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Natural Isatin Derivatives Against Black Fungus: In Silico Studies

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Abstract

During this coronavirus pandemic, when a lot of people are already severely afflicted with SARS-CoV-19, the dispersion of black fungus is making it worse, especially in the Indian subcontinent. Considering this situation, the idea for an in silico study to identify the potential inhibitor against black fungal infection is envisioned and computational analysis has been conducted with isatin derivatives that exhibit considerable antifungal activity. Through this in silico study, several pharmacokinetics properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) are estimated for various derivatives. Lipinski rules have been used to observe the drug likeliness property, and to study the electronic properties of the molecules, quantum mechanism was analyzed using the density functional theory (DFT). After applying molecular docking of the isatin derivatives with sterol 14-alpha demethylase enzyme of black fungus, a far higher docking affinity score has been observed for the isatin sulfonamide-34 (derivative 1) than the standard fluconazole. Lastly, molecular dynamic (MD) simulation has been performed for 100 ns to examine the stability of the proposed drug complex by estimating Root Mean Square Deviation (RMSD), Radius of gyration (Rg), Solvent accessible surface area (SASA), Root Mean Square Fluctuation (RMSF), as well as hydrogen bond. Listed ligands have precisely satisfied every pharmacokinetics requirement for a qualified drug candidate and they are non-toxic, non-carcinogenic, and have high stability. This natural molecule known as isatin derivative 1 has shown the potential of being a drug for fungal treatment. However, the impact of the chemicals on living cells requires more investigation and research.

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References

  1. Bai Y et al (2023) Study on the COVID-19 epidemic in mainland China between November 2022 and January 2023, with prediction of its tendency. Journal of Biosafety and Biosecurity 5(1):39–44

    Article  PubMed  PubMed Central  Google Scholar 

  2. Andre FE et al (2008) Vaccination greatly reduces disease, disability, death and inequity worldwide. Bull World Health Organ 86:140–146

    Article  CAS  PubMed  Google Scholar 

  3. Al-Tawfiq JA et al (2021) COVID-19 and mucormycosis superinfection: the perfect storm. Infection 49:833–853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Soni S, NamdeoPudake R, Jain U, Chauhan N (2022) A systematic review on SARS-CoV-2-associated fungal coinfections. J Med Virol 94(1):99–109

    Article  CAS  PubMed  Google Scholar 

  5. Garg D et al (2021) Coronavirus disease (Covid-19) associated mucormycosis (CAM): case report and systematic review of literature. Mycopathologia 186:289–298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bari MS, Hossain MJ, Akhter S, Emran TB (2021) Delta variant and black fungal invasion: a bidirectional assault might worsen the massive second/third stream of COVID-19 outbreak in South-Asia. Ethics Med Public Health 19:100722

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chakrabarti A, Singh S (2020) Management of mucormycosis. Curr Fungal Infect Rep 14:348–360

    Article  Google Scholar 

  8. El-Kholy NA, El-Fattah AMA, Khafagy YW (2021) Invasive fungal sinusitis in post COVID-19 patients: a new clinical entity. Laryngoscope 131(12):2652–2658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kumer A, Chakma U, Matin MM, Akash S, Chando A, Howlader D (2021) The computational screening of inhibitor for black fungus and white fungus by D-glucofuranose derivatives using in silico and SAR study. Org Commun 14(4):305–322

    Article  CAS  Google Scholar 

  10. Rahman FI, Islam MR, Bhuiyan MA (2021) Mucormycosis or black fungus infection is a new scare in South Asian countries during the COVID-19 pandemic: associated risk factors and preventive measures. J Med Virol 93(12):6447

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Elfiky AA (2019) The antiviral Sofosbuvir against mucormycosis: an in silico perspective. Futur Virol 14(11):739–744

    Article  CAS  Google Scholar 

  12. Cornely OA et al (2019) Global guideline for the diagnosis and management of mucormycosis: an initiative of the European Confederation of Medical Mycology in cooperation with the Mycoses Study Group Education and Research Consortium. Lancet Infect Dis 19(12):e405–e421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. DeShazo RD, Chapin K, Swain RE (1997) Fungal sinusitis. N Engl J Med 337(4):254–259

