title: Predicting thermophilic proteins with pseudo amino acid composition:approached from chaos game representation and principal component analysis.
abstract: Comprehensive knowledge of thermophilic mechanisms about some organisms whose optimum growth temperature (OGT) ranges from 50 to 80 °C degree plays a major role for helping to design stable proteins. How to predict function-unknown proteins to be thermophilic is a long but not fairly resolved problem. Chaos game representation (CGR) can investigate hidden patterns in protein sequences, and also can visually reveal their previously unknown structures. In this paper, using the general form of pseudo amino acid composition to represent protein samples, we proposed a novel method for presenting protein sequence to a CGR picture using CGR algorithm. A 24-dimensional vector extracted from these CGR segments and the first two PCA features are used to classify thermophilic and mesophilic proteins by Support Vector Machine (SVM). Our method is evaluated by the jackknife test. For the 24-dimensional vector, the accuracy is 0.8792 and Matthews Correlation Coefficient (MCC) is 0.7587. The 26-dimensional vector by hybridizing with PCA components performs highly satisfaction, in which the accuracy achieves 0.9944 and MCC achieves 0.9888. The results show the effectiveness of the new hybrid method.
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan, PR of China.
authors.forename: Xiao-Lei
authors.initials: XL
authors.lastname: Liu
authors.forename: Jin-Long
authors.initials: JL
authors.lastname: Lu
authors.forename: Xue-Hai
authors.initials: XH
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D025341:Principal Component Analysis / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins