A method is presented that uses ß-strand interactions to predict the parallel right-handed ß -helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method and is shown to score known ß -helices above nonß -helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP ß -helix families, when trained primarily on ß-structures that are not ß -helices, together with structural features of known ß -helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown sturcture to be ß -helices; in particular, these proteins serve as virulence factors, adhesins, and toxins in bacterial pathogenesis and include cell surface proteins frm Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other ß-structures for which strand topology and profiles of residue accessibility are well conserved.