SpacePAC

SpacePAC identifies clusters of somatic mutations within protein tertiary structures to localize mutational hotspots and distinguish candidate driver mutations from passenger mutations in cancer.

Key Features:

• Three-dimensional spatial clustering: Detects clusters of amino acid substitutions directly within protein tertiary structures using 3D spatial context.
• Data integration: Integrates somatic mutation data from the Catalogue of Somatic Mutations in Cancer (COSMIC) with structural information from the Protein Data Bank (PDB).
• Driver vs passenger discrimination: Leverages spatial clustering patterns to prioritize candidate driver mutations over likely passenger mutations.
• Mutational hotspot localization: Localizes critical mutational hotspots within protein structures for high-resolution mapping of mutation dense regions.
• Application to specific proteins: Has been applied to proteins including FGFR3, CHRM2, and demonstrated on BRAF and ALK.

Scientific Applications:

• Driver mutation identification: Prioritizes candidate driver mutations based on spatial clustering within protein structures.
• Hotspot mapping in oncogenes: Maps mutational hotspots in proteins such as FGFR3, CHRM2, BRAF, and ALK to inform cancer research.
• Oncology research: Supports studies of tumorigenesis by relating mutation spatial patterns to functional regions of proteins.
• Molecular biology investigations: Enables structural-context analyses of amino acid substitutions to study protein function and mutation impact.

Methodology:

Integrates COSMIC somatic mutation data with PDB-derived protein tertiary structures and performs three-dimensional spatial clustering of amino acid substitutions to identify mutation clusters.