Bioinformatics Vol. 19 no. 14 2003
Pages 1741-1747
© 2003 Oxford University Press
Characterizing proteolytic cleavage site activity using bio-basis function neural networks
1 Department of Structure Biology, Oxford University, UK, 2 GlaxoSmithKline, Stevenage SG1 2NY, UK, 3 School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, 4 GlaxoSmithKline, Biopharmaceuticals Development, Beckenham, BR3 3BS, UK
Received on October 3, 2002
; revised on February 11, 2003
; accepted on March 27, 2003
Motivation: In protein chemistry, proteomics and biopharmaceutical development, there is a desire to know not only where a protein is cleaved by a protease, but also the susceptibility of its cleavage sites. The current tools for proteolytic cleavage prediction have often relied purely on regular expressions, or involve models that do not represent biological data well.
Results: A novel methodology for characterizing proteolytic cleavage site activities has been developed, which incorporates two fundamental features: activity class prediction and the use of an amino acid similarity matrix for (non-parametric) neural learning. The first solved the problem of predicting proteolytic efficiency. The second significantly improved the robustness in prediction and reduced the time complexity for learning. This study shows that activity class prediction is successful when applying this methodology to the prediction and characterization of Trypsin cleavage sites and the prediction of HIV protease cleavage sites.
Availability: Requests for software and data should be made respectively to Dr Zheng Rong Yang and Miss Rebecca Thomson.
Contact: Z.R.Yang{at}ex.ac.uk
* To whom correspondence should be addressed.
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