which effective substrate site predictors were developed and benchmarked. The predictors were derived from bootstrap aggregation (machine learning) algorithms trained with data obtained from multilevel substrate phage display experiments. The experimental sampling and computational learning on substrate specificities can be generalized to proteases for which the active forms are available for the in vitro experiments.
  
  
  BACKGROUND
  http://asqa.iis.sinica.edu.tw/fXaWeb/
authors.affiliation: Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Ching-Tai
authors.initials: CT
authors.lastname: Chen
authors.forename: Ei-Wen
authors.initials: EW
authors.lastname: Yang
authors.forename: Hung-Ju
authors.initials: HJ
authors.lastname: Hsu
authors.forename: Yi-Kun
authors.initials: YK
authors.lastname: Sun
authors.forename: Wen-Lian
authors.initials: WL
authors.lastname: Hsu
authors.forename: An-Suei
authors.initials: AS
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D010447:Peptide Hydrolases / Q000737:chemistry*; D019151:Peptide Library; D013379:Substrate Specificity
chemical_list: D019151:Peptide Library; D010447:Peptide Hydrolases