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Nucleic Acids Research, 2003, Vol. 31, No. 3 944-952
© 2003 Oxford University Press

Improving the performance of DomainParser for structural domain partition using neural network

Jun-tao Guo1, Dong Xu1, Dongsup Kim1 and Ying Xu*,1,2

1 Protein Informatics Group, Life Sciences Division and 2 Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830-6480, USA

*To whom correspondence should be addressed at Protein Informatics Group, Life Sciences Division, Oak Ridge National Laboratory, 1060 Commerce Park Drive MS 6480, Oak Ridge, TN 37830-6480, USA. Tel: +1 865 574 7263; Fax: +1 865 241 1965; Email: xyn{at}ornl.gov

Structural domains are considered as the basic units of protein folding, evolution, function and design. Automatic decomposition of protein structures into structural domains, though after many years of investigation, remains a challenging and unsolved problem. Manual inspection still plays a key role in domain decomposition of a protein structure. We have previously developed a computer program, DomainParser, using network flow algorithms. The algorithm partitions a protein structure into domains accurately when the number of domains to be partitioned is known. However the performance drops when this number is unclear (the overall performance is 74.5% over a set of 1317 protein chains). Through utilization of various types of structural information including hydrophobic moment profile, we have developed an effective method for assessing the most probable number of domains a structure may have. The core of this method is a neural network, which is trained to discriminate correctly partitioned domains from incorrectly partitioned domains. When compared with the manual decomposition results given in the SCOP database, our new algorithm achieves higher decomposition accuracy (81.9%) on the same data set.


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