Protease substrate site predictors derived from machine learning on multilevel substrate phage display data.
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/
Chen
Ching-Tai
CT
Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
Yang
Ei-Wen
EW
Hsu
Hung-Ju
HJ
Sun
Yi-Kun
YK
Hsu
Wen-Lian
WL
Yang
An-Suei
AS
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
D019151:Peptide Library; D010447:Peptide Hydrolases