Advertisement

Likelihood and Empirical Bayes Superposition of Multiple Macromolecular Structures

  • Douglas L. Theobald
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Superpositioning plays a fundamental role in current macromolecular structural analysis. By orienting structures so that their atoms match closely, superpositioning enables the direct analysis of conformational similarities and differences in three-dimensional Euclidean space. Superpositioning is a special case of Procrustes problems, in which coordinate vector sets are optimally oriented via rigid body rotations and translations. Optimal transformations are conventionally determined by minimizing the sum of the squared distances between corresponding atoms in the structures. However, the ordinary unweighted least-squares (OLS) criterion can produce inaccurate results when the atoms have heterogeneous variances (heteroscedasticity) or the atomic positions are correlated, both of which are common features of real data. In contrast, model-based probabilistic methods can easily allow for heterogeneous variances and correlations. Our likelihood treatment of the superposition problem results in more accurate superpositions and provides a framework for a full Bayesian analysis.

Keywords

Covariance Matrix Singular Value Decomposition Sample Covariance Matrix Inverse Gamma Inverse Gamma Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Much of this methodology was initially developed with Deborah S. Wuttke at the University of Colorado at Boulder. I thank Phillip Steindel, Thomas Hamelryck, Kanti Mardia, Ian Dryden, Colin Goodall, and Subhash Lele for helpful comments and criticism. This work was supported by NIH grants 1R01GM094468 and 1R01GM096053.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Brandeis UniversityWalthamUSA

Personalised recommendations