Keywords
β-1,3-glucan synthase
Drug repurposing
Molecular dynamics
Antifungal agents
How to Cite
Copyright © | All rights reserved.
The journal holds the exclusive copyright for the publication of this article under the terms of Brazilian law 9610/98.
Partial reproduction
The use of parts of the texts, figures and questionnaire of the article is free, being mandatory the citation of the authors and journal.
Total reproduction
It is expressly prohibited and must be authorized by the journal.
Abstract
Objective: Identify potential inhibitors of the Candida albicansβ-1,3-glucan synthase enzyme through in silico approaches, aiming at the repurposing of FDA-approved drugs as an alternative for the development of new antifungal agents. Methods: Homology modeling of the enzyme was performed based on the structure of Saccharomycescerevisiae, with 93.45% of residues located in favorable regions of the Ramachandran plot. Virtual screening was conducted on the MTi Open Screen server using the Drugs-lib library, followed by molecular docking and 100 ns molecular dynamics simulations with the AMBER 2024 software. Binding free energies were calculated using the MMGBSA and MMPBSA methods. Results: Among the evaluated compounds, Elbasvir exhibited the most favorable binding energy (–62.4 kcal/mol), conformational stability, and consistent interactions with active site residues of the enzyme, standing out as the best candidate. Itraconazole also showed good affinity and a possible dual mechanism of action, acting on both lanosterol14α-demethylase and β-1,3-glucan synthase. Conclusion: The study confirmed the potential of drug repurposing as an effective strategy for discovering new antifungal agents. Elbasvir emerged as a strong candidate for experimental validation and potential therapeutic use against C. albicans.
References
ANANDAKRISHNAN R, et al. H++ 3.0: automation of pK prediction and preparation of biomolecular structures for molecular modeling and atomistic simulations. NucleicAcids Res. 2012;40(Web Server issue):W537–41.
BARDI T, et al. COVID-19-associated nosocomial infections in the intensive care unit: clinical characteristics and outcomes. Eur J ClinMicrobiolInfectDis. 2021;40(3):495–502.
BELL AM, et al. Elbasvir/Grazoprevir: a review of the latest agent in the fight against hepatitis C. Int J Hepatol.2016;2016:3852126.
Bienvenu AL, et al. Impact of COVID-19 pandemic on antifungal consumption: a multicenter retrospective analysis. Crit Care. 2022;26(1):
CADA DJ, et al. Elbasvir/Grazoprevir. HospPharm. 2016;51(8):665–86.
CASE DA, et al. AmberTools. J ChemInfModel. 2023;63(20):6183–91.
COSTA-DE-OLIVEIRA S, RODRIGUES AG. Antifungal resistance and tolerance of Candida albicans in bloodstream infections: the yeast-host-antifungal triad. Microorganisms. 2020;8(2):154.
D’ENFERT C, et al. The impact of fungus-host-microbiota interaction in Candida albicans infections: current knowledge and new perspectives. FEMS Microbiol Rev. 2021;45(3):fuaa060.
DICHTL K, et al. Signaling of cell wall integrity in human pathogenic fungi. Cell Microbiol. 2016;18(9):1228–38.
HAJ HÁ, et al. Virtual screening of azole libraries: the search for potential antimucormycotic agents using computational tools. NetwModel Anal Health InformBioinform. 2025;14(1):23.
HALL IH, et al. Effects of alatrofloxacin, the parent prodrug of trovafloxacin, on phagocytic, anti-inflammatory, and immunomodulatory events of human THP-1 monocytes. BiomedPharmacother. 2003;57(8):359–65.
HU X, et al. Structural and mechanistic insights into fungal β-1,3-glucan synthase FKS1. Nature. 2023;616(7955):190–8.
INCERT MK, et al. Repurposing drugs for COVID-19: an overview. Health (Santa Maria). 2022;E67893.
IZADI S, et al. Building water models: a different approach. J PhysChemLett. 2014;5(21):3863–71.
JEFFERY-SMITH A, et al. Candida auris: a review of the literature. ClinMicrobiol Rev. 2018;31(1):e00029–17.
KEATING GM. Elbasvir/Grazoprevir: first global approval. Drugs. 2016;76(5):617–24.
LEE Y, et al. Antifungal drug resistance: molecular mechanisms in Candida albicans and beyond. Chem Rev. 2021;121(6):3390–411.
LEE Y, et al. Molecular mechanisms governing antifungal drug resistance. NPJ Antimicrob Resist. 2023;1(1):5.
LOSCHWITZ J, et al. AMBER force field parameter dataset for drugs, natural products, and steroids for simulations using GROMACS. Data Brief. 2021;35:106948.
MEDEIROS CIS. Evaluation of the antifungal activity of linalool on Candida albicans from vulvovaginal secretions [Final Course Project – Biological Sciences]. João Pessoa: Federal University of Paraíba; 2023.
PARAMBATH S, et al. Candida albicans – a systematic review to inform the World Health Organization’s list of priority fungal pathogens. Med Mycol. 2024;62(6):myae045.
PYLKKÄNEN R, et al. Correction: synthesis of β-1,3-glucan, new supramolecular self-assembly, characterization, and application. Nanoscale. 2022;14(41):15542.
REBOUÇAS KL, et al. Recent trends in antifungal therapy: mechanisms of action and emerging challenges. Rev Foco. 2024;9:e6204.
ROCHA APS, et al. Epidemiological profile of systemic yeast infections in intensive care units of public hospitals in Recife, Brazil. Braz J Health Rev. 2020;6:19098–111.
ROSE GD. Ramachandran maps for side chains in globular proteins. Proteins. 2019;87(5):357–64.
SALOMON-FERRER R, et al. An overview of the Amber biomolecular simulation package. WIREs Comput Mol Sci. 2013;3(2):198–210.
SHAFIEI M, et al. History of the development of antifungalazoles: a review of structures, SAR, and mechanism of action. Bioorg Chem. 2020;104:104240.
SILVA JF. Metabolic and structural targets of Candida albicans: production of support for targeted growth of Candida albicans [Final Course Project – Biological Sciences: Biotechnology]. Duque de Caxias: Federal University of Rio de Janeiro; 2023.
SINGH S, et al. Review on analytical methods for estimation of itraconazole in bulk and pharmaceutical dosage form. Int J Res Rev. 2021;8(5):30–7.
TIAN C, et al. Ff19SB: amino acid-specific protein structure parameters trained against quantum mechanical energy surfaces in solution. J ChemTheoryComput. 2020;16(1):528–52.
WAGENER J, et al. Synthesis and remodeling of α- and β-1,3-glucan. Curr Top MicrobiolImmunol. 2020;425:53–82.
WANG C, et al. Recent developments and applications of the MMPBSA method. Front Mol Biosci. 2018;4:87.
WAYMENT-STEELE HK, et al. Deep learning models to predict RNA degradation via double crowdsourcing. Nat Mach Intell. 2022;4(12):1174–84.
ZHAO CR, et al. Structure of a fungal 1,3-β-glucan synthase. Sci Adv. 2023;9(37):eadh7820.