Protein comparison at the domain architecture level

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dc.contributor.authorByungUk Lee-
dc.contributor.authorD Lee-
dc.description.abstractBackground: 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.-
dc.titleProtein comparison at the domain architecture level-
dc.title.alternativeProtein comparison at the domain architecture level-
dc.citation.titleBMC Bioinformatics-
dc.contributor.affiliatedAuthorByungUk Lee-
dc.identifier.bibliographicCitationBMC Bioinformatics, vol. 10, no. Sup15, pp. S5-S5-
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