Bayesian statistical studies of the Ramachandran distribution

Alexander Pertsemlidis, Jan Zelinka, John W. Fondon, R. Keith Henderson, Zbyszek Otwinowski

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Abstract

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.

Original languageEnglish (US)
Article number35
Pages (from-to)i-16
JournalStatistical Applications in Genetics and Molecular Biology
Volume4
Issue number1
DOIs
Publication statusPublished - Nov 22 2005

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Keywords

  • Knowledge-based modeling
  • Maximum likelihood
  • Structure refinement
  • Torsional angles

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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