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

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.

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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Biology and genetics

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT

General Terms:
Algorithms, Design, Human Factors, Management, Measurement, Performance, Theory