Skip to main content
Log in

Prediction of protein essentiality by the improved particle swarm optimization

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The essential protein is very important for understanding cellular critical activities and development. With the development of high throughput technology, how to identify the essential proteins from the protein interaction network has become a hot research topic in proteomics. A series of prediction methods have been proposed to infer the possibility of proteins to be essential by using the network topology. Therefore, it is necessary to develop an efficient method to detect the essential proteins considering both network topology and the biological attribute information. In this work, an effective method for essential proteins identification based on improved particle swarm optimization, named as EPPSO, is proposed. The method first constructs a weighted network by integrating the network topology characteristics and multi-source biological attribute information. To implement the PSO for essential protein identifying, we define the updating rules of the velocity vector and the positions of the particles. To estimate the essentiality of the nodes, we propose an index to measure the overall essentiality of the top-p essential proteins. The experimental results on yeast PPI data show that our algorithm is superior to other similar algorithms in terms of speed, accuracy and the number of essential proteins detected.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richard-son JE, Ringwald M, Rubin GM, Sherlock G, Consortium GO (2000) Nat Genet 25:25–29

    Article  Google Scholar 

  • Blum C, Li X (2008) Swarm intelligence in optimization. Springer, Berlin, Heidelberg

    Book  Google Scholar 

  • Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5):1170–1182

    Article  Google Scholar 

  • Cai Q, Gong M, Shen B, Ma L, Jiao L (2014) Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Netw 58:4–13

    Article  Google Scholar 

  • Cherry JM, Adler C, Ball C et al (1998) SGD: Saccharomyces genome database. Nucleic Acids Res 26(1):73–79

    Article  Google Scholar 

  • Cullen LM, Arndt GM (2005) Genome-wide screening for gene function using RNAi in mammalian cells. Immunol Cell Biol 83(3):217–223

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, vol 1, Nagoya, Japan, pp 39–43

  • Estrada E, Rodriguez-Velazquez JA (2005) Subgraph centrality in complex networks. Phys Rev E 71(5):056103

    Article  MathSciNet  Google Scholar 

  • Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41

    Article  Google Scholar 

  • Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evol Comput 18(1):82–97

    Article  Google Scholar 

  • Jeong H, Mason SP, Barabásiet AL et al (2001) Lethality and centrality in protein networks. Nature 411(6833):41–42

    Article  Google Scholar 

  • Jiang Y, Wang Y, Pang W, Chen L, Sun H, Liang Y, Blanzieri E (2015a) Essential protein identification based on essential protein–protein interaction prediction by integrated edge weights. Methods 83:51–62

    Article  Google Scholar 

  • Jiang Y, Wang Y, Pang W et al (2015b) Essential protein identification based on essential protein–protein interaction prediction by integrated edge weights. Methods 83:51–62

    Article  Google Scholar 

  • Kennedy J (2010) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, US, pp 760–766

  • Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE international conference on computational cybernetics and simulation, Piscataway, NJ, pp 4104–4108

  • Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput 16(2):210–224

    Article  MathSciNet  Google Scholar 

  • Li M, Wang J, Wang H et al (2010) Essential Proteins Discovery from Weighted Protein Interaction Networks, Int J Bioinformatics Res Appl 89–100

  • Li M, Wang J, Chen X et al (2011) A local average connectivity-based method for identifying essential proteins from the network level. Comput Biol Chem 35(3):143–150

    Article  MathSciNet  Google Scholar 

  • Li M, Zhang H, Wang J et al (2012) A new essential protein discovery method based on the integration of protein–protein interaction and gene expression data. BMC Syst Biol 6(1):15

    Article  Google Scholar 

  • Li M, Zheng R, Zhang H et al (2014) Effective identification of essential proteins based on priori knowledge. Netw Topol Gene Expr Methods 67(3):325–333

    Google Scholar 

  • Luo J, Ma L (2013) A new integration-centric algorithm of identifying essential proteins based on topology structure of protein–protein interaction network and complex information. Curr Bioinform 8(3):380–385

    Article  Google Scholar 

  • Luo J, Qi Y (2015) Identification of essential proteins based on a new combination of local interaction density and protein complexes. PLoS ONE 10(6):e0131418

    Article  Google Scholar 

  • Ma T, Wang Y, Tang M, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2016) LED: a fast overlapping communities detection algorithm based on structural clustering. Neurocomputing 207:488–500

    Article  Google Scholar 

  • Mewes HW, Amid C, Arnold R et al (2004) MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 32(Suppl 1):D41–D44

    Article  Google Scholar 

  • Min Li YL, Wang J, Fang-Xiang W, Pan Y (2015) A topology potential-based method for identifying essential proteins from PPI networks. IEEE/ACM Trans Comput Biol Bioinf 12(2):372–383

    Article  Google Scholar 

  • O’Brien KP, Remm M, Sonnhammer EL (2005) Nucleic Acids Res 33:D476–480

    Article  Google Scholar 

  • Peng W, Wang J, Yingjiao Cheng YL, Fangxiang W, Pan Y (2015) UDoNC: an algorithm for identifying essential proteins based on protein domains and protein–protein interaction networks. IEEE/ACM Trans Comput Biol Bioinf 12(2):276–288

    Article  Google Scholar 

  • Qi Y, Luo J (2016) Prediction of essential proteins based on local interaction density. IEEE/ACM Trans Comput Biol Bioinf 13(6):1170–1182

    Article  Google Scholar 

  • Roemer T, Jiang B, Davison J et al (2003) Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery. Mol Microbiol 50(1):167–181

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE international conference on evolutionary computation. IEEE World Congress on Computational Intelligence, IEEE, Anchorage, AK, USA, pp 69–73

  • Stephenson K, Zelen M (1989) Rethinking centrality: methods and examples. Soc Netw 11(1):1–37

    Article  MathSciNet  Google Scholar 

  • Sung Y, Chul S (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FRAT analysis. J Converg 6(2):1–9

    Google Scholar 

  • Tu BP, Kudlicki A, Rowicka M, McKnight SL (2005) Science 310:1152–1158

    Article  Google Scholar 

  • Wang J, Li M, Wang H et al (2012) Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans Comput Biol Bioinform 9(4):1070–1080

    Article  Google Scholar 

  • Wuchty S, Stadler PF (2003) Centers of complex networks. J Theor Biol 223(1):45–53

    Article  MathSciNet  Google Scholar 

  • Xue Y, Jiang J, Zhao B, Ma T (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput. https://doi.org/10.1007/s00500-017-2547-1

    Article  Google Scholar 

  • Yellaboina S, Tasneem A, Zaykin DV, Raghavachari B, Jothi R (2011) Nucleic Acids Res 39:D730–735

    Article  Google Scholar 

  • Yu N, Yu Z, Li B et al (2016) A comprehensive review of emerging computational methods for gene identification. J Inf Process Syst 12:1–34

    Google Scholar 

  • Zafiroula G (2015) The other side of opioid receptor signaling: regulation by protein–protein interaction. Hum-Centric Comput Inf Sci 4(S1):L21

    Google Scholar 

  • Zhang R, Lin Y (2009) DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res 37(Suppl. 1):D455–D458

    Article  Google Scholar 

  • Zhang X, Xu J, Xiao W (2013) A new method for the discovery of essential proteins. PLoS ONE 8(3):e58763

    Article  Google Scholar 

  • Zhang W, Jia X, Li X, Zou X (2016) A new method for identifying essential proteins by measuring co-expression and functional similarity. IEEE Trans Nanobiosci 15(8):939–945

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by the Chinese National Natural Science Foundation under Grant Nos. 61702441, 61772454, 61379066, 61602202, 61379064, 61472344, 61402395, Natural Science Foundation of Jiangsu Province under contracts BK20160428, BK20140492.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by G. Yi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, W., Wang, J., Chen, L. et al. Prediction of protein essentiality by the improved particle swarm optimization. Soft Comput 22, 6657–6669 (2018). https://doi.org/10.1007/s00500-017-2964-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2964-1

Keywords

Navigation