Improved Method for Predicting GPCR-GPCR Interaction Pairs

G protein-coupled receptors (GPCRs) can form oligomers, which activate distinct signaling pathways compared to monomeric GPCRs. Oligomerization influences GPCR trafficking, ligand affinity, and signal transduction, and has been implicated in diseases such as schizophrenia and hypertension. Understanding GPCR oligomerization is essential for uncovering disease mechanisms and developing new therapeutic strategies. Previously, we developed a GPCR-GPCR interaction pair predictor (GGIP) utilizing an SVM algorithm. Features for the predictor were generated by quantifying amino acid properties, assigning scores, and averaging these values across the target sequences. In this study, we aimed to enhance the predictive accuracy of GGIP. We evaluated four methods by combining two feature generation techniques with two prediction algorithms. We tested the sequence segmentation-based method from our previous work and automatic feature generation using amino acid sequences with an autoencoder, while evaluating both the SVM and gradient-boosting decision tree (GBDT) as prediction algorithms. Combining segmentation-based feature generation with GBDT yielded the highest performance, achieving an AUROC of over 0.98. Some features could be identified as a basis for predicting that a pair of GPCRs would interact, based on amino acid properties and their arrangement in the three-dimensional structure. Integrating our improved method with disease-related gene expression variation data revealed a significant association between GPCR interaction pairs and the presence of disease-related Differentially Expressed Genes (DEGs). Specifically, around 90% of experimentally determined interaction pairs contained at least one protomer gene classified as a disease-related DEG, suggesting that GPCRs forming interaction pairs are more likely to be associated with disease-related gene expression changes. Among these pairs, we identified the interaction between mGluR2 and 5-HT2AR, which has been postulated to be linked to schizophrenia. Although this association was not registered in the database, we were able to confirm it through published literature. Given the significant association with disease-related DEGs, this approach is critical for identifying disease-associated GPCR interaction pairs and guiding future therapeutic developments. Information & Authors Information Version history Peer review timeline Published Proteins: Structure, Function, and Bioinformatics Version of Record10 Apr 2026Published Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 230views 145downloads Citations Download citation Aoi Fukushima, Arina Ouma, Hiroki Teruse, et al. Improved Method for Predicting GPCR-GPCR Interaction Pairs. Authorea. 26 August 2025. DOI: https://doi.org/10.22541/au.175622975.51984604/v1 DOI: https://doi.org/10.22541/au.175622975.51984604/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.