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PEDS Advance Access published online on May 27, 2004
Protein Engineering Design and Selection, doi:10.1093/protein/gzh042
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Received March 5, 2004
Accepted May 4, 2004

Article

Learning to discriminate between ligand bound and disulfide bound cysteines

Andrea Passerini 1* Paolo Frasconi 1

1 Dipartimento di Sistemi e Informatica, Università di Firenze, 50139 Firenze, Italy

* To whom correspondence should be addressed. E-mail: passerini{at}dsi.unifi.it.


  Abstract

We present a machine learning method to discriminate between cysteines involved in ligand bindings and cysteines forming disulfide bridges. Our method uses a window of multiple alignment profiles to represent each instance and support vector machines with polynomial kernel as learning algorithm. We also report results obtained with two new kernel functions based on similarity matrices. Experimental results indicate that binding type can be predicted at significantly higher accuracy than using PROSITE patterns.

Keywords: Disulfide bridges, metal binding sites, prediction tools, support vector machines


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