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

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A modular kernel approach for integrative analysis of protein domain boundaries

Paul D Yoo1*, Bing Bing Zhou1 and Albert Y Zomaya12

Author Affiliations

1 Advanced Networks Research Group, School of Information Technologies (J12), the University of Sydney, NSW 2006, Australia

2 Sydney Bioinformatics Centre and the Centre for Mathematical Biology, the University of Sydney, Sydney, NSW 2006, Australia

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BMC Genomics 2009, 10(Suppl 3):S21  doi:10.1186/1471-2164-10-S3-S21

Published: 3 December 2009



In this paper, we introduce a novel inter-range interaction integrated approach for protein domain boundary prediction. It involves (1) the design of modular kernel algorithm, which is able to effectively exploit the information of non-local interactions in amino acids, and (2) the development of a novel profile that can provide suitable information to the algorithm. One of the key features of this profiling technique is the use of multiple structural alignments of remote homologues to create an extended sequence profile and combines the structural information with suitable chemical information that plays an important role in protein stability. This profile can capture the sequence characteristics of an entire structural superfamily and extend a range of profiles generated from sequence similarity alone.


Our novel profile that combines homology information with hydrophobicity from SARAH1 scale was successful in providing more structural and chemical information. In addition, the modular approach adopted in our algorithm proved to be effective in capturing information from non-local interactions. Our approach achieved 82.1%, 50.9% and 31.5% accuracies for one-domain, two-domain, and three- and more domain proteins respectively.


The experimental results in this study are encouraging, however, more work is need to extend it to a broader range of applications. We are currently developing a novel interactive (human in the loop) profiling that can provide information from more distantly related homology. This approach will further enhance the current study.