Abstract
Knowledge of protein secondary structure is a useful step toward prediction of the 3D structure of a particular protein. In this paper, a support vector machine (SVM) based method used for the prediction of secondary structure is introduced in details. Protein sequence data is in a hybrid representation combining the Position-specific Scoring Matrix (PSSM), the Hydrophobicity Sequence Feature (HSF), and the Structural Sequence Feature (SSF). Protein sequences are obtained from CB513 dataset, corresponding PSSM profiles are obtained from PSI-BLAST Program and sequence features are computed based on amino acid scales offered by Expasy website (http://web.expasy.org/protscale/). Basically, PSSM profiles are used as input data to the SVM-PSSM classifier of the secondary structure prediction. Furthermore, to construct more accurate classifiers, more than 40 SFs (sequence features) are examined as accessional input vector to SVM-PSSM classifier for feature selection. The most accurate classifier in this study is constructed using a combination of PSSM and few relevant sequence features. The experimental results show that relevant sequence features extracted from Hydrophobicity index and Structural conformational parameters can improve the SVM-PSSM classifier for the prediction of protein secondary structure elements. Our proposed final SVM-PSSM-SF method achieved an overall accuracy of 78%.
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The research work is supported by the National Natural Science Foundation of China (Grant No. 61375013); and the Natural Science Foundation of Shandong province (Grant No. ZR2013FM020) China.
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Chen, Y., Cheng, J., Liu, Y., Park, P.S. (2018). A Novel Approach of Protein Secondary Structure Prediction by SVM Using PSSM Combined by Sequence Features. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_74
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DOI: https://doi.org/10.1007/978-3-319-56994-9_74
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