GPCRsclass: This webserver predicts amine type of G-protein coupled receptors

Welcome to the official documentation for GPCRsclass, a computational tool developed to recognize and classify the amine subfamily of G-protein-coupled receptors (GPCRs). Amine-type receptors are major drug targets for treating nervous disorders and psychiatric diseases, making their accurate identification of paramount interest for pharmaceutical research. Web Server: http://www.imtech.res.in/raghava/gpcrsclass/(https://webs.iiitd.edu.in/raghava/gpcrsclass) Citation Bhasin, M., & Raghava, G. P. S. (2005). GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors. Nucleic Acids Research, 33(Web Server issue), W143-W147. https://doi.org/10.1093/nar/gki351 GitHub:-https://github.com/Manish-IIITD-repository/GPCRsclass About the Platform GPCRsclass utilizes Support Vector Machines (SVM) to classify amine-type receptors based on their primary sequence. The method builds upon the observation that different types of amine receptors have distinct amino acid compositions. It provides a multi-level classification scheme to categorize these receptors into specific subfamilies. Classification Hierarchy The tool classifies receptors into the following subfamilies: Acetylcholine Adrenoceptor Dopamine Histamine Serotonin Key Features Prediction Modules Amino Acid Composition: Classifies receptors based on the frequency of the 20 natural amino acids. Dipeptide Composition: Utilizes the frequency of pairs of adjacent amino acids to capture local order information. Hybrid Approach: Combines various sequence-based features to achieve superior classification performance. Performance Highlights High Accuracy: The dipeptide-based SVM model achieved an overall accuracy of 99.4% for classifying the five amine subfamilies. Robust Validation: Models were rigorously evaluated using 5-fold cross-validation on a dataset of 167 amine-type GPCRs. Low False Positives: Designed to effectively discriminate amine-type receptors from other types of GPCRs and non-GPCR proteins. Technical Overview GPCRsclass leverages the SVM-light package to handle high-dimensional sequence data. Feature Type Number of Descriptors Accuracy (%) Amino Acid Composition 20 89.8% Dipeptide Composition 400 99.4% Model Functionality Subfamily Recognition: Accurately determines which specific amine ligand (e.g., dopamine vs. serotonin) a query GPCR is likely to bind. Sequence Scanning: Users can submit one or more protein sequences to identify potential amine-type GPCRs. Detailed Reports: Provides a probability or confidence score for each predicted subfamily classification. Applications Drug Discovery: Identifying novel amine receptors as potential targets for neurological and psychiatric drugs. Genome Annotation: Automatically classifying GPCR sequences identified in newly sequenced genomes. Structural Biology: Providing a basis for comparative modeling and docking studies of amine-type receptors. Contact & Authors Prof. Gajendra P. S. Raghava Bioinformatics Center, Institute of Microbial Technology, Sector 39A, Chandigarh, India. Email: [e-mail] License This project is an open-access resource and is available for academic and research use provided the original work is properly credited.