7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation Against Leishmania amazonensis
Abstract
1. Introduction
2. Results
2.1. Evaluation of Coumarins’ Effects on Promastigote Viability
2.2. In Vitro Toxicity Against HaCaT Cells
2.3. Assessment of Evolution of Lesions and Parasite Load Caused by L. amazonensis
2.4. Evaluation of Renal and Hepatic Toxicity Induced by the Treatments
2.5. Histology
2.6. In Silico Analysis
2.6.1. ADME Properties of ACS47, ACS48 and ACS51
2.6.2. Interaction Profile of a Putative LaSpdSyn@coumarins Complex Based on Molecular Docking
2.6.3. Molecular Dynamics and Structural Shifts of LaSpdSyn@coumarins Complex
2.6.4. SpdSyn Conservancy on the Leishmania Genus Based on Sequence and Structure Alignment
3. Discussion
4. Materials and Methods
4.1. Compounds
4.2. Parasites
4.3. Evaluation of Coumarins’ Effects on Promastigote Viability
4.4. In Vitro Toxicity Against HaCaT Cells
4.5. Ethical Statement
4.6. In Vivo Experimental Design
4.7. Limiting Dilution Assay
4.8. Evaluation of Renal and Hepatic Toxicity
4.9. Analysis of Histological Sections
4.10. Statistical Analysis for In Vitro and In Vivo Experiments
4.11. In Silico Analysis
4.11.1. ADME Properties of ACS47, ACS48 and ACS51
4.11.2. Molecular Docking Evaluation of L. amazonensis Strain PH8 Druggable Targets
4.11.3. Molecular Dynamics Evaluation of L. amazonensis Strain PH8 Druggable Targets
4.11.4. Binding Free Energy (ΔGbind) Assessments of Coumarins Against LaSpdSyn
4.11.5. Assessment of SpdSyn Conservancy on the Leishmania Genus
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Health Organization (WHO). Leishmaniasis. Available online: https://www.who.int/news-room/fact-sheets/detail/leishmaniasis (accessed on 25 January 2026).
- Ponte-Sucre, A.; Gamarro, F.; Dujardin, J.; Barrett, M.P.; Garcı, R.; Pountain, A.W.; Mwenechanya, R.; Papadopoulou, B. Drug resistance and treatment failure in leishmaniasis: A 21st century challenge. PLoS Negl. Dis. 2017, 11, e0006052. [Google Scholar]
- Pradhan, S.; Schwartz, R.A.; Patil, A.; Grabbe, S.; Goldust, M. Treatment options for leishmaniasis. Clin. Exp. Dermatol. 2022, 47, 516–521. [Google Scholar] [PubMed]
- Wang, Y.; Wang, F.; Liu, W.; Geng, Y.; Shi, Y.; Tian, Y.; Zhang, B. Pharmacology & Therapeutics New drug discovery and development from natural products: Advances and strategies. Pharmacol. Ther. 2024, 264, 108752. [Google Scholar] [PubMed]
- Srikrishna, D.; Godugu, C.; Dubey, P.K. A review on pharmacological properties of coumarins. Mini Rev. Med. Chem. 2018, 18, 113–141. [Google Scholar] [CrossRef]
- Qin, H.L.; Zhang, Z.W.; Ravindar, L.; Rakesh, K.P. Antibacterial activities with the structure-activity relationship of coumarin derivatives. Eur. J. Med. Chem. 2020, 207, 112832. [Google Scholar] [CrossRef]
- Sankar, J.; Awanish, P. Coumarins: Antifungal effectiveness and future therapeutic scope. Mol. Divers. 2020, 24, 1367–1383. [Google Scholar]
- Valencia, S.; Quiñones, W.; Robledo, S.; Durango, D. Antiparasitic Activity of Coumarin—Chalcone (3-Cinnamoyl-2H-Chromen-2-Ones) Hybrids. Chem. Biodivers. 2025, 22, e202402515. [Google Scholar] [CrossRef]
- Martinez-fierro, M.L. Therapeutic Effects of Coumarins with Different Substitution Patterns. Molecules 2023, 28, 2413. [Google Scholar] [CrossRef]
