Abstract
As protein types continue to increase, there are more and more methods for predicting protein structure. In this paper, a feature extraction method based on multiscale coarse-grained time series recurrence quantification analysis and horizontal visibility graph is proposed. First, the chaos game representation is used to map the protein secondary structure sequence into two time series. Multiscale coarse granulation time series. Then feature extraction by combining recurrence quantification analysis and horizontal visibility graph. Thereby a 30-D feature vector is obtained. This paper uses support vector machine to predict protein tertiary structure. In this paper, the prediction results of the two low homologous protein datasets were 95.33% and 93%, respectively.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61701192, 61640218), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2017QF004).
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Jiang, H., Zhang, A., Zhang, Z., Meng, Q., Li, Y. (2019). Protein Tertiary Structure Prediction Based on Multiscale Recurrence Quantification Analysis and Horizontal Visibility Graph. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_52
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