Target prediction of compounds on jamu formula using nearest profile method

Authors

  • Nur Hilal A Syahrir Hasanuddin University
  • Sumarheni Sumarheni
  • Supri Bin Hj Amir
  • Hedi Kuswanto

DOI:

https://doi.org/10.20956/jmsk.v17i2.11616

Keywords:

supervised-learning, nearest profile, jamu, compund-target protein

Abstract

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.

Author Biography

Nur Hilal A Syahrir, Hasanuddin University

Department of Mathematics

References

Afendi, F. M., Heryanto, R., Darusman, L. K., Syahrir, N. H. A., Bakri, R., & Qomariasih, N, 2016. Jamu informatics: A new perspective in jamu research. CICSJ Bulletin, Vol. 34, No. 2, 47.

Afendi, F. M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K., Ikeda, S., Takahashi, H., Altaf-Ul-Amin, M., Darusman, L. K., Saito, K., & Kanaya, S., 2012. KNApSAcK family databases: Integrated metabolite-plant species databases for multifaceted plant research. Plant and Cell Physiology, Vol. 53, No. 2, 1–12. https://doi.org/10.1093/pcp/pcr165

Bajusz, D., Rácz, A., & Héberger, K., 2015. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? Journal of Cheminformatics, Vol. 7, No. 1, 1–13. https://doi.org/10.1186/s13321-015-0069-3

Bleakley, K., & Yamanishi, Y., 2009. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, Vol. 25, No. 18, 2397–2403. https://doi.org/10.1093/bioinformatics/btp433

Elfahmi, Woerdenbag, H. J., & Kayser, O., 2014. Jamu: Indonesian traditional herbal medicine towards rational phytopharmacological use. Journal of Herbal Medicine, Vol. 4, No. 2, 51–73. https://doi.org/10.1016/j.hermed.2014.01.002

Florkowski, C. M., 2008. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. The Clinical Biochemist. Reviews, 29 Suppl 1(Suppl 1), S83–S87. https://www.ncbi.nlm.nih.gov/pubmed/18852864

Fukunishi, Y., & Nakamura, H., 2011. Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library. Protein Science, Vol. 20, No.1, 95–106. https://doi.org/10.1002/pro.540

Liu, Y., Hong, Y., Lin, C. Y., & Hung, C. L., 2015. Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System. International Journal of Genomics, 2015. https://doi.org/10.1155/2015/761063

Tung, C.-W., 2015. Public Databases of Plant Natural Products for Computational Drug Discovery. Current Computer Aided-Drug Design, Vol. 10, No. 3, 191–196. https://doi.org/10.2174/1573409910666140414145934

Wang, Y., Bryant, S. H., Cheng, T., Wang, J., Gindulyte, A., Shoemaker, B. A., Thiessen, P. A., He, S., & Zhang, J., 2017. PubChem BioAssay: 2017 update. Nucleic Acids Research, Vol. 45, No. D1, D955–D963. https://doi.org/10.1093/nar/gkw1118

Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., MacIejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, Di., … Wilson, M., 2018. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, Vol. 46, No. D1, D1074–D1082. https://doi.org/10.1093/nar/gkx1037

Wu, Z., Li, W., Liu, G., & Tang, Y., 2018. Network-based methods for prediction of drug-target interactions. Frontiers in Pharmacology, Vol.9, 1–14. https://doi.org/10.3389/fphar.2018.01134

Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M., 2008. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, Vol. 24, No.23, 232–240. https://doi.org/10.1093/bioinformatics/btn162

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Published

2020-12-23

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Research Articles