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CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information

Interdisciplinary Sciences: Computational Life Sciences volume 13, pages 349–361 (2021)Cite this article

Abstract

Essential proteins are assumed to be an indispensable element in sustaining normal physiological function and crucial to drug design and disease diagnosis. The discovery of essential proteins is of great importance in revealing the molecular mechanisms and biological processes. Owing to the tedious biological experiment, many numerical methods have been developed to discover key proteins by mining the features of the high throughput data. Appropriate integration of differential biological information based on protein–protein interaction (PPI) network has been proven useful in predicting essential proteins. The main intention of this research is to provide a comprehensive study and a review on identifying essential proteins by integrating multi-source data and provide guidance for researchers. Detailed analysis and comparison of current essential protein prediction algorithms have been carried out and tested on benchmark PPI networks. In addition, based on the previous method TEGS (short for the network Topology, gene Expression, Gene ontology, and Subcellular localization), we improve the performance of predicting essential proteins by incorporating known protein complex information, the gene expression profile, Gene Ontology (GO) terms information, subcellular localization information, and protein’s orthology data into the PPI network, named CEGSO. The simulation results show that CEGSO achieves more accurate and robust results than other compared methods under different test datasets with various evaluation measurements.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation (No. 61802125, No. 61763010, No. 61862026 and No. 61862025), the Natural Science Foundation of Jiangxi Province (No. 20181BAB202006 and No. 20161BAB211022), the Guangxi “BAGUI Scholar” Program under Grant [2016] 127, and the Science and Technology Major Project of Guangxi under Grand AA18118047. The authors would like to thank Dr. John Kalivas from Chemistry Department of Idaho State University for providing simulation code of SRD.

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Affiliations

  1. School of Science, East China Jiaotong University, Nanchang, 330013, China

    Wei Zhang, Xiaoli Xue & Junhong Liu

  2. School of Computer and Information Engineering, Nanning Normal University, Nanning, 530299, China

    Chengwang Xie

  3. School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, 430072, China

    Yuanyuan Li

  4. School of Software, East China Jiaotong University, Nanchang, 330013, China

    Hailin Chen

  5. School of Information Engineering, East China Jiaotong University, Nanchang, 330013, China

    Guanghui Li

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  1. Wei Zhang
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  2. Xiaoli Xue
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  3. Chengwang Xie
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Correspondence to Wei Zhang or Chengwang Xie.

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Zhang, W., Xue, X., Xie, C. et al. CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information. Interdiscip Sci Comput Life Sci 13, 349–361 (2021). https://doi.org/10.1007/s12539-021-00426-7

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  • DOI: https://doi.org/10.1007/s12539-021-00426-7

Keywords

  • Essential proteins
  • Computational method
  • Data integration
  • High throughput data