    Article  CAS  PubMed  Google Scholar 

  14. Gambhir RS, Aggarwal A, Bhardwaj A, Kaur A, Sohi RK, Mehta S (2021) COVID-19 and mucormycosis (black fungus): an epidemic within the pandemic. Roczniki Państwowego Zakładu Higieny 72(3):239–244

    CAS  PubMed  Google Scholar 

  15. Al-Aboody MS, Mickymaray S (2020) Anti-fungal efficacy and mechanisms of flavonoids. Antibiotics 9(2):45

    Article  CAS  PubMed Central  Google Scholar 

  16. Riley TT, Muzny CA, Swiatlo E, Legendre DP (2016) Breaking the mold: a review of mucormycosis and current pharmacological treatment options. Ann Pharmacother 50(9):747–757

    Article  CAS  PubMed  Google Scholar 

  17. Van Daele R et al (2019) Antifungal drugs: what brings the future? Med Mycol 57(3):S328–S343

    Article  PubMed  Google Scholar 

  18. Schwarz P, Cornely OA, Dannaoui E (2019) Antifungal combinations in Mucorales: a microbiological perspective. Mycoses 62(9):746–760

    Article  PubMed  Google Scholar 

  19. Ryder N (1992) Terbinafine: mode of action and properties of the squalene epoxidase inhibition. Br J Dermatol 126(s39):2–7

    Article  PubMed  Google Scholar 

  20. Berger S, El Chazli Y, Babu AF, Coste AT (2017) Azole resistance in Aspergillus fumigatus: a consequence of antifungal use in agriculture? Front Microbiol 8:1024

    Article  PubMed  PubMed Central  Google Scholar 

  21. E. E. Ashu et al. (2018) Widespread amphotericin B-resistant strains of Aspergillus fumigatus in Hamilton, Canada. Infect Drug Resistance:1549–1555

  22. Desai A, Kovanda L, Kowalski D, Lu Q, Townsend R, Bonate PL (2016) Population pharmacokinetics of isavuconazole from phase 1 and phase 3 (SECURE) trials in adults and target attainment in patients with invasive infections due to Aspergillus and other filamentous fungi. Antimicrob Agents Chemother 60(9):5483–5491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Neese F (2018) Software update: the ORCA program system, version 4.0. Wiley Interdisc Rev Comput Mol Sci 8(1):e1327

    Article  Google Scholar 

  24. Neese F, Wennmohs F, Becker U, Riplinger C (2020) The ORCA quantum chemistry program package. J Chem Phys. https://doi.org/10.1063/5.0004608

    Article  PubMed  Google Scholar 

  25. Zhao YH et al (2002) Erratum: evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure–activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 91(2):605

    Article  CAS  Google Scholar 

  26. Dong J et al (2015) ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminf 7(1):1–10

    Article  Google Scholar 

  27. Rao VU, Arakeri G, Madikeri G, Shah A, Oeppen RS, Brennan PA (2021) COVID-19 associated mucormycosis (CAM) in India: a formidable challenge. Br J Oral Maxillofac Surg 59(9):1095–1098

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ke S, Shi L, Yang Z (2015) Discovery of novel isatin–dehydroepiandrosterone conjugates as potential anticancer agents. Bioorg Med Chem Lett 25(20):4628–4631

    Article  CAS  PubMed  Google Scholar 

  29. Eldeeb M et al (2022) Anticancer effects with molecular docking confirmation of newly synthesized isatin sulfonamide molecular hybrid derivatives against hepatic cancer cell lines. Biomedicines 10(3):722

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Salem MA, Ragab A, El-Khalafawy A, Makhlouf AH, Askar AA, Ammar YA (2020) Design, synthesis, in vitro antimicrobial evaluation and molecular docking studies of indol-2-one tagged with morpholinosulfonyl moiety as DNA gyrase inhibitors. Bioorg Chem 96:103619

