We previously proposed a hybrid method for protein secondary structure prediction, called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. HYPROSP uses a global quantitative measure, match rate to determine which of PROSP and PSIPRED to be used for prediction of a target protein. HYPROSP made slight improvement of Q3 over PSIPRED because PROSP predicted well for proteins with match rate above 80%. As the portion of proteins with match rate above 80% is quite small and the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. A new hybrid strategy is essential to improve the hybrid prediction method.
We thus introduce a new quantitative measure called local match rate in contrast to match rate used in HYPROSP and a new hybrid approach HYPROSP II.
Local match rate indicates the amount of structural information that each amino acid can extract from the knowledge base. With the local match rate, we are able to define a confidence level of the PROSP prediction results for each amino acid. Our new hybrid approach, HYPROSP II, is proposed as follows: for each amino acid in a target protein, we combine the prediction results of PROSP and PSIPRED using a hybrid function defined on their respective confidence levels.
Two datasets in nrDSSP and EVA are used to perform a tenfold cross validation. The average Q3 of HYPROSP II is 81.8% and 80.7% on nrDSSP and EVA datasets, respectively, which is 2.0% and 1.1% better than that of PSIPRED. Using local match rate improves the accuracy better than global match rate. There has been a long history of attempts to improve secondary structure prediction. We believe HYPROSP II has greatly utilized the power of peptide knowledge base and raised the prediction accuracy to a new high. The method we developed could have a profound effect on the general use of knowledge base techniques for various prediction algorithms.