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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics

Open Access Proceedings

Protein comparison at the domain architecture level

Byungwook Lee1 and Doheon Lee2

Author Affiliations

1 Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea

2 Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Korea

BMC Bioinformatics 2009, 10(Suppl 15):S5 doi:10.1186/1471-2105-10-S15-S5

Published: 3 December 2009

Abstract

Background

The general method used to determine the function of newly discovered proteins is to transfer annotations from well-characterized homologous proteins. The process of selecting homologous proteins can largely be classified into sequence-based and domain-based approaches. Domain-based methods have several advantages for identifying distant homology and homology among proteins with multiple domains, as compared to sequence-based methods. However, these methods are challenged by large families defined by 'promiscuous' (or 'mobile') domains.

Results

Here we present a measure, called Weighed Domain Architecture Comparison (WDAC), of domain architecture similarity, which can be used to identify homolog of multidomain proteins. To distinguish these promiscuous domains from conventional protein domains, we assigned a weight score to Pfam domain extracted from RefSeq proteins, based on its abundance and versatility. To measure the similarity of two domain architectures, cosine similarity (a similarity measure used in information retrieval) is used. We combined sequence similarity with domain architecture comparisons to identify proteins belonging to the same domain architecture. Using human and nematode proteomes, we compared WDAC with an unweighted domain architecture method (DAC) to evaluate the effectiveness of domain weight scores. We found that WDAC is better at identifying homology among multidomain proteins.

Conclusion

Our analysis indicates that considering domain weight scores in domain architecture comparisons improves protein homology identification. We developed a web-based server to allow users to compare their proteins with protein domain architectures.