    Article  PubMed  Google Scholar 

  31. Nath R, Pathania S, Grover G, Akhtar MJ (2020) Isatin containing heterocycles for different biological activities: analysis of structure activity relationship. J Mol Struct 1222:128900

    Article  CAS  Google Scholar 

  32. Wang J et al (2018) Design, synthesis and QSAR study of novel isatin analogues inspired Michael acceptor as potential anticancer compounds. Eur J Med Chem 144:493–503

    Article  CAS  PubMed  Google Scholar 

  33. Gupta AK, Tulsyan S, Bharadwaj M, Mehrotra R (2019) Systematic review on cytotoxic and anticancer potential of n-substituted isatins as novel class of compounds useful in multidrug-resistant cancer therapy: In silico and in vitro analysis. Top Curr Chem 377:1–21

    Google Scholar 

  34. Chu W, Rothfuss J, Chu Y, Zhou D, Mach RH (2009) Synthesis and in vitro evaluation of sulfonamide isatin Michael acceptors as small molecule inhibitors of caspase-6. J Med Chem 52(8):2188–2191

    Article  CAS  PubMed  Google Scholar 

  35. Shaldam MA et al (2023) Discovery of sulfonamide-tethered isatin derivatives as novel anticancer agents and VEGFR-2 inhibitors. J Enzyme Inhib Med Chem 38(1):2203389

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bienvenu AV, Knizia G (2018) Efficient treatment of local meta-generalized gradient density functionals via auxiliary density expansion: the density fitting J+ X approximation. J Chem Theory Comput 14(3):1297–1303

    Article  CAS  PubMed  Google Scholar 

  37. Nandi S, Kumar M, Saxena AK (2024) QSAR of SARS-CoV-2 main protease inhibitors utilizing theoretical molecular descriptors. Lett Drug Des Discovery 21(1):116–132

    Article  CAS  Google Scholar 

  38. Siddikey F, Roni M, Kumer A, Chakma U, Matin M (2022) Computational investigation of Betalain derivatives as natural inhibitor against food borne bacteria. Curr Chem Lett 11(3):309–320

    Article  Google Scholar 

  39. Richel A, Laurent P, Wathelet B, Wathelet J-P, Paquot M (2011) Microwave-assisted conversion of carbohydrates: state of the art and outlook. C R Chim 14(2–3):224–234

    CAS  Google Scholar 

  40. Kumer A et al (2022) Modified D-glucofuranose computationally screening for inhibitor of breast cancer and triple breast cancer: chemical descriptor, molecular docking, molecular dynamics and QSAR. J Chil Chem Soc 67(3):5623–5635

    Article  CAS  Google Scholar 

  41. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis 18(15):2714–2723

    Article  CAS  PubMed  Google Scholar 

  42. Hargrove TY et al (2017) Structural analyses of Candida albicans sterol 14α-demethylase complexed with azole drugs address the molecular basis of azole-mediated inhibition of fungal sterol biosynthesis. J Biol Chem 292(16):6728–6743

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rappé AK, Casewit CJ, Colwell K, Goddard WA III, Skiff WM (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114(25):10024–10035

    Article  Google Scholar 

  45. A. S. Grewal and A. SinghGrewal. Isatin derivatives with several biological activities discovery of novel agents for diabetes mellitus and metabolic syndrome view project comabting antimicrobial resistance view project isatin derivatives with several biological activities. Accessed 22 Jul 2023

  46. Li AP (2001) Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today 6(7):357–366

    Article  CAS  PubMed  Google Scholar 

  47. Walters WP, Murcko MA (2002) Prediction of ‘drug-likeness.’ Adv Drug Deliv Rev 54(3):255–271

    Article  CAS  PubMed  Google Scholar 

  48. Pires DE, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58(9):4066–4072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Chen X, Li H, Tian L, Li Q, Luo J, Zhang Y (2020) Analysis of the physicochemical properties of acaricides based on Lipinski’s rule of five. J Comput Biol 27(9):1397–1406

