Target Baru Pengobatan Meningitis berdasarkan Centrality Measure jaringan protein dan Self Oganizing Map.

Siti Amiroch, Mohammad Hamim Zajuli al Faroby, Mohammad Dzulfikar Fauzi

Abstract


Meningitis is a serious threat to health with potentially fatal consequences. Understanding protein interactions related to chronic conditions is crucial for the development of effective treatments. In silico analysis is considered to have greater effectiveness because it simulates through computation and tries various possibilities at a lower cost. This study aims to analyze protein-protein interactions related to Meningitis with cluster analysis techniques on undirected graphs. The proposed method is the Self Organizing Map (SOM) algorithm as a cluster. This algorithm can cluster undirected graph-based protein interaction data. Protein data involved in Meningitis disease comes from OMIM. From this data, proteins belonging to the gene locus are explored for their interactions, resulting in interaction data in the form of an undirected graph. The combination of centrality measure is used for feature engineering on undirected graph data. The main protein candidates are potentially located in the Cluster 1 model with the largest silhouette score (0.359) and Davies-Bouldin Index (1.667). The cluster has 18 proteins with the highest significance to Meningitis. From the overall centrality ranking results, the three highest significance proteins are CISH (3.921222), TNFSF10 (3.403541), and ICAM3 (2.623702) which have the potential to become Meningitis target proteins. CISH protein has the highest overall centrality score value compared to the others, so CISH protein may be a new alternative in the treatment of Meningitis.


Keywords


Self-organizing Map; Pembobotan Terpusat; Interaksi-interaksi antar Protein; Penanganan Meningitis

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References


A. Kohil, S. Jemmieh, M. K. Smatti, and H. M. Yassine, “Viral meningitis: an overview,” Archives of Virology 2021 166:2, vol. 166, no. 2, pp. 335–345, Jan. 2021, doi: 10.1007/S00705-020-04891-1.

N. Principi and S. Esposito, “Bacterial meningitis: new treatment options to reduce the risk of brain damage,” Expert Opin Pharmacother, vol. 21, no. 1, pp. 97–105, Jan. 2019, doi: 10.1080/14656566.2019.1685497.

A. Pizzorno, B. Padey, O. Terrier, and M. Rosa-Calatrava, “Drug Repurposing Approaches for the Treatment of Influenza Viral Infection: Reviving Old Drugs to Fight Against a Long-Lived Enemy,” Front Immunol, vol. 10, no. MAR, p. 531, 2019, doi: 10.3389/FIMMU.2019.00531.

M. H. Z. Al Faroby, H. N. Fadhilah, and F. H. Sembiring, “Identifikasi Interaksi Protein-Protein Meningitis Menggunakan ClusterONE dan Analisis Jaringan,” Journal of Advances in Information and Industrial Technology, vol. 4, no. 1, pp. 17–28, May 2022, doi: 10.52435/jaiit.v4i1.180.

O. Hoffman and J. R. Weber, “Pathophysiology and treatment of bacterial meningitis,” Ther Adv Neurol Disord, vol. 2, no. 6, pp. 401–412, Nov. 2009, doi: 10.1177/1756285609337975.

S. Amiroch, M. H. Z. Al Faroby, M. I. Irawan, I. Mukhlash, and A. C. Nidhom, “Analysis of protein-protein interaction to obtain significant protein in influenza virus type A/H9N2,” in AIP Conference Proceedings, AIP Publishing LLCAIP Publishing, Aug. 2022, p. 020021. doi: 10.1063/5.0083336.

E. Ragueneau, A. Shrivastava, J. H. Morris, N. del-Toro, H. Hermjakob, and P. Porras, “IntAct App: a Cytoscape application for molecular interaction network visualization and analysis,” Bioinformatics, vol. 37, no. 20, pp. 3684–3685, Oct. 2021, doi: 10.1093/BIOINFORMATICS/BTAB319.

Z. Kang et al., “Structured graph learning for clustering and semi-supervised classification,” Pattern Recognit, vol. 110, p. 107627, Feb. 2021, doi: 10.1016/j.patcog.2020.107627.

P. B. Lamichhane and W. Eberle, “Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Nov. 2022, pp. 706–713. doi: 10.1109/ICDMW58026.2022.00097.

J. S. Amberger, C. A. Bocchini, A. F. Scott, and A. Hamosh, “OMIM.org: Leveraging knowledge across phenotype-gene relationships,” Nucleic Acids Res, vol. 47, no. D1, pp. D1038–D1043, Jan. 2019, doi: 10.1093/nar/gky1151.

D. Szklarczyk et al., “The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets,” Nucleic Acids Res, vol. 49, no. D1, pp. D605–D612, Jan. 2021, doi: 10.1093/nar/gkaa1074.

B. Liu, M. Lai, J.-L. Wu, C. Fu, and A. Binaykia, “Patent analysis and classification prediction of biomedicine industry: SOM-KPCA-SVM model,” Multimed Tools Appl, vol. 79, no. 15–16, pp. 10177–10197, Apr. 2020, doi: 10.1007/s11042-019-7422-x.

F. Ros, R. Riad, and S. Guillaume, “PDBI: A partitioning Davies-Bouldin index for clustering evaluation,” Neurocomputing, vol. 528, pp. 178–199, Apr. 2023, doi: 10.1016/j.neucom.2023.01.043.

M. Wang, Z. Cang, and G.-W. Wei, “A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation,” Nat Mach Intell, vol. 2, no. 2, pp. 116–123, Feb. 2020, doi: 10.1038/s42256-020-0149-6.

M. C. R. Melo, R. C. Bernardi, C. de la Fuente-Nunez, and Z. Luthey-Schulten, “Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories,” J Chem Phys, vol. 153, no. 13, Oct. 2020, doi: 10.1063/5.0018980.

