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HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure

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Abstract

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid’s physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q 3 and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q 3 and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional methods, including the popular PSIPRED method over overall prediction accuracy. Available: http://www.kdd.ustb.edu.cn/protein_Web.

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Acknowledgments

We are grateful for the support of the National Nature Science Foundation (60675030, 60875029), the Beijing Key Discipline Development Program (XK100080537) and the Beijing Key Discipline Development Program-Computer Architecture.

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Authors and Affiliations

  1. School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Wu Qu, Bingru Yang, Wei Jiang & Lijun Wang

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  1. Wu Qu
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  2. Bingru Yang
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Correspondence to Wu Qu.

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Qu, W., Yang, B., Jiang, W. et al. HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure. Neural Comput & Applic 21, 337–349 (2012). https://doi.org/10.1007/s00521-011-0739-7

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  • DOI: https://doi.org/10.1007/s00521-011-0739-7

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