Abstract
Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95 % on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location.
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Acknowledgments
This paper is supported by grants no. 61170125, 61202290 under the National Natural Science Foundation of China (http://www.nsfc.gov.cn) and grants no. BK20131154 under Natural Science Foundation of Jiangsu Province. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper. Kun Wang wrote the software, paper and implemented the experiments, Hongjie Wu designed the algorithm and experiments, Weizhong Lu prepared the datasets, Baochuan Fu and Qiang Lü cooperated with Hongjie to improve the workflow, Xu Huang organized the paper. The authors thank Jin Wang and Shimin Chen for helping with the analysis of the experiment.
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Wang, K., Wu, H., Lu, W., Fu, B., Lü, Q., Huang, X. (2015). Predicting Helix Boundaries of α-Helix Transmembrane Protein with Feedback Conditional Random Fields. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_73
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DOI: https://doi.org/10.1007/978-3-319-22180-9_73
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