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Nucleic Acids Research Advance Access originally published online on September 13, 2006
Nucleic Acids Research 2006 34(17):e112; doi:10.1093/nar/gkl480
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Nucleic Acids Research, 2006, Vol. 34, No. 17 e112
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

Homology modeling using parametric alignment ensemble generation with consensus and energy-based model selection

Dylan Chivian1 and David Baker1,2,*

1 Department of Biochemistry, University of Washington Seattle, WA, USA 2 Howard Hughes Medical Institute, Seattle WA, USA

*To whom correspondence should be addressed at Department of Biochemistry and HHMI, University of Washington, Box 357350, Seattle, WA 98195, USA. Tel: +1 206 543 1295; Fax: +1 206 685 1792; Email: dabaker{at}u.washington.edu

Received December 31, 2005. Revised June 20, 2006. Accepted June 21, 2006.

The accuracy of a homology model based on the structure of a distant relative or other topologically equivalent protein is primarily limited by the quality of the alignment. Here we describe a systematic approach for sequence-to-structure alignment, called ‘K*Sync’, in which alignments are generated by dynamic programming using a scoring function that combines information on many protein features, including a novel measure of how obligate a sequence region is to the protein fold. By systematically varying the weights on the different features that contribute to the alignment score, we generate very large ensembles of diverse alignments, each optimal under a particular constellation of weights. We investigate a variety of approaches to select the best models from the ensemble, including consensus of the alignments, a hydrophobic burial measure, low- and high-resolution energy functions, and combinations of these evaluation methods. The effect on model quality and selection resulting from loop modeling and backbone optimization is also studied. The performance of the method on a benchmark set is reported and shows the approach to be effective at both generating and selecting accurate alignments. The method serves as the foundation of the homology modeling module in the Robetta server.

Present address: Dylan Chivian, Lawrence Berkeley National Laboratory, Physical Biosciences Division, 1 Cyclotron Road, Mailstop 977-152, Berkeley, CA 94720, USA.


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