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A computational approach for nuclear export signals identification using spiking neural P systems

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Abstract

Nuclear export signal (NES) is a nuclear targeting signal within cargo proteins, which is involved in signal transduction and cell cycle regulation. NES is believed to be “born to be weak”; hence, it is a challenge in computational biology to identify it from high-throughput data of amino acid sequences. This work endeavors to tackle the challenge by proposing a computational approach to identifying NES using spiking neural P (SN P) systems. Specifically, secondary structure elements of 30 experimentally verified NES are randomly selected for training an SN P system, and then 1224 amino acid sequences (containing 1015 regular amino acid sequences and 209 experimentally verified NES) abstracted from 221 NES-containing protein sequences randomly in NESdb are selected to test our method. Experimental results show that our method achieves a precision rate 75.41 %, better than NES-REBS 47.2 %, Wregex 25.4 %, ELM, and NetNES 37.4 %. The results of this study are promising in terms of the fact that it is the first feasible attempt to use SN P systems in computational biology after many theoretical advancements.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61272152, 61370105, 61402187, 61502535, 61572522 and 61572523), China Postdoctoral Science Foundation funded project (2016M592267), Program for New Century Excellent Talents in University (NCET-13-1031), 863 Program (2015AA020925), and Fundamental Research Funds for the Central Universities (R1607005A).

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Authors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Zhihua Chen, Pan Zhang, Xiaolong Shi & Tingfang Wu

  2. College of Computer and Communication Engineering, China University of Petroleum (Eastern China), Qingdao, 266580, Shandong, China

    Xun Wang

  3. Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, 93350, Kuching, Malaysia

    Pan Zheng

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  1. Zhihua Chen
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Correspondence to Xun Wang.

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Chen, Z., Zhang, P., Wang, X. et al. A computational approach for nuclear export signals identification using spiking neural P systems. Neural Comput & Applic 29, 695–705 (2018). https://doi.org/10.1007/s00521-016-2489-z

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