Abstract
Transmembrane proteins are important for cell transport biology and in the treatment of disease. Understanding the helix count and locations in transmembrane proteins is a key problem for structural and functional analyses. But there is a lack of high resolution three-dimensional structures. In this study, we propose a method based on conditional random fields for predicting the helix count and locations, CRF-TM, which reflects long-range correlations in the full-length sequence as joint probabilities. Two datasets are employed in the performance validation. Our results show that CRF-TM can rank the first group better compared with other widely used TM predictors. The results obtained by CRF-TM are also used to predict the three-dimensional structures of GPCRs, which is crucial drug targets and also a subclass of transmembrane with seven spanning α-helices.
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.
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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. The authors thank Jin Wang and Shimin Chen for helping with the analysis of the experiment.
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Lu, W., Fu, B., Wu, H., Lü, Q., Wang, K., Jiang, M. (2015). CRF-TM: A Conditional Random Field Method for Predicting Transmembrane Topology. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_52
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DOI: https://doi.org/10.1007/978-3-319-23862-3_52
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