G12 DREADD Receptor Design for Quantitative GPCR Coupling Prediction
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Solution Overview
Problem
Current methods for determining G-protein coupling with G-protein coupled receptors (GPCRs) are laborious, non-standardized, and lack quantitative data, particularly for G12/13 coupling, leading to incomplete understanding of GPCR signaling and limited predictive capabilities for orphan receptors.
Innovation Solution
A computer-implemented method using amino acid sequence and 3D structural data to predict coupling or no coupling between G-proteins and GPCRs, employing a machine learning classifier to optimize GPCR design for predetermined G-protein coupling profiles, including the development of Designer Receptor Exclusively Activated by Designer Drugs (DREADD) and cell-based assays using a split luciferase complement system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional experimental methods are used to determine G-protein coupling profiles, then coupling information can be obtained, but the process becomes laborious and lacks standardization
Solution Approach 1:
The patent replaces traditional mechanical/wet-lab experimental methods with a computational prediction system that uses amino acid sequence data and machine learning algorithms to determine G-protein coupling profiles, thereby eliminating the laborious and time-consuming nature of experimental approaches while maintaining measurement precision
Solution Approach 2:
The patent creates a computational model that copies the essential features of G-protein coupling interactions through sequence analysis, allowing prediction of coupling probabilities without requiring physical experimental procedures, thus reducing time consumption while preserving measurement accuracy
2Measurement precision
If traditional methods are used to determine G-protein coupling, then some coupling information is obtained, but quantitative data and selectivity are limited
Solution Approach 1:
The patent transforms the approach from qualitative experimental observation to quantitative prediction by analyzing amino acid sequence parameters and using machine learning models to calculate coupling probabilities, thereby providing precise quantitative data while managing system complexity through computational methods
Solution Approach 2:
The patent introduces amino acid sequence data as an intermediary that mediates between the GPCR structure and G-protein coupling outcome, allowing quantitative prediction without direct complex experimental measurement, thus improving measurement precision while controlling device complexity
3Productivity
If sequence-based prediction methods are used, then coupling prediction is faster, but sensitivity and selectivity are reduced
Solution Approach 1:
The patent combines multiple sequence features, structural data, and machine learning approaches into a composite prediction system that maintains high productivity through computational efficiency while achieving improved sensitivity and selectivity through the integration of multiple predictive indicators
Solution Approach 2:
The patent creates a universal prediction system that can analyze various GPCRs and G-proteins using the same computational framework, enabling fast prediction across different targets while maintaining sensitivity through standardized multi-feature analysis
Data Source
AI summary
Disclosed is a Designer Receptor Exclusively Activated by Designer Drugs (DREADD), and an amino acid sequence for determining coupling or no coupling between G-protein and G-protein coupled receptor (GPCR) mediated by a GPCR ligand in a cell based assay.