Integrative Network Pharmacology and In Silico Analysis of Sauropus androgynus Active Compounds as Potential Supplements Against Childhood Stunting

https://doi.org/10.22146/ijc.106207

Arwansyah Arwansyah(1*), Sri Mulyani Sabang(2), Tahril Tahril(3), Abdur Rahman Arif(4), Jamaludin Musa Sakung(5), Muhammad Sulaiman Zubair(6), Setyanto Tri Wahyudi(7)

(1) Department of Chemistry Education, Faculty of Teacher Training and Education, Tadulako University, Jl. Soekarno Hatta No. 9, Palu 94119, Indonesia
(2) Department of Chemistry Education, Faculty of Teacher Training and Education, Tadulako University, Jl. Soekarno Hatta No. 9, Palu 94119, Indonesia
(3) Department of Chemistry Education, Faculty of Teacher Training and Education, Tadulako University, Jl. Soekarno Hatta No. 9, Palu 94119, Indonesia
(4) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Jl. Perintis Kemerdekaan No. 10, Makassar 90241, Indonesia
(5) Department of Nutrition, Faculty of Public Health, Tadulako University, Jl. Soekarno Hatta No. 9, Palu 94119, Indonesia
(6) Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Tadulako University, Jl. Soekarno Hatta No. 9, Palu 94119, Indonesia
(7) Department of Physics, Faculty of Mathematics and Natural Sciences, IPB University, Jl. Raya Dramaga, Bogor 16680, Indonesia
(*) Corresponding Author

Abstract


Stunted growth remains a critical public health concern, particularly in developing regions. This study investigates the potential of active compounds derived from Sauropus androgynus as dietary supplements for preventing growth stunting, employing a network pharmacology approach. Fifteen phytochemicals were analyzed, leading to the identification of 11 growth-related target proteins. Among them, protein tyrosine phosphatase nonreceptor type 11 (PTPN11) emerged as a key regulatory protein related to cell growth, as determined by protein-protein interaction network analysis. Functional enrichment and pathway analyses further highlighted the relevance of these targets in growth-related mechanisms. Molecular docking was performed to investigate the molecular interactions between the active compounds of S. androgynous and PTPN11. The findings revealed that all compounds could form a complex at the active site of PTPN11. Furthermore, the top five ligands were subjected to MD simulation to evaluate the structural stability of all complexes. Among them, only two lead compounds, such as paeonol and phenylacetaldehyde, are stable in complex with PTPN11 during simulation. The findings suggest that S. androgynus contains promising active compounds that may serve as functional food supplements for preventing stunted growth, highlighting their potential role in improving child growth outcomes among at-risk populations.


Keywords


stunting; supplement; network analysis; in silico



References

[1] Beal, T., Tumilowicz, A., Sutrisna, A., Izwardy, D., and Neufeld, L.M., 2018, A review of child stunting determinants in Indonesia, Matern. Child Nutr., 14 (4), e12617.

[2] de Onis, M., and Branca, F., 2016, Childhood stunting: A global perspective, Matern. Child Nutr., 12, 12–26.

[3] Laksono, A.D., Wulandari, R.D., Amaliah, N., and Wisnuwardani, R.W., 2022, Stunting among children under two years in Indonesia: Does maternal education matter?, PLoS One, 17 (7), e0271509.

[4] Titaley, C.R., Ariawan, I., Hapsari, D., Muasyaroh, A., and Dibley, M.J., 2019, Determinants of the stunting of children under two years old in Indonesia: A multilevel analysis of the 2013 Indonesia basic health survey, Nutrients, 11 (5), 1106.

[5] Cahyaningsih, R., Magos Brehm, J., and Maxted, N., 2021, Setting the priority medicinal plants for conservation in Indonesia, Genet. Resour. Crop Evol., 68 (5), 2019–2050.

[6] Nurdianti, R.R., Nuryana, R.S., Handoko, A., Hernaman, I., Ramdani, D., Jayanegara, A., Dickhoefer, U., Böttger, C., and Südekum, K.H., 2023, Nutritional compositions of katuk leaves and their supplementation to hays of different quality: An in vitro study, J. Agric. Sci., 161 (3), 428–437.

[7] Yunita, O., Rantam, F.A., and Yuwono, M., 2019, Metabolic fingerprinting of Sauropus androgynus (L.) Merr. leaf extracts, Pharm. Sci. Asia, 46 (2), 69–79.

[8] Ge, Q., Chen, L., Tang, M., Zhang, S., Liu, L., Gao, L., Ma, S., Kong, M., Yao, Q., Feng, F., and Chen, K., 2018, Analysis of mulberry leaf components in the treatment of diabetes using network pharmacology, Eur. J. Pharmacol., 833, 50–62.

