Predicting Protein Solubility with a Hybrid Approach by Pseudo Amino Acid Composition

Protein solubility plays a major role for understanding the crystal growth and crystallization process of protein. how to predict the propensity of a protein to be soluble or to form inclusion body is a long but not fairly resolved problem. after choosing almost 10,000 protein sequences from ncbi database and eliminating the sequences with 90% homologous similarity by cd-hit,5692 sequences remained. by using chou's pseudo amino acid composition features,we predict the soluble protein with the three methods: support vector machine (svm),back propagation neural network (bp neural network) and hybrid method based on svm and bp neural network,respectively. each method is evaluated by the re-substitution test and 10-fold cross-validation test. in the re-substitution test,the bp neural network performs with the best results,in which the accuracy achieves 92.88% and matthews correlation coefficient (mcc) achieves 0.8513. meanwhile,the other two methods are better than bp neural network in 10-fold cross-validation test. the hybrid method based on svm and bp neural network is the best. the average accuracy is 86.78% and average mcc is 0.7233. although all of the three methods achieve considerable evaluations,the hybrid method is deemed to be the best,according to the performance comparison. © 2010 bentham science publishers ltd.