This article is part of the supplement: Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009) .

Research

GAIA: a gram-based interaction analysis tool – an approach for identifying interacting domains in yeast

Kelvin X Zhang^1,2 and BF Francis Ouellette²

¹  Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada

²  Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Toronto, Ontario, M5G 0A3, Canada

author email corresponding author email

BMC Bioinformatics 2009, 10(Suppl 1):S60doi:10.1186/1471-2105-10-S1-S60

Published:	30 January 2009

Abstract

Background

Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.

Results

In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in Saccharomyces cerevisiae (budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.

Conclusion

GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells.