Welcome to the official documentation for GPCRpred, a specialized computational tool developed for the prediction and classification of G-protein coupled receptors (GPCRs). GPCRs are a major class of eukaryotic cell surface receptors involved in signal transduction and are critical targets for drug discovery.

Web Server: [https://webs.iiitd.edu.in/raghava/gpcrpred]

Bhasin M, Raghava GPS (2004). GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Research, 32 (Web Server issue), W383-W389. https://doi.org/10.1093/nar/gkh416

Zenodo:-(https://doi.org/10.5281/zenodo.20079921)

GPCRpred is a support vector machine (SVM) based method designed to classify GPCRs into their respective families and subfamilies. Given that GPCRs often share low sequence similarity, traditional alignment-based methods like BLAST may fail to provide accurate classifications. GPCRpred addresses this by utilizing global sequence descriptors.

The platform is designed to:

• Functional Annotation: Assign uncharacterized protein sequences to specific GPCR families.
• Drug Target Identification: Facilitate the identification of receptors belonging to families of high therapeutic interest.
• Large-Scale Analysis: Process entire proteomes to find and classify potential GPCRs.

• Machine Learning: Utilizes Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel.
• Classification Hierarchy:
  • Level 1: Distinguishes GPCRs from non-GPCRs.
  • Level 2: Classifies GPCRs into five major families (A, B, C, D, and others).
  • Level 3: Further classifies Family A into various subfamilies (e.g., Amine, Peptide, Hormone protein).
• High Performance:
  • Achieved an overall accuracy of 97.3% for classifying the five major families of GPCRs.
  • Achieved nearly 100% accuracy in distinguishing GPCRs from non-GPCRs using dipeptide composition.

• Dipeptide Composition: Encapsulates 400-dimensional vectors to capture the frequency and local order of amino acids, which is more informative than simple amino acid composition for GPCR classification.
• Robust Validation: Tested using 5-fold cross-validation on non-redundant datasets derived from GPCRDB.

The training datasets were obtained from the GPCRDB (March 2003 release) and filtered for non-redundancy. The classification performance was measured using accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC).

• Pharmacology: Identifying new members of GPCR families for targeted drug screening.
• Proteomics: Annotating G-protein signaling components in eukaryotic genomes.
• Structural Biology: Predicting the functional class of receptors where experimental structures are unavailable.

Prof. G.P.S. Raghava Head, Department of Computational Biology Indraprastha Institute of Information Technology (IIIT-Delhi), India. Email: raghava@iiitd.ac.in

This research and associated software are distributed under the Creative Commons Attribution License, allowing for use and distribution with proper credit to the original authors.