Journal Article

Protease substrate site predictors derived from machine learning on multilevel substrate phage display data

Ching-Tai Chen, Ei-Wen Yang, Hung-Ju Hsu, Yi-Kun Sun, Wen-Lian Hsu and An-Suei Yang

in Bioinformatics

Volume 24, issue 23, pages 2691-2697
Published in print December 2008 | ISSN: 1367-4803
Published online October 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn538
Protease substrate site predictors derived from machine learning on multilevel substrate phage display data

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Motivation: 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.

Availability: http://asqa.iis.sinica.edu.tw/fXaWeb/

Contact: hsu@iis.sinica.edu.tw; yangas@gate.sinica.edu.tw

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5584 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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