Published online 14 April 2006
Article |
A supersecondary structure library and search algorithm for modeling loops in protein structures
Department of Biochemistry and Seaver Foundation Center for Bioinformatics, Albert Einstein College of Medicine 1300 Morris Park Avenue, Bronx, NY 10461, USA 1 Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra C/Doctor Aiguader,80. 08003, Barcelona, Catalonia, Spain
*To whom correspondence should be addressed. Tel: +1 718 430 3233; Fax: +1 718 430 856; Email: andras{at}fiserlab.org
Received January 16, 2006. Revised February 12, 2006. Accepted March 20, 2006.
We present a fragment-search based method for predicting loop conformations in protein models. A hierarchical and multidimensional database has been set up that currently classifies 105 950 loop fragments and loop flanking secondary structures. Besides the length of the loops and types of bracing secondary structures the database is organized along four internal coordinates, a distance and three types of angles characterizing the geometry of stem regions. Candidate fragments are selected from this library by matching the length, the types of bracing secondary structures of the query and satisfying the geometrical restraints of the stems and subsequently inserted in the query protein framework where their fit is assessed by the root mean square deviation (r.m.s.d.) of stem regions and by the number of rigid body clashes with the environment. In the final step remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed 
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main chain dihedral angle propensities. Confidence Z-score cut-offs were determined for each loop length that identify those predicted fragments that outperform a competitive ab initio method. A web server implements the method, regularly updates the fragment library and performs prediction. Predicted segments are returned, or optionally, these can be completed with side chain reconstruction and subsequently annealed in the environment of the query protein by conjugate gradient minimization. The prediction method was tested on artificially prepared search datasets where all trivial sequence similarities on the SCOP superfamily level were removed. Under these conditions it is possible to predict loops of length 4, 8 and 12 with coverage of 98, 78 and 28% with at least of 0.22, 1.38 and 2.47 Å of r.m.s.d. accuracy, respectively. In a head-to-head comparison on loops extracted from freshly deposited new protein folds the current method outperformed in a 
5:1 ratio an earlier developed database search method.
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