KSL-95-18
## A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data Sources

**Reference: **
Altman, R. B. A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data Sources. Knowledge Systems Laboratory, Medical Computer Science, February, 1995.

**Abstract:** Modeling the structure of biological molecules is critical for understanding
how these structures perform their function, and for designing compounds to
modify or enhance this function (for medicinal or industrial purposes). The
determination of molecular structure involves defining three-dimensional
positions for each of the constituent atoms using a variety of experimental,
theoretical and empirical data sources. Unfortunately, each of these data
sources can be noisy or not available in sufficient abundance to determine
the precise position of each atom. Instead, some atomic positions are
precisely defined by the data, and others are poorly defined. An
understanding of structural uncertainty is critical for properly interpreting
structural models. We have developed a Bayesian approach for determining the
coordinates of atoms in a three-dimensional space. Our algorithm takes as
input a set of probabilistic constraints on the coordinates of the atoms, and
an a priori distribution for each atom location. The output is a maximum a
posteriori (MAP) estimate of the location of each atom. We introduce
constraints as updates to the prior distributions. In this paper, we describe
the algorithm and show its performance on three data sets. The first data
set is synthetic and illustrates the convergence properties of the method.
The other data sets comprise real biological data for a protein (the trp
repressor molecule) and a nucleic acid (the transfer RNA fold). Finally, we
describe how we have begun to extend the algorithm to make it suitable for
non-Gaussian constraints.

Full paper available as ps.

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