Exploiting amino acid composition for predicting protein-protein interactions.

ABSTRACT:

Background

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.

Results

AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using

SUBMITTER: Roy S 

PROVIDER: S-EPMC2775920 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature