Prediction of subcellular protein localization based on functional domain composition.
Assigning subcellular localization (SL) to proteins is one of the major tasks of functional proteomics. Despite the impressive technical advances of the past decades, it is still time-consuming and laborious to experimentally determine SL on a high throughput scale. Thus, computational predictions are the preferred method for large-scale assignment of protein SL, and if appropriate, followed up by experimental studies. In this report, using a machine learning approach, the Nearest Neighbor Algorithm (NNA), we developed a prediction system for protein SL in which we incorporated a protein functional domain profile. The overall accuracy achieved by this system is 93.96%. Furthermore, comparisons with other methods have been conducted to demonstrate the validity and efficiency of our prediction system. We also provide an implementation of our Subcellular Location Prediction System (SLPS), which is available at http://pcal.biosino.org.
Jia
Peilin
P
Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. pljia@sibs.ac.cn
Qian
Ziliang
Z
Zeng
Zhenbin
Z
Cai
Yudong
Y
Li
Yixue
Y
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D017434:Protein Structure, Tertiary; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D013347:Subcellular Fractions / Q000737:chemistry / Q000378:metabolism*
D020543:Proteome