Protein sub-cellular localisation prediction by analysis of short-range residue correlations.
Sub-cellular localisation performs an important role in genome analysis. This paper describes a new residue-couple model using a support vector machine to predict the sub-cellular localisation of proteins. This new approach provides better predictions than the existing methods. The total prediction accuracies on Reinhardt and Hubbard's dataset reach 92.0% for prokaryotic protein sequences and 86.9% for eukaryotic protein sequences with fivefold cross validation. For a new dataset with 8304 proteins located in eight sub-cellular locations, the total accuracy achieves 88.9%. Meanwhile, the model shows robust against N-terminal errors in the sequences.
Guo
Jian
J
Laboratory of Statistical Computing and Bioinformatics, Department of Mathematical Sciences, Tsinghua University, Beijing, PR China. guojian99@tsinghua.org.cn
Lin
Yuanlie
Y
Sun
Zhirong
Z
D000465:Algorithms; D001185:Artificial Intelligence; D001695:Biology / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D016678:Genome; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012984:Software
D011506:Proteins