- Annunziata, F.; Pinna, C.; Dallavalle, S.; Tamborini, L.; Pinto, A. An overview of coumarin as a versatile and readily accessible scaffold with broad-ranging biological activities. Int. J. Mol. Sci. 2020, 21, 4618. [Google Scholar] [CrossRef] [PubMed]
- Sajjadi, S.E.; Eskandarian, A.A.; Shokoohinia, Y.; Yousefi, H.A.; Mansourian, M.; Asgarian-Nasab, H.; Mohseni, N. Antileishmanial activity of prenylated coumarins isolated from Ferulago angulata and Prangos asperula. Res. Pharm. Sci. 2016, 11, 324–331. [Google Scholar] [CrossRef]
- De Figueiredo, E.; Rafaella, P.; Merli, J.; Ferreira, P.; Juliana, E.; Nunes, B. en-2-one as a promising coumarin compound for the development of a new and orally effective antileishmanial agent. Mol. Biol. Rep. 2020, 47, 8465–8474. [Google Scholar] [CrossRef] [PubMed]
- Khatoon, S.; Aroosh, A.; Islam, A.; Kalsoom, S.; Ahmad, F. Bioorganic Chemistry Novel coumarin-isatin hybrids as potent antileishmanial agents: Synthesis, in silico and in vitro evaluations. Bioorg. Chem. 2021, 110, 104816. [Google Scholar] [CrossRef]
- Hassan, N.W.; Sabt, A.; El-attar, M.A.Z.; Ora, M.; Bekhit, A.E.A.; Amagase, K.; Bekhit, A.A.; Belal, A.S.F.; Elzahhar, P.A. Modulating leishmanial pteridine metabolism machinery via some new coumarin-1,2,3-triazoles: Design, synthesis and computational studies. Eur. J. Med. Chem. 2023, 253, 115333. [Google Scholar] [CrossRef]
- Silva, A.C.; De Moraes, D.C.; Costa, D.; Cristina, G.; Gomes, C.; Ganesan, A.; Lopes, R.; Lopes, C.; Ferreira-Pereira, A. Synthesis of Altissimacoumarin D and Other Prenylated Coumarins and Their Ability to Reverse the Multidrug Resistance Phenotype in Candida albicans. J Fungi 2023, 9, 758. [Google Scholar] [CrossRef]
- Souza, R.M.d.; Tuon, F.F.; Lindoso, J.A.L.; Viana, J.V.M.; Maia, I.A.; Sampaio, R.N.R.; Amato, V.S. Cutaneous and Mucocutaneous Leishmaniasis: Perspectives on Immunity, Virulence, and Treatment. Biomedicines 2025, 13, 3008. [Google Scholar] [CrossRef] [PubMed]
- Napolitano, H.B.; Silva, M.; Ellena, J.; Rodrigues, B.D.; Almeida, A.L.; Vieira, P.C.; Oliva, G.; Thiemann, O.H. Aurapten, a coumarin with growth inhibition against Leishmania major promastigotes. Braz. J. Med. Biol. Res. 2004, 37, 1847–1852. [Google Scholar] [CrossRef] [PubMed]
- Tariq, H.; Khan, S.; Miyan, K.; Qidwai, S.N.; Ahamad, S.; Saquib, M.; Hussain, M.K. Exploring Natural Coumarins in Antiprotozoal Drug Discovery: A Comprehensive Review. Chem. Biodivers. 2025, 22, e01964. [Google Scholar] [CrossRef]
- Vakili, T.; Iranshahi, M.; Arab, H.; Riahi, B.; Roshan, N.M.; Karimi, G. Safety evaluation of auraptene in rats in acute and subacute toxicity studies. Regul. Toxicol. Pharmacol. 2017, 91, 159–164. [Google Scholar] [CrossRef]
- Mennes, W.C.; Luijckx, N.B.; Wortelboer, H.M.; Noordhoek, J.; Blaauboer, B.J. Differences in the effects of model inducers of cytochrome P450 on the biotransformation of scoparone in rat and hamster liver. Arch. Toxicol. 1993, 67, 92–97. [Google Scholar] [CrossRef]
- Tomiotto-Pellissier, F.; Miranda-Sapla, M.M.; Silva, T.F.; Bortoleti, B.T.D.S.; Gonçalves, M.D.; Concato, V.M.; Rodrigues, A.C.J.; Detoni, M.B.; Costa, I.N.; Panis, C.; et al. Murine Susceptibility to Leishmania amazonensis Infection Is Influenced by Arginase-1 and Macrophages at the Lesion Site. Front. Cell Infect. Microbiol. 2021, 11, 687633. [Google Scholar] [CrossRef]
