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BMC Bioinformatics

, 7:187

First Online: 04 April 2006Received: 10 October 2005Accepted: 04 April 2006


BackgroundThe number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences.

ResultsTo explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm NNA was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11%

ConclusionCompared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-7-187 contains supplementary material, which is available to authorized users.

Xiaojing Yu, Chuan Wang contributed equally to this work.

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Autor: Xiaojing Yu - Chuan Wang - Yixue Li


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