ULPD: An Unsupervised Learning Model to Identify Party and Date Hubs

Document Type : Original Scientific Paper

Authors

1 Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 M.D. of Fazel Araaghi hospital, Tehran, Iran

Abstract

It has been claimed that Protein-Protein Interaction (PPI) networks are scale free that contain a few hubs with ability to bind multiple proteins. Hubs are classified as party and date hubs. Party hubs generally bind different proteins in specific module simultaneously, while date hubs interact with multiple proteins in different modules at different times and locations. Generally, they have been divided into two classes based on the average Pear- son Correlation Coefficient (avPCC) of expression over all partners or their functions. In this study, we propose a two-step algorithm to classify party and date hubs based on their topological features of PPI network. In the first step, we calculate some topological features for each hub vertex in PPI network. In the second step, we apply an unsupervised learning model to calculate Laplacian score for each feature. The Laplace value for each hub vertex are considered based on Laplacian scores. Finally, the hub vertices are classified into two classes date hubs and party hubs with respect to Laplace values. We evaluate our method on reference hubs based on the avPCC on PPI network. We show that the combination of topological features based on ULPD can improve the performance of each topological feature. Finally, we investigate the performance of our method for human dataset and analyze two types of hubs as drug targets for Covid-19.

Keywords

Main Subjects

References

[1] S. Agarwal, C. M. Deane, M. Porter and N. S. Jones, Revisiting date and party Hubs: Novel approaches to role assignment in protein interaction networks, PLoS Comput Biol. 6 (2010) e1000817.
[2] V. K. MD. Aksam, V. M. Chandrasekaran and S. Pandurangan, Hub nodes in the network of human Mitogen-Activated protein Kinase (MAPK) pathways: Characteristics and potential as drug targets, Inform. Med. Unlock. 9 (2017) 173 - 180.
[3] G. Alanis-Lobato, M. A. Andrade-Navarro and M. H. Schaefer, HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks, Nucleic Acids Res. 45 (2017) D408-D414.
[4] D. Alonso-Lopez, F. J. Campos-Laborie, A. Miguel, A. errez, L. Lambourne, M. A. Calderwood, M. Vidal and J. D. L. Rivas, APID database: redefining protein-protein interaction experimental evidences and binary interactomes, Database 2019 (2019) 1 - 8.
[5] G. Apic, T. Ignjatovic, S. Boyer and R. B. Russell, Illuminating drug discovery with biological pathways, FEBS Lett. 579 (2005) 1872 - 1877.
[6] M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Adv. Neural Inf. Process. Syst. 14 (2001) 585-591.
[7] N. Bertin, N. Simonis, D. Dupuy, M. E. Cusick, J. J. Han, H. B. Fraser, F. P. Roth and M. Vidal, Confirmation of organized modularity in the yeast interactome, PLoS Biol. 5 (2007) e153.
[8] P. Bertolazzi, M. E. Bock and C. Guerra, On the functional and structural characterization of hubs in protein-protein interaction networks, Biotechnol. Adv. 31 (2013) 274 - 286.
[9] A. Chatr-Aryamontri, R. Oughtred, L. Boucher, J. Rust, C. Chang, N. k. Kolas, L. Donnell, S. Oster, C. Theesfeld, A. Sellam, C. Stark, B. J. Breitkreutz, K. Dolinski and M. Tyers, The BioGRID interaction database: 2017 update, Nucleic Acids Res. 45 (2016) D369-D379.
[10] V. J. Costela-Ruiz, R. Illescas-Montes, J. M. Puerta-Puerta, C. Ruiz and L. Melguizo-Rodríguez, SARS-CoV-2 infection: The role of cytokines in COVID- 19 disease, Cytokine and Growth Factor Rev. 54 (2020) 62–75.
[11] E. Cukuroglu, E. Ozkirimli and O. Keskin, Structural properties of hub proteins, 2010 5th International Symposium on Health Informatics and Bioinformatics, Ankara, Turkey, 2010.
[12] D. Ekman, S. Light, K. Bjorklund and A. Elofsson, What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae, Genome Biol. 7 (6) (2006) R45.
[13] J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski, J. Cottarel, S. Kasif, J. C. Collins and T. S. Gardner, Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles, PLoS Biol. 5 (2007) e8.
[14] P. S. Gajinder, G. Mythily and D. Debasis, Role of intrinsic disorder in transient interactions of hub proteins, Proteins: Stru. Func. Bioinf. 66 (4) (2006) 761 - 765.
[15] M. Habibi, G. Taheri and R. Aghdam, A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing, Scientific Reports 11 (2021) 9378.
[16] J. J. Han, N. Bertin, T. Hao, D. S. Goldberg, G. F. Berriz, L. V. Zhang, D. Dupuy, A. J. M. Walhout, M. E. Cusick, F. P. Roth and M. Vidal, Evidence for dynamically organized modularity in the yeast protein–protein interaction
network, Nature 430 (2004) 88 - 93.
[17] X. He and P. Niyogi, Locality preserving projections, Adv. Neural Inf. Process. Syst. 16 (1) (2003) 186 - 197.
[18] X. He, C. Deng and N. Partha, Laplacian score for feature selection, Adv. Neural Inf. Process. Syst. 18 (2005) 507–514.
[19] G. Hu, Z. Wu, V. Uversky and L. Kurgan, Functional analysis of human hub proteins and their interactors involved in the intrinsic disorder-enriched interactions, Int. J. Mol. Sci. 18 (12) (2017) 2761.
[20] A. B. M. M. K. Islam, M. A. A. K. Khan, R. Ahmed, M. S. Hossain, S. M. T. Kabir, M. S. Islam and A. Z. Siddiki, Transcriptome of nasopharyngeal samples from COVID-19 patients and a comparative analysis with other
SARS-CoV-2 infection models reveal disparate host responses against SARSCoV-2, J. Transl. Med. 19 (1) (2021) 32.
[21] D. Jishnu and Y. Haiyuan, HINT: High-quality protein interactomes and their applications in understanding human disease, BMC Syst. Biol. 6 (2012) 92.
[22] S. Kany, J. T. Vollrath and B. Relja, Cytokines in inflammatory disease, Int. J. Mol. Sci. 20 (2019) 6008.
[23] Z. Leng, R. Zhu, W. Hou, Y. Feng, Y. Yang, Q. Han and R. C. Zhao, Transplantation of ACE2-mesenchymal stem cells improves the outcome of patients with COVID-19 pneumonia, Aging. Dis. 11 (2020) 216.
[24] K. Luck, D. K. Kim, L. Lambourne, K. Spirohn, B. E. Begg, W. Bian, R. Brignall, T. Cafarelli, F. J. Campos-Laborie, B. Charloteaux, D. Choi, A. G. Coté, M. Daley, S. Deimling, A. Desbuleux, A. Dricot, M. Gebbia, M. F. Hardy, N. Kishore, J. J. Knapp, I. A. Kovács, I. Lemmens, M. W. Mee, J. C. Mellor, C. Pollis, C. Pons, A. D. Richardson, S. Schlabach, B. Teeking, A. Yadav, M. Babor, D. Balcha, O. Basha, C. Bowman-Colin, S. F. Chin, S. G. Choi, C. Colabella, G. Coppin, C. D’Amata, D. De Ridder, S. De Rouck, M. Duran-Frigola, H. Ennajdaoui, F. Goebels, L. Goehring, A. Gopal, G. Haddad, E. Hatchi, M. Helmy, Y. Jacob, Y. Kassa, S. Landini, R. Li, N. van Lieshout, A. MacWilliams, D. Markey, J. N. Paulson, S. Rangarajan, J. Rasla, A. Rayhan, T. Rolland, A. San-Miguel, Y. Shen, D. Sheykhkarimli, G. M. Sheynkman, E. Simonovsky, M. Taan, A.Tejeda, V. Tropepe, J. C. Twizere, Y. Wang, R. J. Weatheritt, J. Weile, Y. Xia, X. Yang, E. YegerLotem, Q. Zhong, P. Aloy, G. D. Bader, J. De Las Rivas, S. Gaudet, T. Hao, J. Rak, J. Tavernier, D. E. Hill, M. Vidal, F. P. Roth and M. A. Calderwood,  A reference map of the human binary protein interactome, Nature 580 (2020) 402 - 408.
[25] A. R. Martin, E. Williams, R. E. Foulger, S. Leigh, L. C. Daugherty, O. Niblock, I. U. S. Leong, K. R. Smith, O. Gerasimenko, E. Haraldsdottir, E. Thomas, R. H. Scott, E. Baple, A. Tucci, H. Brittain, A. de Burca, K. Ibañez, D. Kasperaviciute, D. Smedley, M. Caulfield, A. Rendon and E. M. McDonagh, PPanelApp crowdsources expert knowledge to establish consensus
diagnostic gene panels, Nat Genet. 51 (11) (2019) 1560–1565.
[26] F. Messina, F. and E. Giombini, C, Agrati, F. Vairo, T. A. Bartoli, S. A. Moghazi, M. Piacentini, F. Locatelli, G. Kobinger, M. Maeurer, A. Zumla, M. R. Capobianchi, F. N. Lauria and G. Ippolito, COVID-19: viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection, J. Transl. Med. 18 (2020) 233.
[27] M. Mirzarezaee, B. N. Araabi and M. Sadeghi, Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae, BMC Sys. Bio. 4 (2010) 172.
[28] K. Prasad, F. Khatoon, R. Rashid, N. Ali, A. F. AlAsmari, M. A. Ahmed, A. S. Alqahtani, M. S. Alqahtani and V. Kumar, Targeting hub genes and pathways of innate immune response in COVID-19: A network biology perspective, Int. J. Biol. Macromol. 163 (2020) 1 - 8.
[29] M. J. Ramaiah, mTOR inhibition and p53 activation, microRNAs: The possible therapy against pandemic COVID-19, Gene Rep. 20 (2020) 100765.
[30] C. J. Sigrist, A. Bridge and P. Le Mercier, A potential role for integrins in host cell entry by SARS-CoV-2, Antiviral Res. 177 (2020) 104759.
[31] C. UniProt Consortium, UniProt: a worldwide hub of protein knowledge, Nucleic Acids Res. 47 (D1) (2019) D506-D515.
[32] S. Van Dongen, Graph Clustering by Flow Simulation, Ph. D. Thesis Center for Mathematics and Computer Science (CWI), University of Utrecht, 2000.
[33] D. S. Wishart, Y. D. Feunang, A. C. Guo, E. J. Lo, A. Marcu, J. R. Grant and M. Wilson, DrugBank 5.0: a major update to the DrugBank database for 2018, Nucleic Acids Res. 46 (D1) (2018) D1074-D1082.
[34] H. Yu, P. M. Kim, E. Sprecher, V. Trifonov and M. Gerstein, The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics, PLoS Comput. Biol. 3 (2007) e59.
 
Mathematics Interdisciplinary Research
Volume 7, Issue 2
June 2022
Pages 179-195

Files

Share

How to cite

Statistics