Prediction method and prediction system for G-protein-coupled receptor-ligand interaction relationship

Abstract

The invention discloses a multi-information integration-based prediction method for G-protein-coupled receptor (GPCR)-ligand interaction. The method comprises the steps of constructing a GPCR-ligand interaction network structure graph of a non orphan receptor; embedding a receptor-receptor relationship between an orphan receptor and the non orphan receptor into a receptor-ligand relationship network; and based on a distance relationship between nodes in the network, constructing a feature relationship of a receptor-ligand interaction pair, and in combination with an ensemble learning method, realizing effective prediction of a potential receptor-ligand relationship. The prediction method suitable for prediction of a GPCR-ligand interaction relationship and prediction of a potential ligandof an orphan GPCR is established and realized for bio-features of the GPCR and the ligand of the GPCR; and an experiment shows that the method is good in prediction effect and has a relatively wide application prospect. The invention furthermore discloses a multi-information integration-based prediction system for the GPCR-ligand interaction.

Description

technical field [0001] The invention relates to the field of biotechnology, in particular to a method for predicting the interaction relationship between G protein-coupled receptors and ligands and its biological application. Background technique [0002] G protein coupled receptor (G-protein coupled receptor, GPCR) is currently the largest transmembrane protein superfamily. The protein structure of G protein-coupled receptors includes seven α-helical regions (TM) across the cell membrane, an intracellular carbon-terminal region (C terminal), an extracellular nitrogen-terminal region (N terminal), and three intracellular loop domain (ICL), three extracellular loop domains (ECL). G protein-coupled receptors are ubiquitous in the human body and are widely involved in various regulatory processes of the human physiological system. Their dysfunction is the main mechanism leading to major diseases including cardiovascular diseases, tumors, metabolic diseases and neurodegenerativ...

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Problems solved by technology

Yamanishi et al. (references Yamanishi Y., Araki M., Gutteridge A., Honda W., Kanehisa M., Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24(13): i232 -i240.) According to the shortest path between nodes in the receptor-ligand network, a method based on the kernel regression model (KRM) is proposed, but the sensitivity of the prediction results of this method is relatively low, and the predicted relationship has no Get experimental verification

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