Improved LogitBoost Classifier Based Prediction of GPCR-G-Protein Coupling with Self-Adaptive Immune Algorithm

Abstract

G-Protein coupled receptors (GPCRs) constitute the largest group of membrane receptors with great pharmacological interest. The signal transduction within cells is leaded by a wide range of native ligands interact and activate GPCRs. Most of these responses are mediated through the interaction of GPCRs with coupling GTP-binding proteins (G-proteins). For the reason of the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. In this paper, we have developed an intensive exploratory approach to predict the coupling preference of GPCRs to heterotrimeric G-proteins. An integrated recognition method combined with Self-Adaptive Immune Algorithm and LogitBoost classifier has been applied in prediction. The result indicates that the proposed method might become a potentially useful tool for GPCR-G-protein coupling prediction, or play a complimentary role to the existing methods in the relevant areas. The method predicts the coupling preferences of GPCRs to three kinds of G-protein subclasses, G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i/o</sub> and G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q/11</sub> , but not G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12/13</sub> for the limited amount.