Análise computacional de interações proteína–ligante envolvendo compostos de Acmella oleracea (L.) R.K. Jansen com relevância antitumoral

Vitória Ramos de Moura Santos; Tiago dos Reis Almeida Almeida; Luana Priscilla Rodrigues Macedo; Ana Lívia Ferreira dos Santos

doi:10.47236/2594-7036.2026.v10.2016

Autores

Vitória Ramos de Moura Santos Instituto Federal do Tocantins https://orcid.org/0009-0009-9295-9465
Tiago dos Reis Almeida Almeida Instituto Federal do Tocantins https://orcid.org/0000-0001-6927-4016
Luana Priscilla Rodrigues Macedo Instituto Federal do Tocantins https://orcid.org/0009-0009-6383-5842
Ana Lívia Ferreira dos Santos Instituto Federal do Tocantins https://orcid.org/0009-0007-7519-4638

DOI:

https://doi.org/10.47236/2594-7036.2026.v10.2016

Palavras-chave:

Acmella oleracea, ADMET, Carcinogênese, Docking molecular, Produtos naturais, Triagem in silico

Resumo

A prospecção de compostos naturais com potencial antitumoral tem se destacado como uma estratégia promissora no desenvolvimento de novos agentes bioativos. Nesse contexto, o presente estudo teve como objetivo avaliar, por meio de abordagens in silico, o potencial de metabólitos derivados de Acmella oleracea frente a alvos moleculares associados à carcinogênese. Foram selecionados 18 compostos da espécie, submetidos a estudos de docking molecular utilizando o software AutoDock Vina contra três proteínas relevantes: PMS2 (1H7U), PI3Kβ (4AJW e 4BFR) e COX-2 (3LN1). A validação do protocolo foi realizada por redocking, apresentando valores de RMSD compatíveis com a literatura. Os resultados indicaram que compostos fenólicos e flavonoídicos glicosilados apresentaram melhor desempenho nos cálculos de ancoragem, destacando-se o ligante 2, que exibiu os menores valores de energia estimada de ligação e padrões interacionais favoráveis nos diferentes alvos. Adicionalmente, a avaliação preditiva de propriedades farmacocinéticas e toxicológicas (ADMET) revelou que o ligante 4 apresentou o perfil mais equilibrado em termos de biodisponibilidade, permeabilidade e segurança preditiva. De forma integrada, os resultados sugerem que os compostos avaliados apresentam potencial in silico relevante para interação com alvos associados ao câncer, sendo o ligante 2 promissor sob a perspectiva de reconhecimento molecular e o ligante 4, sob a ótica farmacocinética. Esses achados reforçam a importância de abordagens integradas na priorização de candidatos bioativos e indicam a necessidade de validação experimental complementar.

Downloads

Não há dados estatísticos.

Métricas

Carregando Métricas ...

Biografia do Autor

Vitória Ramos de Moura Santos, Instituto Federal do Tocantins

Graduada em Licenciatura em Ciências Biológicas pelo Campus Araguatins, do Instituto Federal do Tocantins. Bolsista do Programa de Iniciação Científica do Campus Araguatins, do Instituto Federal do Tocantins. Araguatins, Tocantins, Brasil. Endereço eletrônico: vimou2101@gmail.com. Orcid: https://orcid.org/0009-0009-9295-9465. Currículo Lattes: http://lattes.cnpq.br/0438811399069443.

Tiago dos Reis Almeida Almeida, Instituto Federal do Tocantins

Mestre em Físico-Química pelo Instituto de Química de São Carlos da Universidade de São Paulo. Professor do ensino básico, técnico e tecnológico, atuando no Núcleo Docente Articulado da área de Química do Campus Araguatins, do Instituto Federal do Tocantins. Araguatins, Tocantins, Brasil. Endereço eletrônico: tiago.almeida@ifto.edu.br. Orcid: https://orcid.org/0000-0001-6927-4016. Currículo Lattes: http://lattes.cnpq.br/5004685012482665.

Luana Priscilla Rodrigues Macedo, Instituto Federal do Tocantins

Mestra em Química pela Universidade Federal do Tocantins. Professora do ensino básico, técnico e tecnológico, atuando no Núcleo Docente Articulado da área de Química do Campus Araguatins, do Instituto Federal do Tocantins. Araguatins, Tocantins, Brasil. Endereço eletrônico: luana.macedo@ifto.edu.br. Orcid: https://orcid.org/0009-0009-6383-5842. Currículo lattes: http://lattes.cnpq.br/6475213690096051.

Ana Lívia Ferreira dos Santos, Instituto Federal do Tocantins

Graduada em Licenciatura em Ciências Biológicas pelo Campus Araguatins, do Instituto Federal do Tocantins. Bolsista do Programa de Iniciação Científica do Campus Araguatins, do Instituto Federal do Tocantins. Araguatins, Tocantins, Brasil. Endereço eletrônico: ferreiralivia803@gmail.com. Orcid: https://orcid.org/0009-0007-7519-4638. Currículo Lattes: http://lattes.cnpq.br/0892630091846602.

Referências

AGU, P. C.; AFIUKWA, C. A.; ORJI, O. U.; EZEH, E. M.; OFOKE, I. H.; OGBU, C. O.; UGWUJA, E. I.; AJA, P. M. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Scientific Reports, [s. l.], v. 13, art. 13398, 2023. DOI: https://doi.org/10.1038/s41598-023-40160-2.

AKTAR, M. A. et al. Pharmacological and phytochemical review of Acmella oleracea: a comprehensive analysis of its therapeutic potential. Discover Applied Sciences, v. 6, n. 1, p. 412, 2024. DOI: https://doi.org/10.1007/s42452-024-06108-5.

APLIN, C. et al. Evolving experimental techniques for structure-based drug design. Journal of Physical Chemistry B, Washington, v. 126, n. 35, p. 6599-6607, 2022. DOI: https://doi.org/10.1021/acs.jpcb.2c04344.

AZAD, I. et al. Updates on drug designing approach through computational strategies: a review. Future Science OA, Londres, v. 9, n. 5, 2023. DOI: https://doi.org/10.2144/fsoa-2022-0085.

BELLUMORI, M. et al. Acmella oleracea (L.) R.K. Jansen: alkylamides and phenolic compounds in aerial parts and roots of in vitro seedlings. Journal of Pharmaceutical and Biomedical Analysis, [s. l.], v. 220, art. 114991, 2022. DOI: https://doi.org/10.1016/j.jpba.2022.114991.

BRAY, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, Hoboken, v. 74, n. 3, p. 229-263, 2024. DOI: https://doi.org/10.3322/caac.21834.

BUCKLEY, M. E.; NDUKWE, A. R. N.; NAIR, P. C.; RANA, S.; FAIRFULL-SMITH, K. E.; GANDHI, N. S. Comparative assessment of docking programs for docking and virtual screening of ribosomal oxazolidinone antibacterial agents. Antibiotics, v. 12, art. 463, 2023. DOI: https://doi.org/10.3390/antibiotics12030463.

CHEN, H. et al. Structure-based design of anticancer drugs based on β-elemene: research foundations and development potential. Journal of Pharmaceutical Analysis, Amsterdã, 2025. DOI: https://doi.org/10.1016/j.jpha.2025.101325.

CREANZA, T. M. et al. Structure-based prediction of hERG-related cardiotoxicity. Journal of Chemical Information and Modeling, [s. l.], v. 62, n. 18, p. 4390-4403, 2022. DOI: https://doi.org/10.1021/acs.jcim.1c00744.

DAINA, A.; MICHIELIN, O.; ZOETE, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, [s. l.], v. 7, n. 1, p. 42717, 2017. DOI: https://doi.org/10.1038/srep42717.

FROMM, M. F. P-glycoprotein: a defense mechanism limiting oral bioavailability and CNS accumulation of drugs. International Journal of Clinical Pharmacology and Therapeutics, [s. l.], v. 38, n. 2, p. 69-74, 2000. DOI: https://doi.org/10.5414/cpp38069.

GIMENO, A. et al. Minimizing the entropy penalty for ligand binding: lessons from the molecular recognition of the histo blood-group antigens by human Galectin-3. Angewandte Chemie International Edition, Weinheim, v. 58, n. 22, p. 7268-7272, 2019. DOI: https://doi.org/10.1002/anie.201900723.

