Protein secondary structure prediction based on the GOR algorithm incorporating multiple sequence alignment information

Abstract

We have developed a new method for the prediction of the protein secondary structure from the amino acid sequence. The method is based on the most recent version (IV) of the standard GOR (J Mol Biol 120 (1978) 97) algorithm. A significant improvement is obtained by combining multiple sequence alignments with the GOR method. Additional improvement in the predictions is obtained by a simple correction of the results when helices or sheets are too short, or if helices and sheets are direct neighbors along the sequence (we require at least one residue of coil state between them). The imposition of the requirement that the prediction must be strong enough, i.e. that the difference between the probability of the predicted (most probable) state and the probability of the second most probable state must be larger than a certain minimum value also improves significantly secondary structure predictions. We have tested our method on 12 different proteins from the Protein Data Bank with known secondary structures. The average quality of the GOR prediction of the secondary structure for these 12 proteins without multiple sequence alignment was 63.4%. The multiple sequence alignments improve the average prediction to 71.9%. The correction for short helices and sheets and coil states separating sheets and helices improve further the average prediction to 74.4%. Setting the 10% minimum difference between the most probable and the second probable conformation leads to 77.0% accuracy of the prediction, while increasing this limit to 20% increases the average accuracy of the secondary structure prediction to 81.2%.