title: Predicting protein solubility by the general form of Chou's pseudo amino acid composition: approached from chaos game representation and fractal dimension.
abstract: Obtaining soluble proteins in sufficient concentrations is a major obstacle in various experimental studies. How to predict the propensity of targets in large-scale proteomics projects to be soluble is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) can investigate the patterns hiding in protein sequences, and can visually reveal previously unknown structure. Fractal dimensions are good tools to measure sizes of complex, highly irregular geometric objects. In this paper, we convert each protein sequence into a high-dimensional vector by CGR algorithm and fractal dimension, and then predict protein solubility by these fractal features together with Chou's pseudo amino acid composition features and support vector machine (SVM). We extract and study six groups of features computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test. As the results of comparisons, the group of 445-dimensional vector gets the best results, the average accuracy is 0.8741 and average MCC is 0.7358. The resulting predictor is also compared with existing methods and shows significant improvement.
authors.affiliation: College of Science, Huazhong, Agricultural University, Wuhan, P.R. of China.
authors.forename: Xiao-Hui
authors.initials: XH
authors.lastname: Niu
authors.forename: Xue-Hai
authors.initials: XH
authors.lastname: Hu
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.forename: Jing-Bo
authors.initials: JB
authors.lastname: Xia
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D017709:Fractals; D008956:Models, Chemical; D017711:Nonlinear Dynamics; D011506:Proteins / Q000737:chemistry*; D012995:Solubility; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins