GPCR-SAS

GPCR-SAS performs comparative sequence and statistical analyses of G protein-coupled receptors (GPCRs) using a curated structural-informed multiple sequence alignment to quantify residue conservation, entropy, covariance and correlations across positions and transmembrane segments.

Key Features:

• Curated structural-informed alignment: Operates on a curated structural-informed multiple sequence alignment of GPCR sequences as the basis for all analyses.
• Percentage of Occurrence and Entropy Analysis: Calculates percentage occurrence of specific amino acids or motifs at positions and computes entropy for positions or ranges to assess variability and conservation.
• Covariance Analysis: Computes covariance between two sequence positions to reveal co-evolutionary patterns.
• Correlation Analysis: Evaluates correlations between amino acids at different positions and between sequence motifs across specified ranges to identify functional relationships.
• Snake-Plot Representation: Generates snake-plot visualizations of residue or motif conservation along transmembrane segments for individual receptors or consensus sequences derived from selected receptor groups.
• Transmembrane (TM) segment motif analysis: Analyzes conserved residues and motifs across TM segments, including regions such as the "transmission switch".

Scientific Applications:

• Ligand selectivity design: Identifies conserved and variable residues across GPCRs to guide the design of more selective ligands.
• Receptor activation mechanisms: Facilitates rationalization of receptor activation mechanisms by enabling focused analysis of regions like the "transmission switch".
• Functional interaction inference: Infers functional relationships and interactions within receptor structures by detecting covariance and correlation among positions and motifs.
• Structural and functional characterization: Supports elucidation of structural and functional nuances of GPCR families relevant to health and disease.

Methodology:

Analyses are performed on a curated structural-informed multiple sequence alignment and include percentage occurrence and entropy calculations, covariance and correlation analyses between positions or ranges, and generation of snake-plot visualizations for individual receptors or consensus sequences.