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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Full correlation analysis of conformational protein dynamics.

Oliver F Lange¹, Helmut Grubmüller

¹Department of Theoretical and Computational Biophysics, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen 37077, Germany.

|September 19, 2007

Summary

Full correlation analysis (FCA) captures nonlinear correlations in biomolecular dynamics, unlike principal component analysis (PCA). FCA reveals more relevant conformational substates and transition pathways by analyzing mutual information in molecular dynamics trajectories.

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Area of Science:

Biophysics
Computational Biology
Structural Biology

Background:

Correlated motions in biomolecules are crucial for functions like allosteric signaling and energy transport.
Principal Component Analysis (PCA) is a common method for analyzing collective motions in molecular dynamics (MD) but is limited to linear correlations.

Purpose of the Study:

Introduce Full Correlation Analysis (FCA), a novel method to quantify all types of correlations, including nonlinear and higher-order ones.
Compare the efficacy of FCA against PCA in analyzing biomolecular dynamics.

Main Methods:

Developed FCA based on mutual information to capture comprehensive correlations.
Applied both PCA and FCA to analyze ~100 ns MD trajectories of T4 lysozyme and neurotensin.

Main Results:

FCA provided better-resolved conformational substates for both T4 lysozyme and neurotensin compared to PCA.
FCA modes showed higher anharmonicity and better alignment with actual transition pathways.
The enhanced resolution and anharmonicity suggest FCA captures more functionally relevant motions.

Conclusions:

FCA offers a more comprehensive approach to analyzing biomolecular dynamics than PCA.
FCA extracts improved collective degrees of freedom for reduced-dimension descriptions of macromolecular motion.
The method holds promise for deeper insights into biomolecular function through dynamics analysis.