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

Abstract:

: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.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Guo JT,Xu D,Kim D,Xu Y

doi

10.1093/nar/gkg189

keywords:

subject

Has Abstract

pub_date

2003-02-01 00:00:00

pages

944-52

issue

3

eissn

0305-1048

issn

1362-4962

journal_volume

31

pub_type

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