[9] Yuan, C., Wang, M.H., Wang, F., Chen, P.Y., Ke, X.G., Yu, B., Yang, Y.F., You, P.T., and Wu, H.Z., 2021, Network pharmacology and molecular docking reveal the mechanism of Scopoletin against non-small cell lung cancer, Life Sci., 270, 119105.

[10] Rahmadani, K., Manguntungi, B., Arwansyah, A., Jumadi, O., Khizbullah, M.A., Hidayat, A., Ayunda, N.G.A., Faiz, M., Vanggy, L.R., and Septiawati, E., 2022, Efficiency of nitrification inhibitor on designing nitrogen fertilizer by neem compounds based on molecular docking, Trends Sci., 20 (1), 6395.

[11] Eberhardt, J., Santos-Martins, D., Tillack, A.F., and Forli, S., 2021, AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings, J. Chem. Inf. Model., 61 (8), 3891–3898.

[12] Salomon-Ferrer, R., Case, D.A., and Walker, R.C., 2013, An overview of the Amber biomolecular simulation package, WIREs Comput. Mol. Sci., 3 (2), 198–210.

[13] Yao, Z.J., Dong, J., Che, Y.J., Zhu, M.F., Wen, M., Wang, N.N., Wang, S., Lu, A.P., and Cao, D.S., 2016, TargetNet: A web service for predicting potential drug–target interaction profiling via multi-target SAR models, J. Comput.-Aided Mol. Des., 30 (5), 413–424.

[14] Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., Jensen, L.J., and von Mering, C., 2019, STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47 (D1), D607–D613.

[15] Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T., 2003, Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (11), 2498–2504.

[16] Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A.H., Tanaseichuk, O., Benner, C., and Chanda, S.K., 2019, Metascape provides a biologist-oriented resource for the analysis of systems-level datasets, Nat. Commun., 10 (1), 1523.

[17] Arwansyah, A., Arif, A.R., Ramli, I., Hasrianti, H., Kurniawan, I., Ambarsari, L., Sumaryada, T.I., and Taiyeb, M., 2022, Investigation of active compounds of Brucea javanica in treating hypertension using a network pharmacology‐based analysis combined with homology modeling, molecular docking and molecular dynamics simulation, ChemistrySelect, 7 (1), e202102801.

[18] Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B.A., Thiessen, P.A., Yu, B., Zaslavsky, L., Zhang, J., and Bolton, E.E., 2021, PubChem in 2021: New data content and improved web interfaces, Nucleic Acids Res., 49 (D1), D1388–D1395.

[19] Bachtiar, Z., Mustopa, A.Z., Astuti, R.I., Fauziyah, F., Fatimah, F., Rozirwan, R., Wulandari, T.N.M., Wijaya, D.P., Agustriani, F., Arwansyah, A., Irawan, H., and Mamangkey, J., 2023, Production of codon-optimized Factor C fragment from Tachypleus gigas in the Pichia pastoris GS115 expression system for endotoxin detection, J. Genet. Eng. Biotechnol., 21 (1), 103.

[20] Arwansyah, A., Arif, A.R., Syahputra, G., Sukarti, S., and Kurniawan, I., 2021, Theoretical studies of thiazolyl-pyrazoline derivatives as promising drugs against malaria by QSAR modelling combined with molecular docking and molecular dynamics simulation, Mol. Simul., 47 (12), 988–1001.

[21] Arwansyah, A., Rahmawati, S., Nuryanti, S., Yusuf, Y., Hartono, H., and Arif, A.R., 2025, Molecular investigation on active compounds in papaya leaves (Carica papaya Linn) as anti-malaria using network pharmacology, molecular docking, clustering-based analysis and molecular dynamics simulation, Phytomed. Plus, 5 (1), 100713.

[22] Kim, S., 2021, Exploring chemical information in PubChem, Curr. Protoc., 1 (8), e217.

[23] Idrees, M., Xu, L., Song, S.H., Joo, M.D., Lee, K.L., Muhammad, T., El Sheikh, M., Sidrat, T., and Kong, I.K., 2019, PTPN11 (SHP2) is indispensable for growth factors and cytokine signal transduction during bovine oocyte maturation and blastocyst development, Cells, 8 (10), 1272.

[24] Ferreira, L.V, Souza, S.A.L., Arnhold, I.J.P., Mendonca, B.B., and Jorge, A.A.L., 2005, PTPN11 (protein tyrosine phosphatase, nonreceptor type 11) mutations and response to growth hormone therapy in children with Noonan syndrome, J. Clin. Endocrinol. Metab., 90 (9), 5156–5160.

[25] Tartaglia, M., Niemeyer, C.M., Fragale, A., Song, X., Buechner, J., Jung, A., Hählen, K., Hasle, H., Licht, J.D., and Gelb, B.D., 2003, Somatic mutations in PTPN11 in juvenile myelomonocytic leukemia, myelodysplastic syndromes and acute myeloid leukemia, Nat. Genet., 34 (2), 148–150.

[26] Griger, J., Schneider, R., Lahmann, I., Schöwel, V., Keller, C., Spuler, S., Nazare, M., and Birchmeier, C., 2017, Loss of PTPN11 (SHP2) drives satellite cells into quiescence, Elife, 6, e21552.

[27] Proctor, E.A., and Dokholyan, N.V., 2016, Applications of discrete molecular dynamics in biology and medicine, Curr. Opin. Struct. Biol., 37, 9–13.

[28] Hospital, A., Goñi, J.R., Orozco, M., and Gelpí, J.L., 2015, Molecular dynamics simulations: Advances and applications, Adv. Appl. Bioinf. Chem., 8, 37–47.

[29] van Gunsteren, W.F., Daura, X., Hansen, N., Mark, A.E., Oostenbrink, C., Riniker, S., and Smith, L.J., 2018, Validation of molecular simulation: An overview of issues, Angew. Chem., Int. Ed., 57 (4), 884–902.

[30] Mustopa, A.Z., Izaki, A.F., Suharsono, S., Fatimah, F., Fauziyah, F., Damarani, R., Arwansyah, A., Wahyudi, S.T., Sari, S.S., Rozirwan, R., and Bachtiar, Z., 2023, Characterization, protein modeling, and molecular docking of factor C from Indonesian horseshoe crab (Tachypleus gigas), J. Genet. Eng. Biotechnol., 21 (1), 44.

[31] da Fonseca, A.M., Caluaco, B.J., Madureira, J.M.C., Cabongo, S.Q., Gaieta, E.M., Djata, F., Colares, R.P., Neto, M.M., Fernandes, C.F.C., Marinho, G.S., dos Santos, H.S., and Marinho, E.S., 2024, Screening of potential inhibitors targeting the main protease structure of SARS-CoV-2 via molecular docking, and approach with molecular dynamics, RMSD, RMSF, H-bond, SASA and MMGBSA, Mol. Biotechnol., 66 (8), 1919–1933.

[32] Moteki, H., Ogihara, M., and Kimura, M., 2022, S-Allyl-L-cysteine promotes cell proliferation by stimulating growth hormone receptor/Janus kinase 2/phospholipase C pathways and promoting insulin-like growth factor type-I secretion in primary cultures of adult rat hepatocytes, Biol. Pharm. Bull., 45 (5), 625–634.

[33] Garcia, J.M., and Polvino, W.J., 2009, Pharmacodynamic hormonal effects of anamorelin, a novel oral ghrelin mimetic and growth hormone secretagogue in healthy volunteers, Growth Horm. IGF Res., 19 (3), 267–273.

[34] Platel, K., and Srinivasan, K., 2017, Nutritional profile of chekurmanis (Sauropus androgynus), A less explored green leafy vegetable, Indian J. Nutr. Diet., 54 (3), 243–252.

[35] Intan, P.R., Alegantina, S., Isnawati, A., Yunarto, N., Ekawasti, F., Rinendyaputri, R., Sunarno, S., Sani, Y., Mariya, S.S., and Handharyani, E., 2024, Effects of a blend extract of Sauropus androgynus, Moringa oleifera, and Coleus amboinicus on milk production in lactating rats, Open Vet. J., 14 (12), 3630–3639.

[36] Tang, Y., Yang, H., Yu, J., Li, Z., Xu, Q., Xu, Q., Jia, G., and Sun, N., 2023, Network pharmacology-based prediction and experimental verification of the involvement of the PI3K/Akt pathway in the anti-thyroid cancer activity of crocin, Arch. Biochem. Biophys., 743, 109643.

[37] Hata, H., Phuoc Tran, D., Marzouk Sobeh, M., and Kitao, A., 2021, Binding free energy of protein/ligand complexes calculated using dissociation parallel cascade selection molecular dynamics and Markov state model, Biophys. Physicobiol., 18, 305–316.

[38] Tran, D.P., Takemura, K., Kuwata, K., and Kitao, A., 2018, Protein–ligand dissociation simulated by parallel cascade selection molecular dynamics, J. Chem. Theory Comput., 14 (1), 404–417.



DOI: https://doi.org/10.22146/ijc.106207

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