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