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Bioinformatics Advance Access published online on June 14, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti540
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 31, 2005
Revised May 11, 2005
Accepted June 13, 2005

Article

Improving sequence-based fold recognition by use of 3D model quality assessment

Chris S. Pettitt 1, Liam J. McGuffin 1, and David T. Jones 1*

1 Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, WC1E 6BT, UK

* To whom correspondence should be addressed.
David T. Jones, E-mail: d.jones{at}cs.ucl.ac.uk


  Abstract

Motivation: The ability of a simple method (MODCHECK) to determine the sequence-structure compatibility of a set of structural models generated by fold recognition is tested in a thorough benchmark analysis. Four Model Quality Assessment Programs (MQAP's) were tested on 188 targets from the latest LiveBench-9 automated structure evaluation experiment. We systematically test and evaluate whether the MQAP methods can successfully detected native-like models.

Results: We show that MODCHECK is the most reliable method for consistently producing the best top model selection and for ranking the models compared with the other 3 methods tested. In addition, we show that the choice of model similarity score used to assess a model's similarity to the experimental structure can influence the overall performance of these tools. Although these MQAP methods fail to improve the model selection performance for methods which already incorporate protein 3-D structural information, an improvement is observed for methods which are purely sequence-based, including the best profile-profile methods. This suggests that even the best sequence-based fold recognition methods can still be improved by taking into account 3-D structural information.


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