- Gasparotto, J.; Kunzler, A.; Senger, M.R.; Souza, C.D.; Simone, S.G.; Bortolin, R.C.; Somensi, N.; Dal-Pizzol, F.; Moreira, J.C.; Abreu-Silva, A.L.; et al. N-acetyl-cysteine inhibits liver oxidative stress markers in BALB/c mice infected with Leishmania amazonensis. Mem. Inst. Oswaldo Cruz 2017, 112, 146–154. [Google Scholar] [CrossRef]
- Ou, R.; Huang, S.; Ma, L.; Zhao, Z.; Liu, S.; Wang, Y.; Sun, Y.; Xu, N.; Zhou, L.; Li, M.; et al. Mechanism of auraptene in improving acute liver injury induced by diquat poisoning in mice. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2025, 37, 590–594. [Google Scholar]
- Sangshetti, J.N.; Khan, F.A.K.; Kulkarni, A.A.; Patil, R.H.; Pachpinde, A.M.; Lohar, K.S.; Shinde, D.B. Antileishmanial activity of novel indolyl–coumarin hybrids: Design, synthesis, biological evaluation, molecular docking study and in silico ADME prediction. Bioorg Med. Chem. Lett. 2016, 26, 829–835. [Google Scholar] [CrossRef]
- Kumari, M.A.; Rao, C.V.; Triloknadh, S.; Harikrishna, N.; Venkataramaiah, C.; Rajendra, W.; Suneetha, Y. Synthesis, docking and ADME prediction of novel 1,2,3-triazole-tethered coumarin derivatives as potential neuroprotective agents. Res. Chem. Intermed. 2018, 44, 1989–2008. [Google Scholar] [CrossRef]
- Carter, N.S.; Kawasaki, Y.; Nahata, S.S.; Elikaee, S.; Rajab, S.; Salam, L.; Roberts, S.C. Polyamine metabolism in leishmania parasites: A promising therapeutic target. Med. Sci. 2022, 10, 24. [Google Scholar] [CrossRef] [PubMed]
- Vidya, V.M.; Dubey, V.K.; Ponnuraj, K. Identification of two natural compound inhibitors of Leishmania donovani Spermidine Synthase (SpdS) through molecular docking and dynamic studies. J. Biomol. Struct. Dyn. 2018, 36, 2678–2693. [Google Scholar]
- Bisceglia, J.A.; Mollo, M.C.; Gruber, N.; Orelli, L.R. Polyamines and related nitrogen compounds in the chemotherapy of neglected diseases caused by kinetoplastids. Curr. Top. Med. Chem. 2018, 18, 321–368. [Google Scholar] [CrossRef]
- Grover, A.; Katiyar, S.P.; Singh, S.K.; Dubey, V.K.; Sundar, D. A leishmaniasis study: Structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase. Biochim. Biophys. Acta 2012, 1824, 1476–1483. [Google Scholar] [CrossRef]
- Jain, S.; Sahu, U.; Kumar, A.; Khare, P. Metabolic pathways of Leishmania parasite: Source of pertinent drug targets and potent drug candidates. Pharmaceutics 2022, 14, 1590. [Google Scholar] [CrossRef]
- Vannier-Santos, M.A.; Menezes, D.; Oliveira, M.F.; de Mello, F.G. The putrescine analogue 1,4-diamino-2-butanone affects polyamine synthesis, transport, ultrastructure and intracellular survival in Leishmania amazonensis. Microbiology 2008, 154, 3104–3111. [Google Scholar] [CrossRef]
- Jagu, E.; Pomel, S.; Pethe, S.; Loiseau, P.M.; Labruère, R. Polyamine-based analogs and conjugates as antikinetoplastid agents. Eur. J. Med. Chem. 2017, 139, 982–1015. [Google Scholar] [CrossRef]
- Camargo, P.G.; Dos Santos, C.R.; Girão Albuquerque, M.; Rangel Rodrigues, C.; Lima, C.H.D.S. Py-CoMFA, docking, and molecular dynamics simulations of Leishmania (L.) amazonensis arginase inhibitors. Sci. Rep. 2024, 14, 11575. [Google Scholar] [CrossRef]
- Arya, P.K.; Barik, K.; Singh, A.K.; Kumar, A. Molecular docking and simulation studies of medicinal plant phytochemicals with Leishmania donovani adenosylmethionine decarboxylase. J. Appl. Biol. Biotechnol. 2024, 12, 219–228. [Google Scholar] [CrossRef]
- Singh, S.; Sarma, S.; Katiyar, S.P.; Das, M.; Bhardwaj, R.; Sundar, D.; Dubey, V.K. Probing the molecular mechanism of hypericin-induced parasite death provides insight into the role of spermidine beyond redox metabolism in Leishmania donovani. Antimicrob. Agents Chemother. 2015, 59, 15–24. [Google Scholar] [CrossRef] [PubMed]
- Challapa-Mamani, M.R.; Tomás-Alvarado, E.; Espinoza-Baigorria, A.; León-Figueroa, D.A.; Sah, R.; Rodriguez-Morales, A.J.; Barboza, J.J. Molecular docking and molecular dynamics simulations in related to leishmania donovani: An update and literature review. Trop. Med. Infect. Dis. 2023, 8, 457. [Google Scholar] [CrossRef] [PubMed]
- Yoshino, R.; Yasuo, N.; Hagiwara, Y.; Ishida, T.; Inaoka, D.K.; Amano, Y.; Sekijima, M. In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease. Sci. Rep. 2017, 7, 6666. [Google Scholar] [CrossRef]
- Yoshino, R.; Yasuo, N.; Hagiwara, Y.; Ishida, T.; Inaoka, D.K.; Amano, Y.; Sekijima, M. Discovery of a hidden trypanosoma cruzi spermidine synthase binding site and inhibitors through in silico, in vitro, and X-ray crystallography. ACS Omega 2023, 8, 25850–25860. [Google Scholar] [CrossRef] [PubMed]
- Castro, G.M.M.; Fontes, Y.S.; Alves, L.G.; Augusto, L.; Barbosa, O.; Ferreira, J.A.; Villar, P.; Filho, A.G.; Seabra, S.H.; Barreto, A.L.S.; et al. In vitro and in vivo effects of digoxin derivatives on promastigotes and amastigotes of Leishmania infantum. J. Taibah Univ. Sci. 2024, 18, 3655. [Google Scholar]
- Calabrese, K.S.; Cortada, V.M.C.L.; Dorval, M.E.C.; Lima, M.A.A.S.; Oshiro, E.T.; Souza, C.S.F.; Silva-Almeida, M.; Carvalho, L.O.P.; Gonçalves, S.C.; Abreu-Silva, A.L. Leishmania (Leishmania ) infantum/chagasi: Histopathological aspects of the skin in naturally infected dogs in two endemic areas. Exp. Parasitol. 2010, 124, 253–257. [Google Scholar] [CrossRef]
- Daina, A.; Michielin, O.; Zoete, V. iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. J. Chem. Inf. Model. 2014, 54, 3284–3301. [Google Scholar] [CrossRef]
- Daina, A.; Zoete, V. A boiled-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 2016, 11, 1117–1121. [Google Scholar] [CrossRef]
- Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef]
- UniProt Consortium. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 2019, 47, D506–D515. [Google Scholar] [CrossRef]
- Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596, 583–589. [Google Scholar] [CrossRef] [PubMed]
- Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [PubMed]
- Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model. 2021, 61, 3891–3898. [Google Scholar] [CrossRef] [PubMed]
- Hocquet, A.; Langgård, M. An evaluation of the MM+ force field. J. Mol. Med. 1998, 4, 94–112. [Google Scholar] [CrossRef]
- Chidambaram, S.K.; Ali, D.; Alarifi, S.; Radhakrishnan, S.; Akbar, I. In silico molecular docking: Evaluation of coumarin based derivatives against SARS-CoV-2. J. Infect. Public Health 2020, 13, 1671–1677. [Google Scholar] [CrossRef]
- Korkmaz, A. Synthesis, characterization, ADMET prediction, and molecular docking studies of novel coumarin sulfonate derivatives. J. Inst. Sci. Technol. 2022, 12, 918–932. [Google Scholar] [CrossRef]
- DeLano, W.L. Pymol: An open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr. 2002, 40, 82–92. [Google Scholar]
- Dassault Systèmes BIOVIA. BIOVIA Discovery Studio Modeling Environment (Release 2024) [Software]; Dassault Systèmes: San Diego, CA, USA, 2024. [Google Scholar]
- Aslett, M.; Aurrecoechea, C.; Berriman, M.; Brestelli, J.; Brunk, B.P.; Carrington, M.; Wang, H. TriTrypDB: A functional genomic resource for the Trypanosomatidae. Nucleic Acids Res. 2010, 38, D457–D462. [Google Scholar] [CrossRef] [PubMed]
- Madeo, F.; Eisenberg, T.; Pietrocola, F.; Kroemer, G. Spermidine in health and disease. Science 2018, 359, eaan2788. [Google Scholar] [CrossRef]
- O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminform. 2011, 3, 33. [Google Scholar] [CrossRef]
- Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802. [Google Scholar] [CrossRef]
- Phillips, J.C.; Hardy, D.J.; Maia, J.D.; Stone, J.E.; Ribeiro, J.V.; Bernardi, R.C.; Tajkhorshid, E. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 2020, 153, 044130. [Google Scholar] [CrossRef]
- Vanommeslaeghe, K.; MacKerell, A.D., Jr. Automation of the CHARMM General Force Field (CGenFF) I: Bond perception and atom typing. J. Chem. Inf. Model. 2012, 52, 3144–3154. [Google Scholar] [CrossRef] [PubMed]
- Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38. [Google Scholar] [CrossRef]
- Huang, J.; MacKerell, A.D., Jr. CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 2013, 34, 2135–2145. [Google Scholar] [CrossRef]
- Mayne, C.G.; Saam, J.; Schulten, K.; Tajkhorshid, E.; Gumbart, J.C. Rapid parameterization of small molecules using the force field toolkit. J. Comput. Chem. 2013, 34, 2757–2770. [Google Scholar] [CrossRef]
- Wu, E.L.; Cheng, X.; Jo, S.; Rui, H.; Song, K.C.; Dávila-Contreras, E.M.; Im, W. CHARMM-GUI membrane builder toward realistic biological membrane simulations. J. Comput. Chem. 2014, 35, 1997–2004. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.; Cheng, X.; Jo, S.; MacKerell, A.D.; Klauda, J.B.; Im, W. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 2016, 110, 641a. [Google Scholar]
- Hurley, M.M.; Small, M.C. In Silico Analysis of Peptide Macrocycle–Protein Interactions. Methods Mol. Biol. 2022, 2371, 317–334. [Google Scholar]
- Licari, G.; Dehghani-Ghahnaviyeh, S.; Tajkhorshid, E. Membrane mixer: A toolkit for efficient shuffling of lipids in heterogeneous biological membranes. J. Chem. Inf. Model. 2022, 62, 986–996. [Google Scholar] [CrossRef]
- Izaguirre, J.A.; Catarello, D.P.; Wozniak, J.M.; Skeel, R.D. Langevin stabilization of molecular dynamics. J. Chem. Phys. 2001, 114, 2090–2098. [Google Scholar] [CrossRef]
- Gordon, D.; Krishnamurthy, V.; Chung, S.H. Generalized Langevin models of molecular dynamics simulations with applications to ion channels. J. Chem. Phys. 2009, 131, 134102. [Google Scholar] [CrossRef]
