A Knowledge-Based Hybrid Method for Protein Secondary Structure Prediction Based on Local Prediction Confidence – Abstract

Wen-Lian Hsu

We previously proposed a hybrid method for protein secondary structure prediction, called HYPROSP, which combined our knowledge-based prediction algorithm PROSP and another neural net-based algorithm, PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, match rate to determine which of PROSP and PSIPRED is to be used for the structure prediction of a target protein. HYPROSP made a 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% could be rather small and the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. A new hybrid strategy is introduced in this paper to improve the hybrid prediction method, which adopts a new quantitative measure called local match rate.

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 rather than the sequence as a whole. 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 1.0 better than that of PSIPRED.

For local structures with match rate higher than 80%, the average Q3 improvement is 4.4 on the nrDSSP dataset. 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 in this paper could have a profound effect on the general use of knowledge-based techniques for various prediction algorithms.