title: Prediction of membrane protein types from sequences and position-specific scoring matrices.
abstract: Membrane protein plays an important role in some biochemical process such as signal transduction, transmembrane transport, etc. Membrane proteins are usually classified into five types [Chou, K.C., Elrod, D.W., 1999. Prediction of membrane protein types and subcellular locations. Proteins: Struct. Funct. Genet. 34, 137-153] or six types [Chou, K.C., Cai, Y.D., 2005. J. Chem. Inf. Modelling 45, 407-413]. Designing in silico methods to identify and classify membrane protein can help us understand the structure and function of unknown proteins. This paper introduces an integrative approach, IAMPC, to classify membrane proteins based on protein sequences and protein profiles. These modules extract the amino acid composition of the whole profiles, the amino acid composition of N-terminal and C-terminal profiles, the amino acid composition of profile segments and the dipeptide composition of the whole profiles. In the computational experiment, the overall accuracy of the proposed approach is comparable with the functional-domain-based method. In addition, the performance of the proposed approach is complementary to the functional-domain-based method for different membrane protein types.
authors: Department of Computer Sciences, The City University of Hong Kong, Hong Kong.
authors: Xian
authors: X
authors: Pu
authors: Jian
authors: J
authors: Guo
authors: Howard
authors: H
authors: Leung
authors: Yuanlie
authors: Y
authors: Lin
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification; D008956:Models, Chemical; D013329:Structure-Activity Relationship
chemical_list: D008565:Membrane Proteins