    Article  CAS  PubMed  Google Scholar 

  50. Walters WP, Murcko AA, Murcko MA (1999) Recognizing molecules with drug-like properties. Curr Opin Chem Biol 3(4):384–387

    Article  CAS  PubMed  Google Scholar 

  51. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717

    Article  PubMed  PubMed Central  Google Scholar 

  52. Tikhonov DS, Vishnevskiy YV (2023) Describing nuclear quantum effects in vibrational properties using molecular dynamics with Wigner sampling. Phys Chem Chem Phys 25(27):18406–18423

    Article  CAS  PubMed  Google Scholar 

  53. Pronk S et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7):845–854

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Best RB et al (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 8(9):3257–3273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Elkwafi G, Mohamed N, Elabbar F, Alnajjar R (2022) Flavonoid content of the Libyan Onosma Cyrenaicum: isolation, identification, electronic chemical reactivity, drug likeness, docking, and MD study. J Biomol Struct Dyn 40(16):7351–7366

    Article  CAS  PubMed  Google Scholar 

  56. Akash S et al (2023) Target specific inhibition of West Nile virus envelope glycoprotein and methyltransferase using phytocompounds: an in silico strategy leveraging molecular docking and dynamics simulation. Front Microbiol. https://doi.org/10.3389/fmicb.2023.1189786

    Article  PubMed  PubMed Central  Google Scholar 

  57. Walters WP (2012) Going further than Lipinski’s rule in drug design. Expert Opin Drug Discov 7(2):99–107

    Article  CAS  PubMed  Google Scholar 

  58. Ballester PJ, Mitchell JB (2010) A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics 26(9):1169–1175

    Article  CAS  PubMed  Google Scholar 

  59. Kawsar S, Kumer A (2021) Computational investigation of methyl α-D-glucopyranoside derivatives as inhibitor against bacteria, fungi and COVID-19 (SARS-2). J Chil Chem Soc 66(2):5206–5214

    Article  CAS  Google Scholar 

  60. Hoque MM, Hussen MS, Kumer A, Khan MW (2020) Synthesis of 5, 6-diaroylisoindoline-1, 3-dione and computational approaches for investigation on structural and mechanistic insights by DFT. Mol Simul 46(16):1298–1307

    Article  CAS  Google Scholar 

  61. Kumer A et al (2022) Investigation of the new inhibitors by sulfadiazine and modified derivatives of α-d-glucopyranoside for white spot syndrome virus disease of shrimp by in silico: quantum calculations, molecular docking, ADMET and molecular dynamics study. Molecules 27(12):3694

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hosen MA et al (2022) Synthesis, antimicrobial, molecular docking and molecular dynamics studies of lauroyl thymidine analogs against SARS-CoV-2: POM study and identification of the pharmacophore sites. Bioorg Chem 125:105850

    Article  Google Scholar 

  63. Lu L (2015) Can B3LYP be improved by optimization of the proportions of exchange and correlation functionals? Int J Quantum Chem 115(8):502–509

    Article  CAS  Google Scholar 

  64. Aihara J-I (1999) Reduced HOMO− LUMO gap as an index of kinetic stability for polycyclic aromatic hydrocarbons. J Phys Chem A 103(37):7487–7495

    Article  CAS  Google Scholar 

  65. Zannat A, Kumar A, Sarker MN, Sunanda P (2019) The substituent group activity in the anion of cholinium carboxylate ionic liquids on thermo-physical, chemical reactivity, and biological properties: a DFT study. Int J Chem Technol 3(2):151–161

    Article  Google Scholar 

  66. Kumer A, Paul S, Sarker MN, Islam MJ (2019) The prediction of thermo physical, vibrational spectroscopy, chemical reactivity, biological properties of morpholinium borate, phosphate, chloride and bromide ionic liquid: a DFT study. Int J New Chem 6(4):236–253

