Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution.
In the Post Genome Age, there is an urgent need to develop the reliable and effective computational methods to predict the subcellular localization for the explosion of newly found proteins. Here, a novel method of pseudo amino acid (PseAA) composition, the so-called "amino acid composition distribution" (AACD), is introduced. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented by a feature vector. Finally, the feature vectors of all sequences thus obtained are further input into the multi-class support vector machines to predict the subcellular localization. The results show that AACD is quite effective in representing protein sequences for the purpose of predicting protein subcellular localization.
Shi
J-Y
JY
School of Computer Science and Engneering, Northwestern Polytechnical University, Xi'an, China.
Zhang
S-W
SW
Pan
Q
Q
Zhou
G-P
GP
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030562:Databases, Protein; D042541:Intracellular Space / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides / Q000032:analysis; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry*
D000596:Amino Acids; D010455:Peptides; D011506:Proteins