GU, Y. et al. admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties. Nucleic Acids Research, [s. l.], v. 52, n. W1, p. W432-W438, 2024. DOI: https://doi.org/10.1093/nar/gkae298.

GUARNÉ, A.; JUNOP, M. S.; YANG, W. Structure and function of the N-terminal 40 kDa fragment of human PMS2: a monomeric GHL ATPase. The EMBO Journal, Oxford, v. 20, n. 19, p. 5521-5531, 2001. DOI: https://doi.org/10.1093/emboj/20.19.5521.

HOSACK, T. et al. Drug-induced liver injury: a comprehensive review. Pharmaceuticals, [s. l.], v. 16, n. 5, p. 689, 2023. DOI: https://doi.org/10.1177/17562848231163410.

JERÔNIMO, L. B. et al. Acmella oleracea (L.) R.K. Jansen essential oils: chemical composition, antioxidant, and cytotoxic activities. Biochemical Systematics and Ecology, [s. l.], v. 112, art. 104775, 2024. DOI: 10.1016/j.bse.2023.104775.

JIN, K.; QIAN, C.; LIN, J.; LIU, B. Cyclooxygenase-2–Prostaglandin E2 pathway: a key player in tumor-associated immune cells. Frontiers in Oncology, Lausanne, v. 13, p. 1099811, 2023. DOI: https://doi.org/10.3389/fonc.2023.1099811.

LEE, B.-W. Botany, ethnopharmacology, phytochemistry, and biological activities of Acmella oleracea: a comprehensive review. Molecules, [s. l.], v. 31, n. 4, art. 677, 2026. DOI: 10.3390/molecules31040677.

LI, H. et al. Targeting PI3K family with small-molecule inhibitors in cancer therapy: current clinical status and future directions. Molecular Cancer, Londres, v. 23, n. 1, 2024. DOI: https://doi.org/10.1186/s12943-024-02072-1.

LIPINSKI, C. A. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, [s. l.], v. 46, n. 1-3, p. 3-26, 2001. DOI: https://doi.org/10.1016/S0169-409X(00)00129-0.

MA, Z.; AJIBADE, A.; ZOU, X. Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design. Communications in Information and Systems, v. 24, n. 3, p. 199-230, 2024. DOI: https://doi.org/10.4310/cis.241021221101.

MARTIS, E. A. F.; TÉLETCHÉA, S. Ten quick tips to perform meaningful and reproducible molecular docking calculations. PLoS Computational Biology, v. 21, n. 5, p. e1013030, 2025. DOI: https://doi.org/10.1371/journal.pcbi.1013030.

MILLER, E. B. et al. Enabling structure-based drug discovery utilizing predicted models. Cell, Cambridge, v. 187, n. 3, p. 521-525, 2024. DOI: https://doi.org/10.1016/j.cell.2023.12.034.

MIRANDA-FILHO, A. et al. The GLOBOCAN 2022 cancer estimates: data sources, methods, and a snapshot of the cancer burden worldwide. International Journal of Cancer, Hoboken, v. 156, n. 7, 2024. DOI: https://doi.org/10.1002/ijc.35278.

NASCIMENTO, L. E. S. et al. Phytochemical profile of different anatomical parts of jambu (Acmella oleracea (L.) R.K. Jansen): a comparison between hydroponic and conventional cultivation using PCA and cluster analysis. Food Chemistry, [s. l.], v. 327, 2020. DOI: https://doi.org/10.1016/j.foodchem.2020.127393.

ONUFRIEV, A. V.; ALEXOV, E. Protonation and pK changes in protein-ligand binding. Quarterly Reviews of Biophysics, [s. l.], v. 46, n. 2, p. 181-209, 2013. DOI: 10.1017/S0033583513000024.

PATIL, P. A.; KUMBHAR, B. V. Structure based drug design and machine learning approaches for identifying natural inhibitors against the human αβIII tubulin isotype. Scientific Reports, Londres, v. 15, n. 1, 2025. DOI: https://doi.org/10.1038/s41598-025-17708-5.