G. Sivagamasundari and P. Latha, “Clustering Protein Super Secondary Structure with Artificial Neural Network Based Self Organizing Map Machine Learning Approach Using In-Memory Computing Environment,” J Comput Theor Nanosci, vol. 17, no. 5, pp. 2039–2042, May 2020, doi: 10.1166/jctn.2020.8846.

I. F. Ashari, E. Dwi Nugroho, R. Baraku, I. Novri Yanda, and R. Liwardana, “Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta,” Journal of Applied Informatics and Computing, vol. 7, no. 1, pp. 89–97, Jul. 2023, doi: 10.30871/jaic.v7i1.4947.

A. M. Bagirov, R. M. Aliguliyev, and N. Sultanova, “Finding compact and well-separated clusters: Clustering using silhouette coefficients,” Pattern Recognit, vol. 135, p. 109144, Mar. 2023, doi: 10.1016/j.patcog.2022.109144.

M. Shutaywi and N. N. Kachouie, “Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering,” Entropy, vol. 23, no. 6, p. 759, Jun. 2021, doi: 10.3390/e23060759.

L. M. P. Mariño and F. de A. T. de Carvalho, “Two weighted c-medoids batch SOM algorithms for dissimilarity data,” Inf Sci (N Y), vol. 607, pp. 603–619, Aug. 2022, doi: 10.1016/j.ins.2022.06.019.

Z. Wu et al., “The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning,” J Ophthalmic Inflamm Infect, vol. 14, no. 1, pp. 1–19, Dec. 2024, doi: 10.1186/S12348-024-00410-4/FIGURES/15.

C. He et al., “CD19 CAR antigen engagement mechanisms and affinity tuning,” Sci Immunol, vol. 8, no. 81, Mar. 2023, doi: 10.1126/sciimmunol.adf1426.

L. Y. El-Sharkawy, D. Brough, and S. Freeman, “Inhibiting the NLRP3 Inflammasome,” Molecules, vol. 25, no. 23, p. 5533, Nov. 2020, doi: 10.3390/molecules25235533.

C. Ji, Z. Yang, X. Zhong, and J. Xia, “The role and mechanism of CARD9 gene polymorphism in diseases,” Biomed J, vol. 44, no. 5, pp. 560–566, Oct. 2021, doi: 10.1016/j.bj.2020.12.006.

D. Lai et al., “SARS-CoV-2 N Protein Triggers Acute Lung Injury via Modulating Macrophage Activation and Infiltration in in vitro and in vivo,” J Inflamm Res, vol. Volume 16, pp. 1867–1877, Apr. 2023, doi: 10.2147/JIR.S405722.

O. M. Delmonte et al., “SASH3 variants cause a novel form of X-linked combined immunodeficiency with immune dysregulation,” Blood, vol. 138, no. 12, pp. 1019–1033, Sep. 2021, doi: 10.1182/blood.2020008629.

M. R. Zabihi, B. Farhadi, and M. Akhoondian, “Complement protein expression changes in various conditions of breast cancer: in-silico analyses—experimental research,” Annals of Medicine & Surgery, vol. 86, no. 9, pp. 5152–5161, Sep. 2024, doi: 10.1097/MS9.0000000000002216.

Y. Gamallat et al., “ARPC1B Is Associated with Lethal Prostate Cancer and Its Inhibition Decreases Cell Invasion and Migration In Vitro,” Int J Mol Sci, vol. 23, no. 3, p. 1476, Jan. 2022, doi: 10.3390/ijms23031476.

J. Li et al., “A novel compound heterozygous mutation in DGKE in a Chinese patient causes atypical hemolytic uremic syndrome,” Hematology, vol. 25, no. 1, pp. 101–107, Jan. 2020, doi: 10.1080/16078454.2020.1731969.

X. Shen, X. Jin, S. Fang, and J. Chen, “EFEMP2 upregulates PD-L1 expression via EGFR/ERK1/2/c-Jun signaling to promote the invasion of ovarian cancer cells,” Cell Mol Biol Lett, vol. 28, no. 1, p. 53, Jul. 2023, doi: 10.1186/s11658-023-00471-8.

F. Bloch, M. O. Jackson, and P. Tebaldi, “Centrality measures in networks,” Soc Choice Welfare, vol. 61, no. 2, pp. 413–453, Aug. 2023, doi: 10.1007/s00355-023-01456-4.

M. S. Usman, W. A. Kusuma, F. M. Afendi, and R. Heryanto, “Identification of Significant Proteins Associated with Diabetes Mellitus Using Network Analysis of Protein-Protein Interactions,” Computer Engineering and Applications, vol. 8, no. 1, pp. 41–52, Feb. 2019.

W. Naser, S. Maymand, D. Dlugolenski, F. Basheer, and A. C. Ward, “The Role of Cytokine-Inducible SH2 Domain-Containing Protein (CISH) in the Regulation of Basal and Cytokine-Mediated Myelopoiesis,” Int J Mol Sci, vol. 24, no. 16, p. 12757, Aug. 2023, doi: 10.3390/ijms241612757.

S. Maymand et al., “Role of Cytokine-Inducible SH2 Domain-Containing (CISH) Protein in the Regulation of Erythropoiesis,” Biomolecules, vol. 13, no. 10, p. 1510, Oct. 2023, doi: 10.3390/biom13101510.

A. Yadav et al., “Early transcriptomic host response signatures in the serum of dengue patients provides insights into clinical pathogenesis and disease severity,” Sci Rep, vol. 13, no. 1, p. 14170, Aug. 2023, doi: 10.1038/s41598-023-41205-2.




DOI: http://dx.doi.org/10.12962/limits.v21i3.21776

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