An improved classification of G-protein-coupled receptors using sequence-derived features.

ABSTRACT:

Background

G-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable; however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences.

Results

We propose a new method called PCA-GPCR, to predict GPCRs using a comprehensive set of 1497 sequence-derived features. The principal component analysis is first employed to reduce the dimension of the feature space to 32. Then, the resulting 32-dimensional feature vectors are fed into a simple yet

SUBMITTER: Peng ZL 

PROVIDER: S-EPMC3247138 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature