DominoEffect
DominoEffect identifies and annotates hotspot amino acid residues in proteins to detect positions with elevated mutation rates that may indicate functional impact and drive cancer.
Key Features:
- Hotspot Identification: Pinpoints individual amino acids that accumulate mutations at significantly higher frequency than surrounding residues and highlights specific nucleotide positions prone to change.
- Functional Annotation: Provides detailed annotations of hotspot residues indicating potential roles in protein function and interactions.
- Cancer Driver Prioritization: Aids distinction of driver genes in cancer genome sequencing by highlighting recurrently mutated residues and nucleotide positions.
- Structural Interface Analysis: Extends analysis to structural data from human protein complexes to identify protein–protein and protein–ligand interfaces enriched for mutations.
- State-Dependent Mutation Detection: Detects enrichment of hotspot mutations in proteins capable of existing in both active and inactive states.
- Tumor-Origin Correlation: Identifies correlations between tumor identity and mutation patterns at hotspot residues.
Scientific Applications:
- Driver gene discovery in cancer genomics: Prioritizes candidate driver genes and specific hotspot residues from cancer genome sequencing data.
- Mechanistic interpretation of tumorigenesis: Links hotspot residues to disruption of protein function or interactions to inform molecular mechanisms of cancer.
- Structural mapping of mutations: Maps recurrent mutations onto human protein complex structures and interfaces to reveal interaction-disrupting hotspots.
- Target prioritization for therapeutic intervention: Highlights specific mutated residues and interfaces as potential targets for therapeutic development.
Methodology:
Using a novel methodological framework, DominoEffect systematically investigates properties associated with residues' potential to drive cancer when mutated and extends its analysis to structural data from human protein complexes.
Topics
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Details
- License:
- GPL-3.0
- Tool Type:
- library
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- R
- Added:
- 7/8/2018
- Last Updated:
- 7/20/2019
Operations
Publications
Buljan M, Blattmann P, Aebersold R, Boutros M. Systematic characterization of pan‐cancer mutation clusters. Molecular Systems Biology. 2018;14(3). doi:10.15252/msb.20177974. PMID:29572294. PMCID:PMC5866917.
Funding: - European Research Council: 670821, ERC‐2008‐AdG_20080422, ERC‐2011‐ADG_20110310
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung: 3100A0‐688 107679
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Relation: uses