- Darden, T.; Perera, L.; Li, L.; Pedersen, L. New tricks for modelers from the crystallography toolkit: The particle mesh Ewald algorithm and its use in nucleic acid simulations. Structure 1999, 7, R55–R60. [Google Scholar] [CrossRef] [PubMed]
- Simmonett, A.C.; Brooks, B.R. A compression strategy for particle mesh Ewald theory. J. Chem. Phys. 2021, 154, 054112. [Google Scholar] [CrossRef]
- Gowers, R.J.; Linke, M.; Barnoud, J.; Reddy, T.J.E.; Melo, M.N.; Seyler, S.L.; Beckstein, O. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations (No. LA-UR-19-29136); Los Alamos National Laboratory (LANL): Los Alamos, NM, USA, 2019. [Google Scholar]
- Bressert, E. SciPy and NumPy: An Overview for Developers; O’Reilly Media: Sebastopol, CA, USA, 2012. [Google Scholar]
- McKinney, W. pandas: A foundational Python library for data analysis and statistics. Python High Perform. Sci. Comput. 2011, 14, 1–9. [Google Scholar]
- Chapman, B.; Chang, J. Biopython: Python tools for computational biology. Sigbio Newsl. 2000, 20, 15–19. [Google Scholar] [CrossRef]
- Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007, 9, 90–95. [Google Scholar] [CrossRef]
- Liu, H.; Hou, T. CaFE: A tool for binding affinity prediction using end-point free energy methods. Bioinformatics 2016, 32, 2216–2218. [Google Scholar] [CrossRef] [PubMed]
- Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert. Opin. Drug Discov. 2015, 10, 449–461. [Google Scholar]
- Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu, H.; Zhang, J.Z.; Hou, T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev. 2019, 119, 9478–9508. [Google Scholar] [CrossRef]
- Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef]
- Tamura, K.; Stecher, G.; Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 2021, 38, 3022–3027. [Google Scholar] [CrossRef] [PubMed]
- Mahram, A.; Herbordt, M.C. NCBI BLASTP on high-performance reconfigurable computing systems. ACM Trans. Reconfigurable Technol. Syst. 2015, 7, 1–20. [Google Scholar] [CrossRef]
- Gilroy, C.; Olenyik, T.; Roberts, S.C.; Ullman, B. Spermidine synthase is required for virulence of Leishmania donovani. Infect. Immun. 2011, 79, 2764–2769. [Google Scholar] [CrossRef] [PubMed]
- Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605–1612. [Google Scholar] [CrossRef]
- Song, D.; Chen, J.; Chen, G.; Li, N.; Li, J.; Fan, J.; Li, S.C. Parameterized BLOSUM matrices for protein alignment. IEEE/ACM Trans. Comput. Biol. Bioinform. 2014, 12, 686–694. [Google Scholar] [CrossRef]
- Meng, E.C.; Pettersen, E.F.; Couch, G.S.; Huang, C.C.; Ferrin, T.E. Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinform. 2006, 7, 339. [Google Scholar] [CrossRef]
- Benkert, P.; Künzli, M.; Schwede, T. QMEAN server for protein model quality estimation. Nucleic Acids Res. 2009, 37, W510–W514. [Google Scholar] [CrossRef] [PubMed]
- Abramson, J.; Adler, J.; Dunger, J.; Evans, R.; Green, T.; Pritzel, A.; Ronneberger, O.; Willmore, L.; Ballard, A.J.; Bambrick, J.; et al. Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3. Nature 2024, 630, 493–500. [Google Scholar] [CrossRef] [PubMed]
- Laskowski, R.A.; MacArthur, M.W.; Thornton, J.M. PROCHECK: Validation of protein-structure coordinates. Int. Tables Crystallogr. 2006, F. ch. 25.2, 722–725. [Google Scholar]
