Protease substrate site predictors derived from machine learning on multilevel substrate phage display data
Author(s) -
Ching-Tai Chen,
Ei-Wen Yang,
Hung-Ju Hsu,
Yi-Kun Sun,
Wen−Lian Hsu,
AnSuei Yang
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn538
Subject(s) - substrate (aquarium) , protease , phage display , computer science , software , artificial intelligence , substrate specificity , computational biology , chemistry , biology , biochemistry , enzyme , programming language , peptide , ecology
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.
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