Determining Alpha-Helix Correspondence for Protein Structure Prediction from Cryo-EM Density Maps, Master\u27s Thesis, May 2007

Determining protein structure is an important problem for structural biologists, which has received a significant amount of attention in the recent years. In this thesis, we describe a novel, shape-modeling approach as an intermediate step towards recovering 3D protein structures from volumetric images. The input to our method is a sequence of alpha-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of alpha-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

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

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 alpha-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of alpha-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