Predicting the &bgr;-helix fold from protein sequence data

Authors:
Phil Bradley;Lenore Cowen;Matthew Menke;Jonathan King;Bonnie Berger
Affiliations:
Department of Mathematics and Lab for Computer Science, MIT, Cambridge, MA;Department of Mathematical Sciences, Johns Hopkins University, Baltimore, MD;Department of Mathematics and Lab for Computer Science, MIT, Cambridge, MA;Department of Biololgy, MIT, Cambridge, MA;Department of Mathematics and Lab for Computer Science, MIT, Cambridge, MA
Venue:
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Year:
2001

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Abstract

A method is presented that uses &bgr;-strand interactions to predict the right-handed &bgr;-helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method, and is shown to score known &bgr;-helices above non-&bgr;-helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP &bgr;-helix families, when trained on the the known &bgr;-helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown structure that play a role in human infectious disease to &bgr;-helices; in particular, these proteins serve as virulence factors, adhesins and toxins in bacterial pathogenesis, and include cell surface proteins from Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other &bgr; structures for which strand topology and profiles of residue accessibility are well conserved.