Network Pharmacology and Molecular Docking of the Marine Peptide FVKVEVLPQ: A Computational Exploration of Putative Breast Cancer Targets
- Authors
-
-
Amanda Yulita Amalia
Department of Medicine, Faculty of Medicine, Universitas Andalas, Padang, Indonesia
-
Dessy Arisanty
-
Henny Mulyani
-
Kevin Nathaniel Cuandra
-
- Keywords:
- Array, Array, Array, Array
- Abstract
-
Background: FVKVEVLPQ from Arbacia lixula has previously been studied computationally in triple-negative breast cancer. This study explored its putative targets in breast cancer more broadly using network pharmacology and molecular docking.
Methods: Peptide-associated targets were predicted using SwissTargetPrediction and SEA. Breast cancer-associated genes were obtained from DisGeNET and GeneCards. Shared genes underwent protein interaction and enrichment analyses. ADMET properties and toxicity were predicted using ADMETlab 3.0 and ProTox-III. Six proteins prioritized by network analysis were examined by molecular docking in MOE v2022.02.
Results: The analysis identified 247 putative peptide targets and 3,431 breast cancer-associated genes, including 89 shared genes. The protein interaction network contained 87 nodes and 521 edges. FVKVEVLPQ had low predicted Caco-2 permeability (?6.565), plasma protein binding of 1.0%, and an estimated acute oral LD?? of 836 mg/kg. Docking S-scores ranged from ?9.08 to ?10.77 across STAT3, CASP3, ERBB2, CCND1, ABL1, and IGF1R.
Discussion: Enrichment analysis identified biological processes involving PDGF and VEGF receptor signaling. Docking indicated possible contacts with the six selected proteins, without establishing their biological effects.
Conclusion: The findings generate testable hypotheses about FVKVEVLPQ interactions with breast cancer-associated proteins. Experimental studies are needed to assess target engagement, biological activity, and safety.
- References
-
1. World Health Organization. Cancer Today, Global Cancer Observatory. Lyon: International Agency for Research on Cancer. 2020. Available from: https://gco.iarc.fr/today/online-analysis-multi-bars.
2. Lee J, Choi MK, Song IS. Recent advances in doxorubicin formulation to enhance pharmacokinetics and tumor targeting. Pharmaceuticals (Basel). 2023;16:802. doi:10.3390/ph16060802.
3. Luparello C, Ragona D, Asaro DML, Lazzara V, Affranchi F, Arizza V, et al. Cell-free coelomic fluid extracts of the sea urchin Arbacia lixula impair mitochondrial potential and stimulate autophagic activity in MDA-MB231 breast cancer cells. J Mar Sci Eng. 2020;8:261. doi:10.3390/jmse8040261.
4. Arisanty D, Cuandra KN, Putra AE, Rita RS, Ilmiawati C, Anggraini FT. Exploring the potential of Arbacia lixula coelomic fluid peptides as cell cycle and angiogenesis inhibitor for triple-negative breast cancer therapy: In silico study. J Pharm Pharmacogn Res. 2025;13(1):242–253. doi:10.56499/jppres24.1991_13.1.242.
5. Wang L, Wang N, Zhang W, Cheng X, Yan Z, Shao G, et al. Therapeutic peptides: current applications and future directions. Signal Transduct Target Ther. 2022;7:48. doi:10.1038/s41392-022-00904-4.
6. Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, et al. Computational/in silico methods in drug target and lead prediction. Brief Bioinform. 2020;21:1663–1675. doi:10.1093/bib/bbz103.
7. Fu L, Shi S, Yi J, Wang N, He Y, Wu Z, et al. ADMETlab 3.0: an updated comprehensive online ADMET prediction platform. Nucleic Acids Res. 2024;52:W422–W431. doi:10.1093/nar/gkae236.
8. Banerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024;52:W513–W520. doi:10.1093/nar/gkae303.
9. Hu Y, Guo X, Yun Y, Lu L, Huang X, Jia S. DisGeNET: a disease-centric interaction database among diseases and associated genes. Database (Oxford). 2025;2025:baae122. doi:10.1093/database/baae122.
10. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.1–1.30.33. doi:10.1002/cpbi.5.
11. Zafar A, Khatoon S, Khan MJ, Abu J, Naeem A. Advancements and limitations in traditional anti-cancer therapies: a comprehensive review. Discov Oncol. 2025;16:607. doi:10.1007/s12672-025-02198-8.
12. Zhang J. Marine-derived compounds: a new horizon in cancer, renal, and metabolic disease therapeutics. Mar Drugs. 2025;23:283. doi:10.3390/md23070283.
13. He Y, Sun MM, Zhang GG, Yang J, Chen KS, Xu WW, et al. Targeting PI3K/Akt signal transduction for cancer therapy. Signal Transduct Target Ther. 2021;6:425. doi:10.1038/s41392-021-00828-5.
14. Li YW, Dai LJ, Wu XR, Zhao S, Xu YZ, Jin X, et al. Molecular characterization and classification of HER2-positive breast cancer inform tailored therapeutic strategies. Cancer Res. 2024;84:3669–3683. doi:10.1158/0008-5472.CAN-23-4066.
15. Yang X, Yang D, Qi X, Luo X, Zhang G. Endocrine treatment mechanisms in triple-positive breast cancer: from targeted therapies to advances in precision medicine. Front Oncol. 2025;14:1467033. doi:10.3389/fonc.2025.1467033.
16. Samad MA, Ahmad I, Khan MR, Suhail M, Zughaibi TA, Al-Abbasi FA, et al. Breast cancer: molecular pathogenesis and targeted therapy. MedComm. 2025;6:e70404. doi:10.1002/mco2.70404.
17. Agoni C, Fernández-Díaz R, Timmons PB, Adelfio A, Gómez H, Shields DC. Molecular modelling in bioactive peptide discovery and characterisation. Biomolecules. 2025;15:524. doi:10.3390/biom15040524.
18. Dong Q, Wang D, Song C, Gong C, Liu Y, Zhou X, et al. ABL1-mediated phosphorylation promotes FOXM1-related tumorigenicity by increasing FOXM1 stability. Cell Death Differ. 2024;31:1285–1301. doi:10.1038/s41418-024-01339-w.
19. Dou H, Yu PY, Liu YQ, Zhu Y, Li FC, Wang YY, et al. Recent advances in caspase-3, breast cancer, and traditional Chinese medicine: a review. J Chemother. 2024;36:370–388. doi:10.1080/1120009X.2023.2278014.
20. Fu YC, Liang SB, Luo M, Wang XP. Intratumoral heterogeneity and drug resistance in cancer. Cancer Cell Int. 2025;25:103. doi:10.1186/s12935-025-03734-w.
- Cover Image
-
- Downloads
- Published
- 2026-10-20
- Section
- Article Review
- License
-
Copyright (c) 2026 Amanda Yulita Amalia, Dessy Arisanty, Henny Mulyani, Kevin Nathaniel Cuandra
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright and grant JIMKI the right of first publication. Articles are published under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), permitting sharing and adaptation for non-commercial purposes with appropriate attribution to the authors and the original publication.
