Shape modeling and matching in identifying protein structure from low-resolution images

  • Authors:

  • Sasakthi S. Abeysinghe;Tao Ju;Wah Chiu;Matthew Baker

  • Affiliations:

  • Washington University in St. Louis;Washington University in St. Louis;Baylor College of Medicine;Baylor College of Medicine

  • Venue:
  • Proceedings of the 2007 ACM symposium on Solid and physical modeling
  • Year:
  • 2007

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Abstract

In this paper, we describe a novel, shape-modeling approach to recovering 3D protein structures from volumetric images. The input to our method is a sequence of α-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of α-helices have been detected. Our task is to identify the correspondence between the two sets of helices, which will shed light on how the protein folds in space. The central theme of our approach is to cast the correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model both the shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem. To compute the matching, we developed an optimal algorithm based on the A*-search with several choices of heuristic functions. As demonstrated in a suite of real protein data, the shape-modeling approach is capable of correctly identifying helix correspondences in noise-abundant volumes with minimal or no user intervention.