CompaCt

CompaCt performs automated integrative comparative analysis of complexome profiling datasets across multiple species to characterize and compare protein complexes using orthology information.


Key Features:

  • Automated Integrative Analysis: Performs fully automated analysis of complexome profiling data derived from separation of intact protein complexes and mass spectrometric analysis of fractions.
  • Comparative Approach: Handles datasets from multiple species to identify and compare conserved and taxon-specific components of protein complexes.
  • Orthology Leveraging: Utilizes orthologous relationships between proteins across species to enhance comparative identification of conserved elements and taxon-specific features.

Scientific Applications:

  • Characterization of Protein Complexes: Integrates multiple complexome profiles to improve characterization of protein complex composition and organization.
  • Discovery of Novel Interactors and Complexes: Identifies novel candidate interactors and previously unrecognized protein complexes, including examples such as the emp24 complex, V-ATPase, and mitochondrial ATP synthase.
  • Evolutionary Insights: Enables large-scale comparative studies that provide insights into the evolution of metazoan protein complexes, exemplified by analyses in Anopheles stephensi.

Methodology:

Applies integrative and comparative algorithms to complexome profiling datasets and identifies conserved and taxon-specific elements through orthology-based methods.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
10/15/2023
Last Updated:
11/24/2024

Operations

Data Inputs & Outputs

Clustering

Publications

van Strien J, Evers F, Lutikurti M, Berendsen SL, Garanto A, van Gemert G, Cabrera-Orefice A, Rodenburg RJ, Brandt U, Kooij TWA, Huynen MA. Comparative Clustering (CompaCt) of eukaryote complexomes identifies novel interactions and sheds light on protein complex evolution. PLOS Computational Biology. 2023;19(8):e1011090. doi:10.1371/journal.pcbi.1011090. PMID:37549177. PMCID:PMC10434966.

PMID: 37549177
Funding: - ZonMw: 91217009 - Nederlandse Organisatie voor Wetenschappelijk Onderzoek: 714.017.004, 864.13.009

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