Abstract
Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. In practice, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Computational methods are not suitable to identify a large number of acetylated sites quickly. Therefore, machine learning methods are still very valuable to accelerate lysine acetylated site finding. In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A special structure neural network, which is named flexible neural tree (FNT), was then utilized to integrate such information for generating a novel lysine acetylation prediction system named LA+FNT. When compared with existing methods, our proposed method overwhelms most of state-of-the-art methods. Such method has the ability to integrating different biological features to predict lysine acetylation with high accuracy.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61520106006, 31571364, 61532008, 61572364, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61572447, and 61373098, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.
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Bao, W., Jiang, Z., Han, K., Huang, DS. (2016). Prediction of Lysine Acetylation Sites Based on Neural Network. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_77
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DOI: https://doi.org/10.1007/978-3-319-42294-7_77
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