GraphPAC

GraphPAC identifies mutational clusters of amino acids within protein tertiary structures to detect potential driver mutations in cancer.

Key Features:

• Graph theoretical approach: Uses a graph-theoretical model to incorporate three-dimensional protein spatial relationships into mutational clustering analyses.
• Integration with structural data (PDB): Maps amino acid positions onto Protein Data Bank (PDB) coordinates to analyze mutations in tertiary structure context.
• Integration of somatic mutation data (COSMIC): Incorporates mutational datasets from the Catalogue of Somatic Mutations in Cancer (COSMIC) for comprehensive mutation analysis.
• Novel cluster detection: Detects mutational clusters that may be missed by sequence-based analyses by leveraging structural information.
• Extension of iPAC methodology: Extends the iPAC approach by explicitly accounting for tertiary structure in clustering analyses.

Scientific Applications:

• Oncology research: Identifies spatial mutational clustering to aid the study of tumorigenesis and potential driver mutations.
• Analysis of oncogenes: Applied to identify mutational clusters in oncogenes such as EGFR and KRAS.
• Discovery in other proteins: Applied to detect clusters in proteins including DPP4 and NRP1 that were not identified by existing sequence-based methods.

Methodology:

Implements a graph-theoretical model that maps amino acid positions using Protein Data Bank (PDB) tertiary-structure coordinates, integrates somatic mutation data from COSMIC, and functions as an extension of iPAC.