PREDICTION OF PROTEIN STRUCTURAL CLASSES

A protein is usually classified into one of the following five structu ral classes: alpha, beta, alpha + beta, alpha/beta, and zeta (irregula r). The structural class of a protein is correlated with its amino aci d composition. However, given the amino acid composition of a protein, how may one predict its structural class? Various efforts have been m ade in addressing this problem. This review addresses the progress in this field, with the focus on the: state of the art, which is featured by a novel prediction algorithm and a recently developed database. Th e novel algorithm is characterized by a covariance matrix that takes i nto account the coupling effect among different amino acid components of a protein. The new database was established based on the requiremen t that the classes should have (1) as many nonhomologous structures as possible, (2) good quality structure, and (3) typical or distinguisha ble features for each of the structural classes concerned. The very hi gh success rate for both the training-set proteins and the testing-set proteins, which has been further validated by a simulated analysis an d a jackknife analysis, indicates that it is possible to predict the s tructural class of a protein according to its amino acid composition i f an ideal and complete database can be established. It also suggests that the overall fold of a protein is basically determined by its amin o acid composition.