Fast detection of common geometric substructure in proteins

L. Paul Chew, Dan Huttenlocher, Klara Kedem, Jon Kleinberg

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


We consider the problem of identifying common three-dimensional substructures between proteins. Our method is based on comparing the shape of the α-carbon backbone structures of the proteins in order to find three- dimensional (3D) rigid motions that bring portions of the geometric structures into correspondence. We propose a geometric representation of protein backbone chains that is compact yet allows for similarity measures that are robust against noise and outliers. This representation encodes the structure of the backbone as a sequence of unit vectors, defined by each adjacent pair of α-carbons. We then define a measure of the similarity of two protein structures based on the root mean squared (RMS) distance between corresponding orientation vectors of the two proteins. Our measure has several advantages over measures that are commonly used for comparing protein shapes, such as the minimum RMS distance between the 3D positions of corresponding atoms in two proteins. A key advantage is that this new measure behaves well for identifying common substructures, in contrast with position- based measures where the nonmatching portions of the structure dominate the measure. At the same time, it avoids the quadratic space and computational difficulties associated with methods based on distance matrices and contact maps. We show applications of our approach to detecting common contiguous substructures in pairs of proteins, as well as the more difficult problem of identifying common protein domains (i.e., larger substructures that are not necessarily contiguous along the protein chain).

Original languageEnglish
Pages (from-to)313-325
Number of pages13
JournalJournal of Computational Biology
Issue number3-4
StatePublished - 1 Sep 1999


  • Efficient algorithms
  • Structure matching
  • Unit-vector RMS


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