A method is presented that uses β-strand interactions at both the sequence and the atomic level, to predict the betastructural motifs in protein sequences. A program called Wrap-and-Pack implements this method, and is shown to recognize β-trefoils, an important class of globular β-structures, in the Protein Data Bank with 92 % specificity and 92.3 % sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP βtrefoil families, when trained primarily on β-structures that are not β-trefoils, together with 3D structures of known βtrefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be β-trefoils. The computational method used here may generalize to other βstructures for which strand topology and profiles of residue accessibility are well conserved
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