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

Sasakthi S. Abeysinghe, Tao Ju, Wah Chiu, Matthew Baker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - SPM 2007
Subtitle of host publicationACM Symposium on Solid and Physical Modeling
Pages223-232
Number of pages10
DOIs
StatePublished - 2007
EventSPM 2007: ACM Symposium on Solid and Physical Modeling - Beijing, China
Duration: Jun 4 2007Jun 6 2007

Publication series

NameProceedings - SPM 2007: ACM Symposium on Solid and Physical Modeling

Conference

ConferenceSPM 2007: ACM Symposium on Solid and Physical Modeling
Country/TerritoryChina
CityBeijing
Period06/4/0706/6/07

Keywords

  • Cryo-EM
  • Graph matching
  • Protein structure
  • Shape matching

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