Indexing protein structures using suffix trees

Feng Gao, Mohammed J. Zaki

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


Approaches for indexing proteins and fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this chapter, we describe a new method for extracting the local feature vectors of protein structures. Each residue is represented by a triangle, and the correlation between a set of residues is described by the distances between Cα atoms and the angles between the normals of planes in which the triangles lie. The normalized local feature vectors are indexed using a suffix tree. For all query segments, suffix trees can be used effectively to retrieve the maximal matches, which are then chained to obtain alignments with database proteins. Similar proteins are selected by their alignment score against the query. Our results show classification accuracy up to 97.8 and 99.4% at the superfamily and class level according to the SCOP classification and show that, on average 7.49 out of 10 proteins from the same superfamily are obtained among the top 10 matches. These results outperform the best previous methods.

Original languageEnglish
Title of host publicationProtein Structure Prediction, Second Edition
EditorsMohammed J. Zaki, Christopher Bystroff
Number of pages23
Publication statusPublished - 5 Oct 2007

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745



  • 3D database search
  • Approximate matches
  • Protein structure indexing
  • Structural motifs
  • Suffix trees

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Gao, F., & Zaki, M. J. (2007). Indexing protein structures using suffix trees. In M. J. Zaki, & C. Bystroff (Eds.), Protein Structure Prediction, Second Edition (pp. 147-169). (Methods in Molecular Biology; Vol. 413).