Using pseudo-amino acid composition and support vector machine to predict protein structural class.
As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.
Chen
Chao
C
School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
Tian
Yuan-Xin
YX
Zou
Xiao-Yong
XY
Cai
Pei-Xiang
PX
Mo
Jin-Yuan
JY
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000235:genetics*; D019295:Computational Biology; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000145:classification*; D040901:Proteomics
D000596:Amino Acids; D011506:Proteins