- Chen, V.B.; Arendall, W.B.; Headd, J.J.; Keedy, D.A.; Immormino, R.M.; Kapral, G.J.; Richardson, D.C. MolProbity: All-atom structure validation for macromolecular crystallography. Biol. Crystallogr. 2010, 66, 12–21. [Google Scholar] [CrossRef]
- Unni, S.; Huang, Y.; Hanson, R.M.; Tobias, M.; Krishnan, S.; Li, W.W.; Baker, N.A. Web servers and services for electrostatics calculations with APBS and PDB2PQR. J. Comput. Chem. 2011, 32, 1488–1491. [Google Scholar] [CrossRef]
- Case, D.A.; Aktulga, H.M.; Belfon, K.; Cerutti, D.S.; Cisneros, G.A.; Cruzeiro, V.W.D.; Merz, K.M., Jr. AmberTools. J. Chem. Inf. Model. 2023, 63, 6183–6191. [Google Scholar] [CrossRef]
- Case, D.A.; Cerutti, D.S.; Cruzeiro, V.W.D.; Darden, T.A.; Duke, R.E.; Ghazimirsaeed, M.; Merz, K.M., Jr. Recent developments in Amber biomolecular simulations. J. Chem. Inf. Model. 2025, 65, 7835–7843. [Google Scholar] [CrossRef]
- Zhao, G.; London, E. Strong correlation between statistical transmembrane tendency and experimental hydrophobicity scales for identification of transmembrane helices. J. Membr. Biol. 2009, 229, 165–168. [Google Scholar] [CrossRef]
- Høie, M.H.; Kiehl, E.N.; Petersen, B.; Nielsen, M.; Winther, O.; Nielsen, H.; Marcatili, P. NetSurfP-3.0: Accurate and fast prediction of protein structural features by protein language models and deep learning. Nucleic Acids Res. 2022, 50, W510–W515. [Google Scholar] [CrossRef] [PubMed]
- Mistry, J.; Chuguransky, S.; Williams, L.; Qureshi, M.; Salazar, G.A.; Sonnhammer, E.L.; Bateman, A. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021, 49, D412–D419. [Google Scholar] [CrossRef]
- Gupta, A.; Zhou, H.X. Machine learning-enabled pipeline for large-scale virtual drug screening. J. Chem. Inf. Model. 2021, 61, 4236–4244. [Google Scholar] [PubMed]
- Noor, F.; Junaid, M.; Almalki, A.H.; Almaghrabi, M.; Ghazanfar, S.; Tahir ul Qamar, M. Deep learning pipeline for accelerating virtual screening in drug discovery. Sci. Rep. 2024, 14, 28321. [Google Scholar] [CrossRef]











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Nico, D.; de Moraes, D.C.; Silva, A.C.; Taveira, I.N.; Fontes, Y.d.S.; Lopes, R.S.C.; Lopes, C.C.; Ferreira-Pereira, A. 7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation Against Leishmania amazonensis. Pharmaceuticals 2026, 19, 426. https://doi.org/10.3390/ph19030426
Nico D, de Moraes DC, Silva AC, Taveira IN, Fontes YdS, Lopes RSC, Lopes CC, Ferreira-Pereira A. 7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation Against Leishmania amazonensis. Pharmaceuticals. 2026; 19(3):426. https://doi.org/10.3390/ph19030426
Chicago/Turabian StyleNico, Dirlei, Daniel Clemente de Moraes, Anna Claudia Silva, Igor Nunes Taveira, Yasmin da Silva Fontes, Rosangela Sabbatini Capella Lopes, Cláudio Cerqueira Lopes, and Antonio Ferreira-Pereira. 2026. "7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation Against Leishmania amazonensis" Pharmaceuticals 19, no. 3: 426. https://doi.org/10.3390/ph19030426
APA StyleNico, D., de Moraes, D. C., Silva, A. C., Taveira, I. N., Fontes, Y. d. S., Lopes, R. S. C., Lopes, C. C., & Ferreira-Pereira, A. (2026). 7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation Against Leishmania amazonensis. Pharmaceuticals, 19(3), 426. https://doi.org/10.3390/ph19030426

