DOI: https://doi.org/10.14710/jtsiskom.2021.13985

Identifikasi protein signifikan pada interaksi protein-protein penyakit Alzheimer menggunakan algoritme top-k representative skyline query

Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query

1Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University. Jl. Meranti Wing 20 Level 5, Kampus IPB Darmaga, Bogor 16680, Indonesia

2Tropical Biopharmaca Research Center, IPB University. Jl. Taman Kencana No. 3, Bogor 16128, Indonesia

Received: 19 Nov 2020; Revised: 20 Mar 2021; Accepted: 24 Apr 2021; Available online: 26 Apr 2021; Published: 31 Jul 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Results
Supplementary Data
Subject The collecting data on genes/proteins associated with Alzheimer's disease from the OMIM database, information on interactions between proteins from the STRING database, and the results of topological analysis of protein-protein interaction networks.
Type Research Results
  Download (781KB)    Indexing metadata
Email colleagues
Keywords: alzheimer; centrality measures; protein-protein interaction; skyline query; Top-k RSP
Funding: Ministry of Research, Technology, and Higher Education under contract 4168/IT3.I.1/PN/2019

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Application caching strategy based on in-memory using Redis server to accelerate relational data access Multitenant Computing Infrastructure Service with OpenStack in MaaS Environment K-nearest neighbor performance for Nusantara scripts image transliteration Prediction of the peak Covid-19 pandemic in Indonesia using SIR model Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor More related articles
Most cited articles
Mobile Application of Expert System for Meningitis Early Diagnosis People counter on CCTV video using histogram of oriented gradient and Kalman filter methods Identification of Rupiah Paper Currency Denomination using SURF and FLANN Methods at Rotation Variation Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor PSS Tuning on Power Generator System using Flower Pollination Algorithm More cited articles
  1. J. Weller and A. Budson, “Current understanding of Alzheimer’s disease diagnosis and treatment,” F1000Research, Vol 7, 1161, 2018. doi: 10.12688/f1000research.14506.1
  2. - , “Alzheimer disease and other dementia,” World Health Organization, 2013. [Online]. Available: https://www.who.int/medicines/areas/priority_medicines/BP6_11Alzheimer.pdf. [Accessed: Mar. 14, 2021]
  3. M. T. Hayes, “Parkinson’s disease and parkinsonism,” The American Journal of Medicine, vol. 132, pp. 802-807, 2019. doi: 10.1016/j.amjmed.2019.03.001
  4. G. Glaever et al., “Functional profiling of the Saccharomyces cerevisiae genome,” Nature, vol. 418, no. 6896, pp. 387-391, 2002. doi: 10.1038/nature00935
  5. M. L. Acencio and N. Lemke, “Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information,” BMC Bioinformatics, vol. 10, 290, 2009. doi: 10.1186/1471-2105-10-290
  6. C. Qin, Y. Sun, and Y. Dong, “A new method for identifying essential proteins based on network topology properties and protein complexes,” PLoS One, vol. 11, no. 8, e0161042, 2016. doi: 10.1371/journal.pone.0161042
  7. J. W. Chang, Y. Q. Zhou, M. T. Ul Qamar, L. L. Chen, and Y. D. Ding, “Prediction of protein-protein interactions by evidence combining methods,” International Journal of Molecular Sciences, vol. 17, no. 11, 1946, 2016. doi: 10.3390/ijms17111946
  8. W. Liu, A. Wu, M. Pellegrini, and X. Wang, “Integrative of human protein, function, and disease networks,” Scientific Report, vol. 5, 14344, 2015. doi: 10.1038/srep.14344
  9. J. D. L. Rivas and C. Fontanillo, “Protein-protein interactions essentials: key concepts to building and analyzing interactome networks,” PLoS Computational Biology, vol. 6, no. 6, e1000807, 2010. doi: 10.1371/journal.pcbi.1000807
  10. D. Szklarcyzk et al., “STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Research, vol 47, pp. 607-613, 2019. doi: 10.1093/nar/gky/1131
  11. D. Szklarczyk et al., “The STRING database in 2017: quality controlled protein-protein association networks, made broadly accessible,” Nucleic Acids Research, vol. 45, pp. D362-D368, 2017. doi: 10.1093/nar/gkw937
  12. J. Yu and F. Fotouhi, “Computational approaches for predicting protein-protein interactions: a survey,” Journal of Medical Systems, vol. 30, pp. 39-44, 2006. doi: 10.1007/s10916-006-7402-3
  13. W. Ali and C. M. Deane, “Evolutionary analysis reveals low coverage as the major challenge for protein interaction network alignment,” Molecular BioSystems, vol. 6, no. 11, pp. 2296-2304, 2010. doi: 10.1039/c004430j
  14. M. E. Cusick, N. Klitgord, M. Vidal, and D. E. Hill, “Interactome: gateway into systems biology,” Human Molecular Genetics, vol. 14, no. 2, pp. R171-R181, 2005. doi: 10.1093/hmg/ddi335
  15. M. P. H. Stumpf et al., “Estimating the size of human interactome,” PNAS, vol. 105, no. 19, pp. 6959-6964, 2008. doi: 10.1073/pnas.0708078105
  16. R. Jansen, N. Ian, J. Qian, and M. Gerstein, “Integration of genomic datasets to predict protein complexes in yeast,” Journal of Structural and Functional Genomics, vol. 2, pp. 71-81, 2002. doi: 10.1023/A:1020495201615
  17. L. J. Lu, Y. Xia, A. Paccanaro, H. Yu, and M. Gerstein, “Assessing the limits of genomic data integration for predicting protein networks,” Genome Research, vol. 15, pp. 945-953, 2005. doi: 10.1101/gr.3610305
  18. Y. Qi, Z. Bar-Joseph, and J. Klein-Seetharaman, “Evaluation of different biological and computational classification methods for use in protein interaction prediction,” Proteins, vol. 62, no. 3, pp. 490-500, 2006. doi: 10.1002/prot.20865
  19. G. Agapito, P. H. Guzzi, and M. Cannataro, “Visualization of protein interaction networks: problems and solutions,” BMC Bioinformatics, vol. 14, no. 1, S1, 2013. doi: 10.1186/1471-2105-14-S1-S1
  20. D. Mistry, R. P. Wise, and J. A. Dickerson, “DiffSLC: a graph centrality method to detect essential proteins of a protein-protein interaction network,” PloS ONE, vol. 12, no. 11, e0187091, 2017. doi: 10.1371/journal.pone.0.187091
  21. M. W. Hahn and A. D. Kern, “Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks,” Molecular Biology and Evolution, vol. 22, no. 4, pp. 803-806, 2005. doi: 10.1093/molbev/msi072
  22. A. Ozgur, T. Vu, G. Erkan, and D. R. Radev, “Identifying gene-disease associations using centrality on a literature mined gene-interaction network,” Bioinformatics, vol. 24, pp. i277-i285, 2008. doi: 10.1093/bioinformatics/btn182
  23. M. R. Diansyah, W. A. Kusuma, and A. Annisa, “Analysis of protein-protein interaction using skyline query on parkinson disease,” in International Conference on Advanced Computer Science and Information Systems, Bali, Indonesia, Oct. 2019, pp. 175-180. doi: 10.1109/ICACSIS47736.2019.8979892
  24. G. S. Lee and M. A. Djauhari, “An overall centrality measures: the case of U.S stock market,” International Journal of Basic & Applied Sciences, vol. 12, no. 6, pp. 99-103, 2012
  25. S. Borzsonyi, D. Kossman, and K. Stocker, “The skyline operator,” in 17th International Conference on Data Engineering, Heidelberg, Germany, Apr. 2001, pp. 421-430
  26. M. Kontaki, A. N. Papadopoulos, and Y. Manolopoulos, “Continous k-dominant skyline computation on multidimensional data streams,” in ACM Symposium on Applied Computing (SAC). Ceara, Brazil, Jun. 2008, pp. 956-960. doi: 10.1145/1363686.1363908
  27. X. Lin, Y. Yuan, Q. Zhang, and Y. Zhang, “Selecting stars: the k most representative skyline operator,” in 23rd International Conference on Data Engineering, Istanbul, Turkey, Apr. 2007, pp. 86-95. doi: 10.1109/ICDE.2007.367.854
  28. P. Sharma, D. K. Bhattacharyya, and J. K. Kalita, “Centrality analysis in PPI networks,” in 2016 International Conference on Accessibility to Digital World, Guwahati, India, Dec. 2016, pp. 135-140. doi: 10.1109/ICADW.2016.7942528
  29. G. Scardoni, F. Fabbri, C. Laudanna, and G. Tossadori, “CentiScaPe: Network centralities for Cytoscape,” Univ of Verona, 2009
  30. H. M. Lanoiselee et al., “APP, PSEN1, and PSEN2 mutations in early-onset Alzheimer disease: A genetic screening study of familial and sporadic cases,” PLoS Medicine, vol. 14,no. 3, e1002270, 2017. doi: 10.10371/journal.pmed.1002270
  31. V. V. Giau, J. M Pyun, J. Suh, E. Bagyinszky, S. S. A. An, and S. Y. Kim, “A pathogenic PSEN1 Trp165Cys mutation associated with ealy-onset Alzheimer’s disease,” BMC Neurology, vol. 19, 188, 2019. doi: 10.1186/s12883-019-1419-y
  32. Y. Zhang, R. Thompson, H. Zhang, and H. Xu, “APP processing in alzheimer’s disease,” Molecular Brain, vol. 4, 3, 2011. doi: 10.1186/1756-6606-4-3
  33. R. J. Brien and P. C. Wong, “Amyloid precursor protein processing and alzheimer’s disease,” Annual Review of Neuroscience, vol. 34, pp. 185-204, 2011. doi: 10.1146/annurev-neuro-061010-113613
  34. C. Haass, E. H. Koo, A. Mellon, A. Y. Hyung, and D. J. Selkoe, “Targeting of cell-surface β-amyloid precursor protein to lysosomes:alternative processing into amyloid-bearing fragments,” Nature, vol. 357, pp. 500-503, 1992. doi: 10.1038/357500a0
  35. A. Goate et al., “Segregation of a missense mutation in the amyloid precursor protein gene with familiat alzheimer’s disease,” Nature, vol. 349, no. 6311, pp. 704-706, 1992. doi: 10.1038/349704a0
  36. R. J. Kelleher and J. Shen, “Presenilin-1 mutations and Alzheimer’s disease,” PNAS, vol. 114, no. 4, pp. 629-631, 2017. doi: 10.1073/pnas.1619574114
  37. L. M. Bekris, C. E. Yu, T. D. Bird, and D. W. Tsuang, “Genetics of Alzheimer disease,” Journal of Geriatric Psychiatry and Neurology, vol. 23, no. 4, pp. 213-227, 2010. doi: 10.117/0891988710383571
  38. V. M. Giau, E. Bagyinszky, Y. C. Youn, S. S. A. An, and S. Kim, “APP, PSEN1, and PSEN2 mutations in Asian patients with early-onset Alzheimer disease,” International Journal of Molecular Sciences, vol. 20, no. 19, 4757, 2019. doi: 10.3390/ijms20194757
  39. E. Tamagno, M. Guglielmotto, D. Monteleone, G. Manassero, V. Vasciaveo, and M. Tabaton, “The unexpected role of Aβ1-42 monomers in the pathogenesis of alzheimer’s disease,” Journal of Alzheimer’s Disease, vol. 62, pp. 1241-1245, 2017. doi: 10.3233/JAD-170581
  40. C. L. Masters and K. Beyreuther, “Alzheimer’s centennial legacy: prospects forrational therapeutic intervention targeting the Aβ amyloid pathway,” Brain, vol. 129, no. 11, pp. 2823-2839, 2006. doi: 10.1093/brain/awl251

Last update:

  1. Construction and analysis of protein-protein interaction to identify the molecular mechanism in hypertension

    Lusi Agus Setiani, Fadlina Chany Saputri, Arry Yanuar, Abdul Mun’im. THE 2ND INTERNATIONAL CONFERENCE ON NATURAL SCIENCES, MATHEMATICS, APPLICATIONS, RESEARCH, AND TECHNOLOGY (ICON-SMART 2021): Materials Science and Bioinformatics for Medical, Food, and Marine Industries, 2694 , 2023. doi: 10.1063/5.0118985

Last update: 2024-11-20 06:34:56

No citation recorded.