TY - GEN
T1 - Shape modeling and matching in identifying protein structure from low-resolution images
AU - Abeysinghe, Sasakthi S.
AU - Ju, Tao
AU - Chiu, Wah
AU - Baker, Matthew
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Cryo-EM
KW - Graph matching
KW - Protein structure
KW - Shape matching
UR - http://www.scopus.com/inward/record.url?scp=35348865170&partnerID=8YFLogxK
U2 - 10.1145/1236246.1236278
DO - 10.1145/1236246.1236278
M3 - Conference contribution
AN - SCOPUS:35348865170
SN - 1595936661
SN - 9781595936660
T3 - Proceedings - SPM 2007: ACM Symposium on Solid and Physical Modeling
SP - 223
EP - 232
BT - Proceedings - SPM 2007
T2 - SPM 2007: ACM Symposium on Solid and Physical Modeling
Y2 - 4 June 2007 through 6 June 2007
ER -