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Angle‐distance image matching techniques for protein structure comparison
Author(s) -
Chu ChiaHan,
Tang Chuan Yi,
Tang ChengYin,
Pai TunWen
Publication year - 2008
Publication title -
journal of molecular recognition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 79
eISSN - 1099-1352
pISSN - 0952-3499
DOI - 10.1002/jmr.914
Subject(s) - similarity (geometry) , structural similarity , pattern recognition (psychology) , computer science , matching (statistics) , sequence (biology) , image (mathematics) , structural classification of proteins database , transformation (genetics) , artificial intelligence , cluster analysis , structural alignment , protein structure database , data mining , function (biology) , protein structure , mathematics , sequence alignment , peptide sequence , biology , sequence database , statistics , biochemistry , gene , genetics , evolutionary biology
Proteins that contain similar structural elements often have analogous functions regardless of the degree of sequence similarity or structure connectivity in space. In general, protein structure comparison (PSC) provides a straightforward methodology for biologists to determine critical aspects of structure and function. Here, we developed a novel PSC technique based on angle‐distance image ( A‐D image) transformation and matching, which is independent of sequence similarity and connectivity of secondary structure elements (SSEs). An A‐D image is constructed by utilizing protein secondary structure information. According to various types of SSEs, the mutual SSE pairs of the query protein are classified into three different types of sub‐images. Subsequently, corresponding sub‐images between query and target protein structures are compared using modified cross‐correlation approaches to identify the similarity of various patterns. Structural relationships among proteins are displayed by hierarchical clustering trees, which facilitate the establishment of the evolutionary relationships between structure and function of various proteins. Four standard testing datasets and one newly created dataset were used to evaluate the proposed method. The results demonstrate that proteins from these five datasets can be categorized in conformity with their spatial distribution of SSEs. Moreover, for proteins with low sequence identity that share high structure similarity, the proposed algorithms are an efficient and effective method for structural comparison. Copyright © 2008 John Wiley & Sons, Ltd.