Secondary Structure of Proteins and Three-dimensional Pattern Recognition

Abstract We describe a new, parameter-free, method to predict the secondary structure of a protein. It is based on the recognition of well-defined pentapeptides, which allows the discrimination of α helices, β sheets and random coils in proteins. Presently, a success rate of about 65% is achieved for a three-state model.