    CAS  Google Scholar 

  67. Kumer A, Jahidul M, Paul S (2020) Effect of external electric field and temperature on entropy, heat of capacity, and chemical reactivity with QSAR study of morphonium chloride and nitrous ionic liquids crystal using DFT. Chem Methodol 4:595–604

    CAS  Google Scholar 

  68. Kumer A, Sarker MN, Sunanda P (2019) The theoretical investigation of HOMO, LUMO, thermophysical properties and QSAR study of some aromatic carboxylic acids using HyperChem programming. Int J Chem Technol 3(1):26–37

    Article  Google Scholar 

  69. Kumer A, Sarker MN, Paul S (2019) The thermo physical, HOMO, LUMO, Vibrational spectroscopy and QSAR study of morphonium formate and acetate Ionic Liquid Salts using computational method. Turk Comput Theoret Chem 3(2):59–68

    Article  CAS  Google Scholar 

  70. Halder SK et al (2022) A comprehensive study to unleash the putative inhibitors of serotype2 of dengue virus: insights from an in silico structure-based drug discovery. Mol Biotechnol. https://doi.org/10.1007/s12033-022-00582-1

    Article  PubMed  PubMed Central  Google Scholar 

  71. Patel HM et al (2014) Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Med Chem Res 23:4991–5007

    Article  CAS  Google Scholar 

  72. Dev S, Dhaneshwar SR, Mathew B (2016) Discovery of camptothecin based topoisomerase I inhibitors: identification using an atom based 3D-QSAR, pharmacophore modeling, virtual screening and molecular docking approach. Comb Chem High Throughput Screening 19(9):752–763

    Article  CAS  Google Scholar 

  73. De Oliveira DB, Gaudio AC (2000) BuildQSAR: a new computer program for QSAR analysis. Quant Struct-Act Relat 19(6):599–601

    Article  Google Scholar 

  74. Rahman MA, Matin MM, Kumer A, Chakma U, Rahman MR (2022) Modified D-glucofuranoses as new black fungus protease inhibitors: computational screening, docking, dynamics, and QSAR study. Phys Chem Res 10(2):195–209

    Google Scholar 

  75. Lee G-R, Shin W-H, Park H-B, Shin S-M, Seok C-O (2012) Conformational sampling of flexible ligand-binding protein loops. Bull Korean Chem Soc 33(3):770–774

    Article  CAS  Google Scholar 

  76. Teague SJ (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2(7):527–541

    Article  CAS  PubMed  Google Scholar 

  77. Khan RJ et al (2021) Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2′-O-ribose methyltransferase. J Biomol Struct Dyn 39(8):2679–2692

    Article  CAS  PubMed  Google Scholar 

  78. Mosquera-Yuqui F, Lopez-Guerra N, Moncayo-Palacio EA (2022) Targeting the 3CLpro and RdRp of SARS-CoV-2 with phytochemicals from medicinal plants of the Andean Region: molecular docking and molecular dynamics simulations. J Biomol Struct Dyn 40(5):2010–2023

    Article  CAS  PubMed  Google Scholar 

  79. Kushwaha PP, Singh AK, Prajapati KS, Shuaib M, Gupta S, Kumar S (2021) Phytochemicals present in Indian ginseng possess potential to inhibit SARS-CoV-2 virulence: a molecular docking and MD simulation study. Microb Pathog 157:104954

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Xiao X, Wu Z-C, Chou K-C (2011) A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PLoS ONE 6(6):e20592

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Pillaiyar T, Manickam M, Namasivayam V, Hayashi Y, Jung S-H (2016) An overview of severe acute respiratory syndrome–coronavirus (SARS-CoV) 3CL protease inhibitors: peptidomimetics and small molecule chemotherapy. J Med Chem 59(14):6595–6628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Hossain, M.S., Rahman, M.A., Dey, P.R. et al. Natural Isatin Derivatives Against Black Fungus: In Silico Studies. Curr Microbiol 81, 113 (2024). https://doi.org/10.1007/s00284-024-03621-z

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