PINHEIRO, M. S. da S.; MOYSÉS, D. A.; GALUCIO, N. C. R.; SANTOS, W. O.; PINA, J. R. S.; OLIVEIRA, L. C.; SILVA, S. Y. S.; SILVA, S. da C.; FRAZÃO, N. F.; MARINHO, P. S. B.; NOVAIS, A. L. F.; KHAYAT, A. S.; MARINHO, A. M. do R. Cytotoxic and molecular evaluation of spilanthol obtained from Acmella oleracea (L.) R.K. Jansen (jambu) in human gastric cancer cells. Natural Product Research, [s. l.], v. 38, n. 10, p. 1806-1811, 2024. DOI: 10.1080/14786419.2023.2222220.

RAMÍREZ, D.; CABALLERO, J. Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules, Basel, v. 23, n. 5, p. 1038, 2018. DOI: https://doi.org/10.3390/molecules23051038.

SHAMSIAN, S.; SOKOUTI, B.; DASTMALCHI, S. Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes. BioImpacts, Tabriz, v. 14, p. 29955, 2023. DOI: https://doi.org/10.34172/bi.2023.29955.

SILVA, R. C. da; SALLET, L. A. P.; SOUSA, K. P. de. Efeitos da amora (Morus spp.) no controle dos sintomas da menopausa: uma revisão integrativa. Revista Sítio Novo, Palmas, v. 9, p. e1687, 2025. DOI: https://doi.org/10.47236/2594-7036.2025.v9.1687.

SILVEIRA, G. E.; BARROSO, M. A. de S. Upcycling de subprodutos da agroindústria da Amazônia e do Cerrado: microrrevisão de tecnologias e impactos socioeconômicos. Revista Sítio Novo, Palmas, v. 10, p. e1896, 2026. DOI: https://doi.org/10.47236/2594-7036.2026.v10.1896.

SUNG, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, Hoboken, v. 71, n. 3, p. 209-249, 2021. DOI: https://doi.org/10.3322/caac.21660.

TROTT, O.; OLSON, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, [s. l.], v. 31, n. 2, p. 455-461, 2010. DOI: https://doi.org/10.1002/jcc.21334.

TRUONG, N. T. H. et al. Effects of chemotherapy agents on circulating leukocyte populations: potential implications for the success of CAR-T cell therapies. Cancers, Basel, v. 13, n. 9, p. 2225, 2021. DOI: https://doi.org/10.3390/cancers13092225.

VEBER, D. F. et al. Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, [s. l.], v. 45, n. 12, p. 2615-2623, 2002. DOI: https://doi.org/10.1021/jm020017n.

VIJAY, U.; RAMESH, M.; DURGADEVI, R. Microbial mutagenicity assay: Ames test. Bio-protocol, [s. l.], v. 8, n. 17, e2997, 2018. DOI: 10.21769/BioProtoc.2763.

VITTORIO, S. et al. Addressing docking pose selection with structure-based deep learning: recent advances, challenges and opportunities. Computational and Structural Biotechnology Journal, v. 23, p. 2141-2151, 2024. DOI: https://doi.org/10.1016/j.csbj.2024.05.024.

WALLERSTEIN, J. et al. Entropy-entropy compensation between the protein, ligand, and solvent degrees of freedom fine-tunes affinity in ligand binding to Galectin-3C. JACS Au, Washington, v. 1, n. 4, p. 484-500, 2021. DOI: https://doi.org/10.1021/jacsau.0c00094.

WINKLER, D. A. Ligand entropy is hard but should not be ignored. Journal of Chemical Information and Modeling, Washington, v. 60, n. 10, p. 4421-4423, 2020. DOI: https://doi.org/10.1021/acs.jcim.0c01146.

YANG, C.; CHEN, E. A.; ZHANG, Y. Protein-ligand docking in the machine-learning era. Molecules, Basel, v. 27, n. 14, p. 4568, 2022. DOI: https://doi.org/10.3390/molecules27144568.

ZEIGER, E. The Ames test and the regulation of chemicals. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, [s. l.], v. 841, p. 43-48, 2019. DOI: 10.1016/j.mrgentox.2019.05.007.