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Feature #2005

Full Correlation Analysis (FCA) as Trajectory Analysis Module

Added by Christian Blau about 3 years ago. Updated over 1 year ago.

Status:
New
Priority:
Normal
Category:
analysis tools
Target version:
Difficulty:
uncategorized
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Description

Why

Full Correlation Analysis (FCA) aids resolve conformational substates and identify actual transition pathways, usually much more successfully so than Principal Components Analysis (PCA).

Algorithm

FCA of a trajectory follows the spirit of PCA in finding important modes of motion through an orthonormal coordinate transformation and subsequent sorting of basis vectors.
While the projection of the trajectory on the subspace spanned by any two PCA basis vectors is linearly uncorrelated, they usually share mutual information (think of a circle).
The flavour of FCA to implement here, minimizes mutual information in the total system as defined by the information entropy H, I=\sum H[x_i]-H[{x}].
It starts from an intial guess of Eigenvectors from a PCA, then iteratively minimizes Mutual Information by 2d-planes rotations of pairs of basis vectors.

Implementation

The algorithm described here has been implmented for gromacs3 by Oliver Lange and then ported to gromacs4.6 by Christian Blau.
It should fit perfectly the trajectoryanlysis framework

References

Lange, Oliver F., and Helmut Grubm├╝ller. "Full correlation analysis of conformational protein dynamics." Proteins: Structure, Function, and Bioinformatics 70.4 (2008): 1294-1312.

[[http://www.mpibpc.mpg.de/9619680/g_fca_ver_1_3.tgz]]
[[http://www.mpibpc.mpg.de/9768579/g_fca_ver_2_0.tgz]]

History

#1 Updated by Mark Abraham over 2 years ago

  • Target version changed from 2018 to future

not happening for 2017

#2 Updated by Gerrit Code Review Bot over 1 year ago

Gerrit received a related DRAFT patchset '1' for Issue #2005.
Uploader: berenger bramas ()
Change-Id: gromacs~master~Id9f8a59a82d9ae2d0ec2a49a19de7363d13c9a44
Gerrit URL: https://gerrit.gromacs.org/7552

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