iPAC

iPAC identifies somatic amino acid mutation clustering within proteins by integrating protein tertiary structure to detect spatially non-random clusters relevant to cancer.

Key Features:

• Integration of Tertiary Structure: iPAC incorporates three-dimensional protein conformations to analyze mutation proximity in space rather than only along the linear sequence.
• Use of Protein Data Bank structures: iPAC maps mutations onto tertiary structures using structural data from the Protein Data Bank (PDB) to improve cluster detection.
• Identification of Driver Mutations: iPAC pinpoints candidate driver somatic amino acid substitutions and clustered mutations in oncogenes such as KRAS, PI3KC α, EGFR, and EIF2AK2.

Scientific Applications:

• Cancer genomics analysis: Detection of spatial mutation clusters to refine interpretation of somatic variants in cancer studies.
• Target identification for therapies: Structural clustering information to highlight potential drug-targetable regions within proteins for targeted therapy development.
• Discovery of novel oncogenic clusters: Identification of previously unrecognized proteins or regions exhibiting non-random mutational clustering.

Methodology:

iPAC integrates three-dimensional protein structures from the Protein Data Bank with somatic mutation data from the Catalogue of Somatic Mutations in Cancer (COSMIC) to detect non-random spatial clustering of amino acid substitutions not apparent from linear sequence analysis alone.