Bios2cor

Bios2cor integrates dynamic correlations from molecular dynamics (MD) simulations and evolutionary correlations from multiple sequence alignments (MSAs) as an R package to identify residues critical for protein function.


Key Features:

  • Integration of dynamic and evolutionary data: Combines sidechain motion correlations from MD simulations with evolutionary covariation derived from MSAs to analyze residue relationships.
  • Correlation/covariation score computation: Computes correlation or covariation scores between positions within an MSA and between sidechain dihedral angles or rotamers in MD trajectories.
  • Analytical, visualization, and interpretation tools: Provides analysis, visualization, and interpretation capabilities for correlation data to support residue-level functional inferences.

Scientific Applications:

  • Structure–function analysis: Identifies residues whose combined dynamic and evolutionary correlations implicate roles in protein function and mechanism.
  • Stability and interaction-site analysis: Highlights residues that may influence protein stability or mediate interaction sites via correlated motions and conservation patterns.
  • Drug target and functional residue identification: Pinpoints candidate residues for functional perturbation or drug design based on combined dynamic and evolutionary signals.

Methodology:

Operates on either an MSA or an MD trajectory as input, computes correlation/covariation scores between MSA positions or MD sidechain dihedral angles/rotamers, and performs analysis and visualization of the resulting correlation data.

Topics

Details

License:
GPL-3.0
Tool Type:
library
Programming Languages:
R
Added:
3/19/2021
Last Updated:
4/21/2021

Operations

Publications

Taddese B, Garnier A, Deniaud M, Henrion D, Chabbert M. Bios2cor: an R package integrating dynamic and evolutionary correlations to identify functionally important residues in proteins. Bioinformatics. 2021;37(16):2483-2484. doi:10.1093/bioinformatics/btab002. PMID:33471079.

PMID: 33471079
Funding: - The French National Research Agency: ANR-11-BSV2-026 - GENCI: 100567