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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Bayesian Statistical Studies of the Ramachandran Distribution

Alexander Pertsemlidis1 / Jan Zelinka2 / John W. Fondon III3 / R. Keith Henderson4 / Zbyszek Otwinowski5

1UT Southwestern Medical Center

2UT Southwestern Medical Center

3UT Southwestern Medical Center

4UT Southwestern Medical Center

5UT Southwestern Medical Center

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 4, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1165, November 2005

Publication History

Published Online:
2005-11-22

We describe a method for the generation of knowledge-based potentials and apply it to the observed torsional angles of known protein structures. The potential is derived using Bayesian reasoning, and is useful as a prior for further such reasoning in the presence of additional data. The potential takes the form of a probability density function, which is described by a small number of coefficients with the number of necessary coefficients determined by tests based on statistical significance and entropy. We demonstrate the methods in deriving one such potential corresponding to two dimensions, the Ramachandran plot. In contrast to traditional histogram-based methods, the function is continuous and differentiable. These properties allow us to use the function as a force term in the energy minimization of appropriately described structures. The method can easily be extended to other observable angles and higher dimensions, or to include sequence dependence and should find applications in structure determination and validation.

Keywords: knowledge-based modeling; maximum likelihood; structure refinement; torsional angles

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