title: Prediction of protein subcellular location using a combined feature of sequence.
abstract: To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.
authors.affiliation: Institute of Automation, National University of Defense Technology, Changsha 410073, Peoples Republic of China. gqb_kd@yahoo.com.cn
authors.forename: Qing-Bin
authors.initials: QB
authors.lastname: Gao
authors.forename: Zheng-Zhi
authors.initials: ZZ
authors.lastname: Wang
authors.forename: Chun
authors.initials: C
authors.lastname: Yan
authors.forename: Yao-Hua
authors.initials: YH
authors.lastname: Du
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D005057:Eukaryotic Cells / Q000378:metabolism; D042541:Intracellular Space / Q000737:chemistry* / Q000378:metabolism; D011387:Prokaryotic Cells / Q000378:metabolism; D011506:Proteins / Q000032:analysis* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins