Skip to main content
SpringerLink
Log in

A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases

  • Original research article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

One important challenge in the post-genomic era is to explore disease mechanisms by efficiently integrating different types of biological data. In fact, a single disease is usually caused through multiple genes products such as protein complexes rather than single gene. Therefore, it is meaningful for us to discover protein communities from the protein–protein interaction network and use them for inferring disease–disease associations. In this article, we propose a new framework including protein–protein networks, disease–gene associations and disease–complex pairs to cluster protein complexes and infer disease associations. Complexes discovered by our approach is superior in quality (Sn, PPV and ACC) and clustering quantity than other four popular methods on three PPI networks. A systematic analysis shows that disease pairs sharing more protein complexes (such as Glucose and Lipid Metabolic Disorders) are more similar and overlapping proteins may have different roles in different diseases. These findings can provide clinical scholars and medical practitioners with new ideas on disease identification and treatment.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Barabasi AT, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68. https://doi.org/10.1038/nrg2918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhuang Q, An X, Liu H et al (2019) Uncovering the resistance mechanism of mycobacterium tuberculosis to rifampicin due to RNA polymerase H451D/Y/R mutations from computational perspective. Front Chem 7:819. https://doi.org/10.3389/fchem.2019.00819

    Article  CAS  Google Scholar 

  3. Li ZC, Huang QX, Chen XY et al (2020) Identification of drug-disease associations using information of molecular structures and clinical symptoms via deep convolutional neural network. Front Chem 7:924. https://doi.org/10.3389/fchem.2019.00924

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hu L, Chan KCC (2015) A density-based clustering approach for identifying overlapping protein complexes with functional preferences. BMC Bioinf 16:174. https://doi.org/10.1186/s12859-015-0583-3

    Article  CAS  Google Scholar 

  5. Tang XQ, Zhu P (2013) Hierarchical clustering problems and analysis of fuzzy proximity relation on granular space. IEEE Trans Fuzzy Syst 21:814–824. https://doi.org/10.1109/TFUZZ.2012.2230176

    Article  Google Scholar 

  6. Maddi AMA, Moughari FA, Balouchi M et al (2019) CDAP: an online package for evaluation of complex detection methods. Sci Rep 9:12751. https://doi.org/10.1038/s41598-019-49225-7

    Article  CAS  Google Scholar 

  7. Doorbar J, Egawa N, Griffin H et al (2015) Human papillomavirus molecular biology and disease association. Rev Med Virol 25:2–23. https://doi.org/10.1002/rmv.1822

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shen XJ, Yi L, Jiang XP et al (2016) Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network. Methods 110:90–96. https://doi.org/10.1016/j.ymeth.2016.06.010

    Article  CAS  PubMed  Google Scholar 

  9. Ren J, Wang JX, Li M, Wu FX (2015) Discovering essential proteins based on PPI network and protein complex. Int J Data Min Bioinf 12:24–43. https://doi.org/10.1504/IJDMB.2015.068951

    Article  Google Scholar 

  10. Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466:761–764. https://doi.org/10.1038/nature09182

    Article  CAS  PubMed  Google Scholar 

  11. Wei G, Tao Z, Tao H, Cai YD (2020) Disease cluster detection and functional characterization. IEEE Access 99:1–1. https://doi.org/10.1109/ACCESS.2020.3013666

    Article  Google Scholar 

  12. Bouguettaya A, Yu Q, Liu XM et al (2015) Efficient agglomerative hierarchical clustering. Exp Syst Appl 42:2785–2797. https://doi.org/10.1016/j.eswa.2014.09.054

    Article  Google Scholar 

  13. Palla G, Derenyi I, Farkas I et al (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:814–818. https://doi.org/10.1038/nature03607

    Article  CAS  PubMed  Google Scholar 

  14. Liu G, Wong L, Chua HN (2009) Complex discovery from weighted PPI networks. Bioinformatics 25:1891–1897. https://doi.org/10.1093/bioinformatics/btp311

    Article  CAS  PubMed  Google Scholar 

  15. Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11:033015. https://doi.org/10.1088/1367-2630/11/3/033015

    Article  Google Scholar 

  16. Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 9:471-U81. https://doi.org/10.1038/nmeth.1938

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Maddi AMA, Eslahchi C (2017) Discovering overlapped protein complexes from weighted PPI networks by removing inter-module hubs. Sci Rep 7:3247. https://doi.org/10.1038/s41598-017-03268-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ding Z, Zhang X, Sun D et al (2016) Overlapping community detection based on network decomposition. Sci Rep 6:24115. https://doi.org/10.1038/srep24115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Karrer B, Newman MEJ (2010) Stochastic blockmodels and community structure in networks. Phys Rev E 83:016107. https://doi.org/10.1103/PhysRevE.83.016107

    Article  CAS  Google Scholar 

  20. Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12:103018. https://doi.org/10.1088/1367-2630/12/10/103018

    Article  Google Scholar 

  21. Wen X, Chen WN, Lin Y et al (2017) A maximal clique based multiobjective evolutionary algorithm for overlapping community detection. IEEE Trans Evolut Comput 21:363–377. https://doi.org/10.1109/TEVC.2016.2605501

    Article  Google Scholar 

  22. Batool Z, Usman M, Saleem K et al (2018) Disease-disease association using network modeling: challenges and opportunities. J Med Image Health Inf 8:627–638. https://doi.org/10.1166/jmihi.2018.2342

    Article  Google Scholar 

  23. Qi JM, Zhou JX, Tang XQ, Wang YL (2020) Gene biomarkers derived from clinical data of hepatocellular carcinoma. Interdiscip Sci Comput Life Sci 12:226–236. https://doi.org/10.1007/s12539-020-00366-8

    Article  CAS  Google Scholar 

  24. Higalgo CA, Blumm N, Barabasi AL et al (2009) A dynamic network approach for the study of human phenotypes. PLoS Comput Biol 5:e1000353. https://doi.org/10.1371/journal.pcbi.1000353

    Article  CAS  Google Scholar 

  25. Gamba A, Salmona M, Bazzoni G (2020) Quantitative analysis of proteins which are members of the same protein complex but cause locus heterogeneity in disease. Sci Rep 10:10423. https://doi.org/10.1038/s41598-020-66836-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ni P, Wang JX, Zhong P et al (2018) Constructing disease similarity networks based on disease module theory. IEEE-ACM Trans Comput Biol Bioinf 17:906–915. https://doi.org/10.1109/TCBB.2018.2817624

    Article  Google Scholar 

  27. Choobdar S, Ahsen ME, Crawford J et al (2019) Assessment of network module identification across complex diseases. Nat Methods 16:843–852. https://doi.org/10.1038/s41592-019-0509-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Goh KI, Cusick ME, Valle D et al (2007) The human disease network. Proc Natl Acad Sci USA 104:8685–8690. https://doi.org/10.1073/pnas.0701361104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang QH, Liu WS, Ning SW et al (2012) Community of protein complexes impacts disease association. Eur J Genet 20:1162–1167. https://doi.org/10.1038/ejhg.2012.74

    Article  CAS  Google Scholar 

  30. Szklarczyk D, Franceschini A, Kuhn M et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucl Acids Res 39:D561–D568. https://doi.org/10.1093/nar/gkq973

    Article  CAS  PubMed  Google Scholar 

  31. Giurgiu M, Reinhard J, Brauner B et al (2019) CORUM: the comprehensive resource of mammalian protein complexes 2019. Nucl Acids Res 47:D559–D563. https://doi.org/10.1093/nar/gky973

    Article  CAS  PubMed  Google Scholar 

  32. Pinero J, Bravo A, Queralt-Rosinach N et al (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucl Acids Res 45:D833–D839. https://doi.org/10.1093/nar/gkw943

    Article  CAS  PubMed  Google Scholar 

  33. Radicchi F, Castellano C et al (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101:2658–2663. https://doi.org/10.1073/pnas.0400054101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. ACM. https://doi.org/10.1145/1081870.1081893

    Article  Google Scholar 

  35. Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf 4:2. https://doi.org/10.1186/1471-2105-4-2

    Article  Google Scholar 

  36. Brohee S, van-Helden J, (2006) Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinf 7:488. https://doi.org/10.1186/1471-2105-7-488

    Article  CAS  Google Scholar 

  37. Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol 11:37–50. https://doi.org/10.1111/j.1469-8137.1912.tb05611.x

    Article  Google Scholar 

  38. Chen LG, Chen XW, Huang X et al (2019) Regulation of glucose and lipid metabolism in health and disease. Sci Chin Life Sci 62:1420–1458. https://doi.org/10.1007/s11427-019-1563-3

    Article  Google Scholar 

  39. Recalde D, Cenarro A, Garcia-Otin AL et al (2002) Analysis of apolipoprotein A-I, lecithin: cholesterol acyltransferase and glucocerebrosidase genes in hypoalphalipoproteinemia. Atherosclerosis 163:49–58. https://doi.org/10.1016/S0021-9150(01)00753-5

    Article  CAS  PubMed  Google Scholar 

  40. Yates JRW, Sepp T, Matharu BK et al (2007) Complement C3 variant and the risk of age-related macular degeneration. New Engl J Med 357:553–561. https://doi.org/10.1056/NEJMoa072618

    Article  CAS  PubMed  Google Scholar 

  41. Zhao Q, Zhang Y, Hu H et al (2018) (2018) IRWNRLPI: integrating random walk and neighborhood regularized logistic matrix factorization for lncRNA-protein interaction prediction. Front Genet 9:239. https://doi.org/10.3389/fgene.2018.00239.eCollection

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hu H, Zhang L, Ai HX et al (2018) HLPI-ensemble: prediction of human lncRNA-protein interactions based on ensemble strategy. RNA Biol 15:797–806. https://doi.org/10.1080/15476286.2018.1457935

    Article  PubMed  PubMed Central  Google Scholar 

  43. Zhao Q, Yu HF, Ming Z et al (2018) The bipartite network projection-recommended algorithm for predicting long non-coding RNA-protein interactions. Mol Therap Nucl Acids 13:464–471. https://doi.org/10.1016/j.omtn.2018.09.020

    Article  CAS  Google Scholar 

Download references

Funding

This work has been supported by the National Natural Science Foundation of China (Grand No. 11371174).

Author information

Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, 214122, China

    Lei Xue & Xu-Qing Tang

Authors
  1. Lei Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu-Qing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XQT and LX designed the study. LX implemented the analysis. LX and XQT wrote the manuscript. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Xu-Qing Tang.

Ethics declarations

Conflict of interest

All the authors declare no conflicts of interest in this paper.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 7581 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, L., Tang, XQ. A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases. Interdiscip Sci Comput Life Sci 13, 683–692 (2021). https://doi.org/10.1007/s12539-021-00432-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-021-00432-9

Keywords

Search

Navigation