Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network.
Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.
Chen
Chao
C
School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, PR China.
Zhou
Xibin
X
Tian
Yuanxin
Y
Zou
Xiaoyong
X
Cai
Peixiang
P
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D003205:Computing Methodologies; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
D000596:Amino Acids; D011506:Proteins