title: A correlation-coefficient method to predicting protein-structural classes from amino acid compositions.
abstract: A protein is usually classified into one of the following four structural classes: all alpha, all beta, (alpha + beta) and alpha/beta. In this paper, based on the maximum correlation-coefficient principle, a new formulation is proposed for predicting the structural class of a protein according to its amino acid composition. Calculations have been made for a development set of proteins from which the amino acid compositions for the standard structural classes were derived, and an independent set of proteins which are outside the development set. The former can test the self consistency of a method and the latter can test its extrapolating effectiveness. In both cases, the results showed that the new method gave a considerably higher rate of correct prediction than any of the previous methods, implying that a significant improvement has been achieved by implementing the maximum-correlation-coefficient principle in the new method.
authors.affiliation: Computational Chemistry, Upjohn Research Laboratories, Kalamazoo.
authors.forename: K C
authors.initials: KC
authors.lastname: Chou
authors.forename: C T
authors.initials: CT
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013329:Structure-Activity Relationship
chemical_list: D000596:Amino Acids; D011506:Proteins