Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties

Abstract

Motivation: Identification of novel G protein-coupled receptors and other multi-transmembrane proteins from genomic databases using structural features. Results: Here we describe a new algorithm for identifying multi-transmembrane proteins from genomic databases with a specific application to identifying G protein-coupled receptors (GPCRs) that we call quasi-periodic feature classifier (QFC). The QFC algorithm uses concise statistical variables as the 'feature space' to characterize the quasi-periodic physico-chemical properties of multi-transmembrane proteins. For the case of identifying GPCRs, the variables are then used in a non-parametric linear discriminant function to separate GPCRs from non-GPCRs. The algorithm runs in time linearly proportional to the number of sequences, and performance on a test dataset shows 96% positive identification of known GPCRs. The QFC algorithm also works well with short random segments of proteins and it positively identified GPCRs at a level greater than 90% even with segments as short as 100 amino acids. The primary advantage of the algorithm is that it does not directly use primary sequence patterns which may be subject to sampling bias. The utility of the new algorithm has been demonstrated by the isolation from the Drosophila genome project database of a novel class of seven-transmembrane proteins which were shown to be the elusive olfactory receptor genes of Drosophila.