A combined approach for the classification of G protein-coupled receptors and its application to detect GPCR splice variants

Authors:
Hae-Seok Eo;Jae Pil Choi;Seung-Jae Noh;Cheol-Goo Hur;Won Kim
Affiliations:
School of Biological Sciences, Seoul National University, Seoul 151-742, Republic of Korea;Korea Research Institute of Bioscience and Biotechnology (KRIBB), 52, Oun-dong, Yuseong, Daejeon 305-333, Republic of Korea;Korea Research Institute of Bioscience and Biotechnology (KRIBB), 52, Oun-dong, Yuseong, Daejeon 305-333, Republic of Korea;Korea Research Institute of Bioscience and Biotechnology (KRIBB), 52, Oun-dong, Yuseong, Daejeon 305-333, Republic of Korea;School of Biological Sciences, Seoul National University, Seoul 151-742, Republic of Korea
Venue:
Computational Biology and Chemistry
Year:
2007

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Abstract

G protein-coupled receptors (GPCRs) constitute the largest family of cell surface receptors and play a central role in cellular signaling pathways. The importance of GPCRs has led to their becoming the targets of more than 50% of prescription drugs. However, drug compounds that do not differentiate between receptor subtypes can have considerable side effects and efficacy problems. An accurate classification of GPCRs can solve the side effect problems and raise the efficacy of drugs. Here, we introduce an approach that combines a fingerprint method, statistical profiles and physicochemical properties of transmembrane (TM) domains for a highly accurate classification of the receptors. The approach allows both the recognition and classification for GPCRs at the subfamily and subtype level, and allows the identification of splice variants. We found that the approach demonstrates an overall accuracy of 97.88% for subfamily classification, and 94.57% for subtype classification.