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Ferreting out correlations from trajectory data.

Robert I Cukier¹

¹Department of Chemistry, Michigan State University, East Lansing, Michigan 48824-1322, USA. cukier@chemistry.msu.edu

The Journal of Chemical Physics

|December 16, 2011

Summary

Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA) reveal correlations between protein backbone and side chain motions. These methods offer insights into functional movements and conformational changes, outperforming Principal Component Analysis (PCA) in certain applications.

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Area of Science:

Biophysics
Computational Biology
Structural Biology

Background:

Proteins exhibit complex motions across various scales due to their energy landscapes.
Principal Component Analysis (PCA) is commonly used to identify functional motions in protein trajectory data.
Analyzing correlated motions between different parts of a protein is crucial for understanding its function.

Purpose of the Study:

To formulate and apply Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA) for protein trajectory data analysis.
To compare the effectiveness of MCA and CCA with PCA, particularly for systems with complex conformational changes.
To investigate the correlations between backbone and side chain motions in the peptide met-enkephalin.

Main Methods:

Formulation of MCA and CCA using singular value decomposition for protein trajectory analysis.
Application of MCA and CCA to met-enkephalin peptide dynamics.
Utilized internal coordinates (dihedral angles and atom distances) for a more reliable analysis basis.
Comparison with Principal Component Analysis (PCA) in Cartesian coordinates.

Main Results:

MCA and CCA effectively partition protein coordinates into measurement domains to identify correlated motions.
Internal coordinates proved more reliable than Cartesian coordinates for PCA, MCA, and CCA.
MCA identified specific correlations between backbone dihedral angles and side chain atom distances in met-enkephalin.
The study highlights limitations of PCA and the advantages of MCA/CCA for certain analyses.

Conclusions:

MCA and CCA provide powerful frameworks for analyzing correlated motions in proteins.
These methods can guide strategies to influence protein conformations and functions.
MCA and CCA have broad applicability to proteins with domain rearrangements or multi-subunit proteins exhibiting correlated motions.