Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins

Proteins. 2004 Feb 1;54(2):282-8. doi: 10.1002/prot.10589.


Beta-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent beta-strands into beta-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of beta-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 +/- 4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3 +/- 6.1%, diverging turns with an accuracy of 73.9 +/- 6.0%. Incorporation of the beta-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-beta-proteins and 3 alphabeta-proteins. The beta-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003;

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Motifs
  • Computational Biology*
  • Computer Simulation*
  • Databases, Protein
  • Hydrogen Bonding
  • Internet
  • Models, Molecular
  • Neural Networks, Computer
  • Protein Structure, Secondary
  • Proteins / chemistry*
  • Proteins / metabolism*
  • Sensitivity and Specificity
  • Software


  • Proteins