Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition.
Integral membrane proteins are central to many cellular processes and constitute approximately 50% of potential targets for novel drugs. However, the number of outer membrane proteins (OMPs) present in the public structure database is very limited due to the difficulties in determining structure with experimental methods. Therefore, discriminating OMPs from non-OMPs with computational methods is of medical importance as well as genome sequencing necessity. In this study, some sequence-derived structural and physicochemical features of proteins were incorporated with amino acid composition to discriminate OMPs from non-OMPs using support vector machines. The discrimination performance of the proposed method is evaluated on a benchmark dataset of 208 OMPs, 673 globular proteins, and 206 alpha-helical membrane proteins. A high overall accuracy of 97.8% was observed in the 5-fold cross-validation test. In addition, the current method distinguished OMPs from globular proteins and alpha-helical membrane proteins with overall accuracies of 98.2 and 96.4%, respectively. The prediction performance is superior to the state-of-the-art methods in the literature. It is anticipated that the current method might be a powerful tool for the discrimination of OMPs.
Gao
Qing-Bin
QB
Department of Health Statistics, Second Military Medical University, No. 800 Xiangyin Road, Shanghai 200433, China. qb.gao@yahoo.com
Ye
Xiao-Fei
XF
Jin
Zhi-Chao
ZC
He
Jia
J
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis* / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008565:Membrane Proteins / Q000032:analysis / Q000737:chemistry*; D017433:Protein Structure, Secondary; D012984:Software
D000596:Amino Acids; D008565:Membrane Proteins