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Bioinformatics Advance Access published online on October 29, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn538
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Protease Substrate Site Predictors Derived from Machine Learning on Multilevel Substrate Phage Display Data

Ching-Tai Chen 1,2,&, Ei-Wen Yang 1,&, Hung-Ju Hsu 3,4, Yi-Kun Sun 3, Wen-Lian Hsu 1,* and An-Suei Yang 3,*

1Institute of Information Science, Academia Sinica, Taipei, Taiwan 115.
2Institute of bioinformatics, National Chiao Tung University, Hsin Chu, Taiwan 300.
3Genomics Research Center, Academia Sinica, Taipei, Taiwan 115.
4Graduate Institute of Life Sciences, National Defense Medical University, Taipei, Taiwan 114.

*Corresponding authors: AS Yang, Genomic Research Center, Academia Sinica, 128 Academia Rd., Section 2, Nankang District, Taipei, Taiwan, 115. email:yangas{at}gate.sinica.edu.tw; WL Hsu, Institutes of Information Sci-ence, Academia Sinica, 128 Academia Rd., Section 2, Nankang District, Taipei, Taiwan, 115. email: hsu{at}iis.sinica.edu.tw


  Abstract

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: yangas{at}gate.sinica.edu.tw; hsu{at}iis.sinica.edu.tw

Associate Editor: Prof. Burkhard Rost

&The first two authors contribute equally to this work.

Received on August 27, 2008; revised on October 9, 2008; accepted on October 10, 2008

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