xProtCAS

xProtCAS identifies conserved accessible surfaces on protein structures to pinpoint interaction interfaces and infer functional and regulatory sites, including post-translational modification loci and regions affected by disease-associated mutations.


Key Features:

  • Integration with AlphaFold2 Models: Uses structural models predicted by AlphaFold2 to analyze protein structures.
  • Autonomous Structural Module Definition: Defines autonomous structural modules within predicted structures for localized analysis.
  • Graph Representation of Modules: Transforms structural modules into graph representations that encode residue topology, accessibility, and conservation.
  • Eigenvector Centrality-Based Approach: Applies an eigenvector centrality metric on module graphs to extract conserved surfaces and discriminate functional versus structural conservation constraints.

Scientific Applications:

  • Protein Function Analysis: Identifies conserved accessible surfaces to support interpretation of protein function and regulation, including potential post-translational modification sites.
  • Disease Mutation Impact Assessment: Highlights conserved surface regions that overlap disease-associated mutations to aid assessment of mutation impact.
  • Proteome-wide Discovery: Enables application to the human proteome to reveal previously uncharacterized conserved surfaces, some containing clinically significant mutations.

Methodology:

Defines structural modules from AlphaFold2 models, converts modules into graphs encoding residue topology, accessibility, and conservation, and applies an eigenvector centrality-based method to identify conserved accessible surfaces and distinguish functional from structural conservation.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
2/23/2024
Last Updated:
11/24/2024

Operations

Publications

Kotb HM, Davey NE. xProtCAS: A Toolkit for Extracting Conserved Accessible Surfaces from Protein Structures. Biomolecules. 2023;13(6):906. doi:10.3390/biom13060906. PMID:37371487. PMCID:PMC10296640.

PMID: 37371487
Funding: - Marie Sklodowska-Curie grant: 860517, A28159, C68484 - Cancer Research UK Senior Cancer Research Fellowship: 860517, A28159, C68484

Links