Using Cellular Automata Images to Predict Protein Structural Classes

How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [K. C Chou, Proteins: Struct. Funct. Genet. 43(2001)246-255] and protein cellular automata image, a novel approach for predicting the protein structural classes was introduced. The advantage by incorporating the cellular automata image factor into the pseudo amino acid composition as its components is that many important features, which are originally hidden in a long and complicated amino acid sequence, can be clearly revealed thru its cellular automata images. It was demonstrated thru the jackknife cross-validation test that the overall success rate by the new approach was significantly higher than those by the others. The remarkable merit of this approach is that many image recognition tools can be straightforwardly utilized in predicting protein structural classes.