Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach.
The subnuclear localization of nuclear protein is very important for in-depth understanding of the construction and function of the nucleus. Based on the amino acid and pseudo amino acid composition (PseAA) as originally introduced by K. C. Chou can incorporate much more information of a protein sequence than the classical amino acid composition so as to significantly enhance the power of using a discrete model to predict various attributes of a protein, an algorithm of increment of diversity combined with the improved quadratic discriminant analysis is proposed to predict the protein subnuclear location. The overall predictive success rates and correlation coefficient are 75.4% and 0.629 for 504 single localization proteins in jackknife test, and 80.4% for an independent set of 92 multi-localization proteins, respectively. For 406 single localization nuclear proteins with < or =25% sequence identity, the results of jackknife test show that the overall accuracy of prediction is 77.1%.
Li
F-M
FM
Laboratory of Theoretical Biophysics, Department of Physics, College of Sciences and Technology, Inner Mongolia University, Hohhot, China.
Li
Q-Z
QZ
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D019295:Computational Biology; D008954:Models, Biological; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
D000596:Amino Acids; D011506:Proteins