Better prediction of the location of α‐turns in proteins with support vector machine
Authors:
- 7 August 2006
- journal article
- research article
- Published by Wiley in Proteins: Structure, Function, and Bioinformatics
- Vol. 65 (1) , 49-54
Abstract
We have developed a novel method named AlphaTurn to predict α‐turns in proteins based on the support vector machine (SVM). The prediction was done on a data set of 469 nonhomologous proteins containing 967 α‐turns. A great improvement in prediction performance was achieved by using multiple sequence alignment generated by PSI‐BLAST as input instead of the single amino acid sequence. The introduction of secondary structure information predicted by PSIPRED also improved the prediction performance. Moreover, we handled the very uneven data set by combining the cost factor j with the “state‐shifting” rule. This further promoted the prediction quality of our method. The final SVM model yielded a Matthews correlation coefficient (MCC) of 0.25 by a 10‐fold cross‐validation. To our knowledge, this MCC value is the highest obtained so far for predicting α‐turns. An online Web server based on this method has been developed and can be freely accessed at http://bmc.hust.edu.cn/bioinformatics/ or http://210.42.106.80/. Proteins 2006.Keywords
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