A statistical analytical approach to predict the secondary structure of proteins from amino acid sequence information

A statistical analytical approach has been used to analyze the secondary structure (SS) of amino acids as a function of the sequence of amino acid residues. We have used 306 non-homologous best-resolved protein structures from the Protein Data Bank for the analysis. A sequence region of 32 amino acids on either side of the residue is considered in order to calculate single amino acid propensities, di-amino acid potentials and tri-amino acid potentials. A weighted sum of predictions obtained using these properties is used to suggest a final prediction method. Our method is as good as the best-known SS prediction methods, is the simplest of all the methods, and uses no homologous sequence/family alignment data, yet gives 72% SS prediction accuracy. Since the method did not use many other factors that may increase the prediction accuracy there is scope to achieve greater accuracy using this approach. Received: 4 May 1998 / Accepted: 17 September 1998 / Published online: 10 December 1998