title: DomSVR: domain boundary prediction with support vector regression from sequence information alone.
abstract: Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the problem of protein domain boundary identification based on novel input profiles extracted from AAindex database. As a result, our method achieves an average sensitivity of approximately 36.5% and an average specificity of approximately 81% for multi-domain protein chains, which is overall better than the performance of published approaches to identify domain boundary. As our method used sequence information alone, our method is simpler and faster.
authors.affiliation: Department of Systems and Computer Science, Howard University, 2400 Sixth Street, NW, Washington, DC 20059, USA. pchen1978@gmail.com
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
authors.forename: Chunmei
authors.initials: C
authors.lastname: Liu
authors.forename: Legand
authors.initials: L
authors.lastname: Burge
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
authors.forename: Mahmood
authors.initials: M
authors.lastname: Mohammad
authors.forename: William
authors.initials: W
authors.lastname: Southerland
authors.forename: Clay
authors.initials: C
authors.lastname: Gloster
authors.forename: Bing
authors.initials: B
authors.lastname: Wang
mesh_terms: D000818:Animals; D030562:Databases, Protein; D006801:Humans; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012044:Regression Analysis; D016415:Sequence Alignment / Q000379:methods*
chemical_list: D011506:Proteins