Analysis of three-dimensional protein images

  • Authors:

  • Laurence Leherte;Janice Glasgow;Kim Baxter;Evan Steeg;Suzanne Fortier

  • Affiliations:

  • Laboratoire de Physico-Chimie Informatique, Facultés Universitaires Notre-Dame de la Paix, Namur, Belgium;Department of Computing and Information Science, Queen's University, Kingston, Ontario, Canada;Department of Computing and Information Science, Queen's University, Kingston, Ontario, Canada;Molecular Mining Corp., PARTEQ Innovations, Queen's University, Kingston, Ontario, Canada;Departments of Computing and Information Science and Chemistry, Queen's University, Kingston, Ontario, Canada

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 1997

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Abstract

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as α-helices and β-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.