Decoding Functional Specialization in GPCRs through Evolution-Guided Residue Profiling - Supplementary Information

This directory contains supplementary information for the manuscript "Decoding Functional Specialization in GPCRs through Evolution-Guided Residue Profiling".

Directory Structure

Supplementary Information/
├── README.md                           # This file
├── Supplementary_Table.xlsx            # Comprehensive data table with all annotations
├── Figures/                            # Manuscript figures
│   ├── Figure1.png                    # Automated ortholog identification workflow
│   ├── Figure2.png                    # Class A, olfactory, and Class T analysis
│   ├── Figure3.png                    # Class B1 and B2 analysis
│   ├── Figure4.png                    # Class F analysis
│   ├── Figure5.png                    # Class C analysis
│   └── Figure6.png                    # Subtype-specific selective residues in ligand and transducer selectivity
└── code_and_data/                     # Computational analysis files
    ├── benchmark/                     # Interactive benchmark visualizations
    ├── ortholog_pipeline/             # Ortholog identification pipeline
    ├── CRs_and_SRs_on_structure/     # Structural visualizations
    └── scatter_plots_and_CR_SR_calculation/  # Scatter plot generation

Supplementary Table

Supplementary_Table.xlsx contains comprehensive data from the GPCR conservation analysis, including:

Data Content

• Scatter Plot Information: Conservation percentages and entropy values for all residues
• Initial Annotations: Detailed functional annotations assigned during analysis
• Simplified Annotations: Streamlined functional categories for visualization
• CR/SR Information: Classification of residues as Common (CR) or Selective (SR)
• Class-Specific Data: Separate sheets for each GPCR class (A, B1, B2, C, F, T, Olfactory)

Simplified Annotation Categories

• Ligand: Residues involved in ligand binding
• Transducer: Residues involved in G protein/transducer binding
• Known Motifs: Conserved motifs (CWxP, NPxxY, PIF, DRY, Na+ Pocket)
• Disulfide Bridge: Cysteine residues forming disulfide bonds
• Cholesterol: Cholesterol binding site residues
• ICL2: Intracellular loop 2 residues (Class T specific)
• Tethered Agonist: Residues involved in tethered agonist binding (Class B2)
• VFT Ligand: Venus Flytrap domain ligand binding residues (Class C)
• Allosteric Modulator: Residues involved in allosteric modulation
• WNT Binding: Residues involved in WNT protein binding (Class F)
• Other: Residues without specific functional annotation

Data Organization

Each GPCR class has its own worksheet containing:

• Residue numbers (using class-specific reference proteins)
• GPCRdb numbers for the given reference receptor
• Family-wide conservation percentages across family members
• Family-wide entropy values for sequence variability
• Initial detailed annotations
• Simplified functional categories
• CR/SR classification with thresholds

Figures

The Figures/ directory contains all manuscript figures:

• Figure 1: Automated ortholog identification and conservation analysis workflow
• Figure 2: Family-wide conservation and structural mapping for Class A, olfactory, and Class T receptors
• Figure 3: Conservation and structural organization for Class B1 and adhesion (B2) GPCRs
• Figure 4: Evolutionary conservation patterns in Class F GPCRs across distinct domains
• Figure 5: Evolutionary conservation patterns in Class C GPCRs across different domains
• Figure 6: Subtype-specific selective residues in ligand and transducer selectivity

Code and Data

The code_and_data/ directory contains all computational analysis files:

benchmark/

• Interactive HTML visualizations for benchmarking evolutionary predictions against experimental data
• 4 visualization files:
  • FigS1_DMS_benchmark.html - Deep mutational scanning data (ADRB2, MC4R, GPR68, V2R)
  • FigS2_correlation_plots.html - Correlation analyses across experimental datasets
  • FigS3_evolutiıon_benchmark.html - Evolution-based functional predictions (OPSD, Sanders)
  • Fig2cde.html - Combined view (ClinVar, mutational intolerance, activation network)
• Consolidated mapping: Single unified GPCR residue mapping file for mapping class A receptors to HRH2
• Data folder: Experimental datasets including DMS studies, clinical variants, and evolutionary data
• Analysis scripts: Python scripts for calculating combined mutational intolerance scores
• See benchmark/README.md for detailed documentation

ortholog_pipeline/

• Complete pipeline for identifying GPCR orthologs
• Python scripts for sequence processing and phylogenetic analysis
• Human protein sequences organized by GPCR class
• SLURM scripts for cluster execution

CRs_and_SRs_on_structure/

• Structural visualizations of CR/SR mapping
• PyMOL session files for interactive 3D analysis
• AlphaFold2 models and experimental structures
• PNG images used as manuscript figures

scatter_plots_and_CR_SR_calculation/

• Scripts for generating scatter plots
• CR/SR calculation algorithms
• Label files with simplified annotations
• Special analysis for Class F GPCRs
• Class alignments: Multiple sequence alignments used for receptor mapping and analysis

Usage

Accessing Data

1. Open Supplementary_Table.xlsx for comprehensive analysis data
2. Navigate to specific class worksheets for detailed information
3. Use conservation and entropy values for further analysis

Viewing Figures

1. Open PNG files in any image viewer
2. Figures correspond to manuscript figures with same numbering

Interactive Benchmark Visualizations

1. Navigate to code_and_data/benchmark/
2. Open any HTML file in a modern web browser (Chrome, Firefox, Edge)
3. Explore interactive plots:
   • FigS1: Deep mutational scanning benchmarks (10 panels)
   • FigS2: Correlation analyses across datasets (20 panels)
   • FigS3: Evolution-based predictions (2 panels)
   • Fig2cde: Combined horizontal view (3 panels)
4. Features: hover for details, click legend to toggle, pan and zoom
5. All data loads locally - no internet connection required

Running Analysis

1. Navigate to code_and_data/ for computational scripts
2. Follow README files in each subdirectory for specific instructions
3. Use provided SLURM scripts for cluster execution
4. Run benchmark/data/calculate_combined_significance.py to calculate combined mutational intolerance scores

Alignment Strategy

The analysis uses representative sequences ("reps") and multiple sequence alignments to determine receptor sets and mapping:

Representative Sequence Selection

• CD-HIT clustering identifies representative sequences from ortholog sets
• Top 5 largest clusters are selected as representatives
• Representatives provide evolutionary diversity while reducing computational complexity

Alignment Process

1. Initial Alignment: Representative sequences + human sequences aligned using MAFFT
2. Human Extraction: Human sequences extracted from the alignment
3. Final Analysis: Family-wide analysis performed on human-only alignments

Alignment Types

• Full alignments: Complete receptor sequences with representatives
• Human-only alignments: Extracted human sequences for family-wide analysis
• Domain-specific alignments: Specific regions (VFT domain, CRD domain, etc.)

Impact of Alignment Choice

Different alignments produce different outcomes:

• Receptor Set: Different representative sequences change receptor composition
• Mapping: Alignment positions determine residue numbering and conservation calculations
• Analysis Scope: Domain-specific alignments focus on particular functional regions

Alignment Files

The class_alignments/ directory contains:

• Standard alignments with representatives
• Human-only extracted alignments
• Domain-specific alignments for particular regions
• Scripts for extracting human sequences from representative alignments

Data Availability

All data and code are available at: https://github.com/CompGenomeLab/GPCR_Family_Divergence

Contact

For questions or issues with the data or code, please contact:

• Berkay Selcuk: selcuk.1@buckeyemail.osu.edu
• Ogün Adebali: oadebali@sabanciuniv.edu