icorrelationplus

icorrelationplus extracts and analyzes dynamical pairwise correlations in proteins from molecular dynamics (MD) trajectories and normal-mode analyses of coarse-grained models to elucidate residue coupling and functional relationships.


Key Features:

  • Calculation of Pairwise Correlations: Computes pairwise dynamical correlations between residues from large MD trajectories and normal-mode analyses of coarse-grained models.
  • Correlation Map Visualization: Produces correlation maps to represent residue-residue dynamical couplings for interpretation of protein dynamics.
  • Identification of Key Residues: Identifies residues with strong dynamical coupling that may contribute to allosteric regulation or functional effects of mutations.
  • Integration with Sequence Coevolution Data: Enables combination and comparison of dynamical correlation data with sequence coevolution information.

Scientific Applications:

  • Allosteric Regulation Studies: Detects long-distance dynamical couplings and residues implicated in transmission of allosteric signals.
  • Protein Function Analysis: Assists in dissecting the functional implications of residue interactions and mutational effects on dynamics.
  • Comparative Dynamics and Evolution: Compares dynamical coupling patterns with evolutionary sequence coevolution to link dynamics and conservation.

Methodology:

Extracts correlation data from extensive MD simulations and from normal-mode analyses of coarse-grained models to examine residue interactions.

Topics

Details

License:
LGPL-3.0
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
4/30/2022
Last Updated:
4/30/2022

Operations

Publications

Tekpinar M, Neron B, Delarue M. Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by<i>correlationplus</i>. Journal of Chemical Information and Modeling. 2021;61(10):4832-4838. doi:10.1021/acs.jcim.1c00742. PMID:34652149.

Documentation