title: Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.
abstract: Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins.
authors.affiliation: Department of Biotechnology and Bioengineering, Huaqiao University, Xiamen 361021, Fujian, PR China. zhgyghh@hqu.edu.cn
authors.forename: Guangya
authors.initials: G
authors.lastname: Zhang
authors.forename: Huihua
authors.initials: H
authors.lastname: Ge
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D002623:Chemistry Techniques, Analytical / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D008954:Models, Biological; D010316:Particle Size; D011506:Proteins / Q000737:chemistry*; D012965:Sodium Chloride / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins; D012965:Sodium Chloride
keywords: ACC; Amino acid composition; FN; FP; Halophile; Hypersaline adaptation; MCC; Matthew’s correlation coefficient; PUFK; Pearson VII function kernel; Pearson VII universal function kernel; RBF neural network; ROC; SE; SP; SVMs; Support vector machine; TN; TP; Waikato environment for knowledge analysis; Weka; accuracy; false negatives; false positives; radial basis function neural network; receiver operating characteristic; sensitivity; specificity; support vector machine; true negatives; true positives