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PEDS Advance Access originally published online on May 27, 2004
Protein Engineering Design and Selection 2004 17(4):367-373; doi:10.1093/protein/gzh042
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Protein Engineering, Design & Selection vol. 17 no. 4 © Oxford University Press 2004; all rights reserved

Learning to discriminate between ligand-bound and disulfide-bound cysteines

Andrea Passerini1 and Paolo Frasconi

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

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

We present a machine learning method to discriminate between cysteines involved in ligand binding and cysteines forming disulfide bridges. Our method uses a window of multiple alignment profiles to represent each instance and support vector machines with a polynomial kernel as the 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.

Received March 5, 2004; accepted May 4, 2004.

Edited by Marius Clore


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