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Bioinformatics Advance Access originally published online on June 14, 2005
Bioinformatics 2005 21(17):3509-3515; 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{at}oupjournals.org

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

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

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

*To whom correspondence should be addressed.

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 (MQAPs) 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 detect native-likemodels.

Results: We show that compared with the other three methods tested MODCHECK is the most reliable method for consistently performing the best top model selection and for ranking the models. 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 that already incorporate protein three dimension (3D) structural information, an improvement is observed for methods that 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 the 3D structural information.

Contact: d.jones{at}cs.ucl.ac.uk

Received on January 31, 2005; revised on May 11, 2005; accepted on June 13, 2005

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