title: Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image.
abstract: A novel approach was developed for predicting the structural classes of proteins based on their sequences. It was assumed that proteins belonging to the same structural class must bear some sort of similar texture on the images generated by the cellular automaton evolving rule [Wolfram, S., 1984. Cellular automation as models of complexity. Nature 311, 419-424]. Based on this, two geometric invariant moment factors derived from the image functions were used as the pseudo amino acid components [Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct., Funct., Genet. (Erratum: ibid., 2001, vol. 44, 60) 43, 246-255] to formulate the protein samples for statistical prediction. The success rates thus obtained on a previously constructed benchmark dataset are quite promising, implying that the cellular automaton image can help to reveal some inherent and subtle features deeply hidden in a pile of long and complicated amino acid sequences.
authors.affiliation: Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 33300, China. xiaoxuan0326@yahoo.com.cn
authors.forename: Xuan
authors.initials: X
authors.lastname: Xiao
authors.forename: Pu
authors.initials: P
authors.lastname: Wang
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D030562:Databases, Protein; D007091:Image Processing, Computer-Assisted / Q000379:methods; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000145:classification*
chemical_list: D011506:Proteins