Shape modeling and matching in identifying 3D protein structures

In this paper, we describe a novel geometric approach in the process of recovering 3D protein structures from scalar volumes. The input to our method is a sequence of alpha-helices that make LIP I protein, and a low-resolution protein density volume 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 shares as attributed relational graphs. and formulaic a constrained inexact graph matching problem. To compute the matching, we developed all optimal algorithm based on the A*-search with several choices of heuristic functions. As demonstrated in a Suite of synthetic and authentic inputs, the shape-modeling approach is capable of identifying, helix correspondences in noise-abundant volumes at high accuracy with minimal or no user intervention. 2008 Elsevier Ltd. All Lights reserved.