Full Correlation Analysis of Conformational Protein Dynamics

Application of FCA to a mock-protein ensemble with known result.

Collective coordinates for protein motions can be extracted from MD simulations with established methods, mainly via calculation of the covariance matrix and subsequent principal component analysis (PCA) [Amadei et al. 1993]. This established approach, however, relies on quasi-harmonic treatment of the configurational ensemble and, therefore, detects only linearly correlated motions. Full Correlation Analysis (FCA) utilizes an information theoretical approach that detects and quantifies any correlated motion [Lange et al. 2006]. In this way, FCA yields a low dimensional representation of protein dynamics that often features more functional details than a representation obtained with PCA [Lange et al. 2008]. The g_fca tool allows to perform a full correlation analysis and can be used within the GROMACS framework. To use it, you also need to install GROMACS; please read the file INSTALL file for instructions.

The software is free for everyone. However, if you use it for publications or presentations I ask you to cite the original publication [Lange et al. 2008]. Please note that the software is distributed with NO WARRANTY OF ANY KIND. The author is not responsible for any losses or damages suffered directly or indirectly from the use of the software. Use it at your own risk. (C) Oliver Lange, 2005

Please download g_fca versions 1.x for GROMACS versions 3.2.x, or g_fca version 2 for GROMACS version 4.6.x.

Download g_fca

Publications and References

Lange, O.; Grubmueller, H.: Full correlation analysis of conformational protein dynamics. Proteins 70 (4), pp. 1294 - 1312 (2008)
Lange, O.; Grubmueller, H.: Generalized correlation for biomolecular dynamics. Proteins: Structure, Function and Bioinformatics 62 (4), pp. 1053 - 1061 (2006)
Amadei A, Linssen ABM, Berendsen HJC
Essential dynamics of proteins
Proteins 17, 412-425 (1993)
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