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Bioinformatics Advance Access published online on June 2, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti524
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 29, 2004
Revised March 10, 2005
Accepted May 29, 2005

Article

A knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence

Hsin-Nan Lin 1, Jia-Ming Chang 1, Kuen-Pin Wu 1, Ting-Yi Sung 1, and Wen-Lian Hsu 1*

1 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan

* To whom correspondence should be addressed.
Wen-Lian Hsu, E-mail: hsu{at}iis.sinica.edu.tw


  Abstract

Motivation: In our previous approach, we proposed a hybrid method for protein secondary structure prediction, called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and 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 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. To overcome this limitation and further improve the hybrid prediction method, we present in this paper a new hybrid strategy HYPROSP II which is based on a new quantitative measure called local match rate.

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

Availability: The Linux executable file of HYPROSP II, as well as both nrDSSP and EVA datasets can be downloaded at http://bioinformatics.iis.sinica.edu.tw/HYPROSPII/.


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