A PROBABILISTIC APPROACH TO DETERMINING BIOLOGICAL STRUCTURE - INTEGRATING UNCERTAIN DATA SOURCES

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.