Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition.
The successful prediction of protein subcellular localization directly from protein primary sequence is useful to protein function prediction and drug discovery. In this paper, by using the concept of pseudo amino acid composition (PseAAC), the mycobacterial proteins are studied and predicted by support vector machine (SVM) and increment of diversity combined with modified Mahalanobis Discriminant (IDQD). The results of jackknife cross-validation for 450 non-redundant proteins show that the overall predicted successful rates of SVM and IDQD are 82.2% and 79.1%, respectively. Compared with other existing methods, SVM combined with PseAAC display higher accuracies.
Lin
Hao
H
Center for Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. hlbedh@yahoo.cn.
Ding
Hui
H
Guo
Feng-Biao
FB
Zhang
An-Ying
AY
Huang
Jian
J
D000596:Amino Acids / Q000032:analysis; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism*; D030562:Databases, Protein; D016002:Discriminant Analysis; D008954:Models, Biological; D009161:Mycobacterium / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism
D000596:Amino Acids; D001426:Bacterial Proteins