Open AccessA correlation‐coefficient method to predicting protein‐structural classes from amino acid compositions
Author(s) -
CHOU KuoChen,
ZHANG ChunTing
Publication year - 1992
Publication title -
european journal of biochemistry
Language(s) - English
Resource type - Journals
eISSN - 1432-1033
pISSN - 0014-2956
DOI - 10.1111/j.1432-1033.1992.tb17067.x
Subject(s) - correlation coefficient , consistency (knowledge bases) , mathematics , test set , amino acid , set (abstract data type) , beta (programming language) , alpha (finance) , correlation , chemistry , biological system , internal consistency , statistics , computer science , biochemistry , biology , discrete mathematics , geometry , programming language , psychometrics
A protein is usually classified into one of the following four structural classes: all α, all β, (α+β) and α/β. In this paper, based on the maximum correlation‐coefficient principle, a new formulation is proposed for pridicting 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 compo‐sitions 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.