title: Effective moment feature vectors for protein domain structures.
abstract: Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100-400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity.
authors.affiliation: School of Life Science, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China ; Department of Computer Science, The University of Hong Kong, Hong Kong, China.
authors.forename: Jian-Yu
authors.initials: JY
authors.lastname: Shi
authors.affiliation: Department of Computer Science, The University of Hong Kong, Hong Kong, China.
authors.forename: Siu-Ming
authors.initials: SM
authors.lastname: Yiu
authors.affiliation: School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China.
authors.forename: Yan-Ning
authors.initials: YN
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, The University of Hong Kong, Hong Kong, China.
authors.forename: Francis Yuk-Lun
authors.initials: FY
authors.lastname: Chin
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D011506:Proteins