Abstract
Fungal histone deacetylases (HDACs) are enzymes known for their crucial role in gene expression regulation through histone deacetylation, leading to chromatin compaction and transcriptional control. Among them, Rpd3, a lysine deacetylase, has been extensively studied for its involvement in chromatin remodeling, gene expression, and various biological processes such as development, cell cycle progression, and stress response. Rpd3's significance in fungal pathogenesis makes it a potential target for antifungal therapies. This study utilized advanced computational tools to identify biogenic molecule hits against a homology-modeled Rpd3 structure. Molecular dynamics simulations verified the stability of the hits while docking studies revealed strong binding affinities (< – 8 kcal/mol) for Rpd3-ZINC000019941755, Rpd3-ZINC000005854718, and Rpd3-ZINC000014762752 complexes. The correlation between binding interactions and HOMO–LUMO properties was established through density functional theory calculations. Additionally, in silico pharmacokinetic and drug-likeness assessments highlighted the potential of these hits as drug candidates. Consequently, ZINC000019941755, ZINC000005854718, and ZINC000014762752 emerge as promising candidates for further investigation.
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References
Garnaud C, Champleboux M, Maubon D et al (2016) Histone deacetylases and their inhibition in Candida species. Front Microbiol. https://doi.org/10.3389/fmicb.2016.01238
Kuchler K, Jenull S, Shivarathri R, Chauhan N (2016) Fungal KATs/KDACs: a new highway to better antifungal drugs? PLoS Pathog 12:e1005938
Bauer I, Graessle S (2021) Fungal lysine deacetylases in virulence, resistance, and production of small bioactive compounds. Genes 12:1470
Kmetzsch L (2015) Histone deacetylases: Targets for antifungal drug development. Virulence 6:535. https://doi.org/10.1080/21505594.2015.1049807
Li X, Cai Q, Mei H et al (2015) The Rpd3/Hda1 family of histone deacetylases regulates azole resistance in Candida albicans. J Antimicrob Chemother 70:1993–2003. https://doi.org/10.1093/jac/dkv070
Hartl M, Füßl M, Boersema PJ et al (2017) Lysine acetylome profiling uncovers novel histone deacetylase substrate proteins in Arabidopsis. Mol Syst Biol 13:949. https://doi.org/10.15252/MSB.20177819
Yang XJ, Seto E (2008) The Rpd3/Hda1 family of lysine deacetylases: from bacteria and yeast to mice and men. Nat Rev Mol Cell Biol 9:206. https://doi.org/10.1038/NRM2346
Kurdistani SK, Robyr D, Tavazoie S, Grunstein M (2002) Genome-wide binding map of the histone deacetylase Rpd3 in yeast. Nat Genet 31:248–254. https://doi.org/10.1038/ng907
McKnight JN, Boerma JW, Breeden LL, Tsukiyama T (2015) Global promoter targeting of a conserved lysine deacetylase for transcriptional shutoff during quiescence entry. Mol Cell 59:732–743. https://doi.org/10.1016/j.molcel.2015.07.014
Zhang N, Yang Z, Zhang Z, Liang W (2020) BcRPD3-mediated histone deacetylation is involved in growth and pathogenicity of Botrytis cinerea. Front Microbiol 11:1832. https://doi.org/10.3389/FMICB.2020.01832/BIBTEX
Brandão FAS, Derengowski LS, Albuquerque P et al (2015) Histone deacetylases inhibitors effects on Cryptococcus neoformans major virulence phenotypes. Virulence 6:618–630. https://doi.org/10.1080/21505594.2015.1038014
Ma XJ, Yang CP, Xia DA (2016) Characterization and expression analysis of histone deacetylases family RPD3/HDA1 in Populus trichocarpa. Biol Plant 60:235–243. https://doi.org/10.1007/S10535-015-0579-X/METRICS
Mak KK, Pichika MR (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 24:773–780. https://doi.org/10.1016/J.DRUDIS.2018.11.014
Kelley EW (2022) Computer-aided drug design project for introductory high school students. J Chem Educ. https://doi.org/10.1021/ACS.JCHEMED.2C00989/ASSET/IMAGES/MEDIUM/ED2C00989_0008.GIF
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14:177–184. https://doi.org/10.1016/J.CSBJ.2016.04.004
Srinivas Reddy A, Priyadarshini Pati S, Praveen Kumar P et al (2007) Virtual screening in drug discovery—a computational perspective. Curr Protein Pept Sci 8:329–351. https://doi.org/10.2174/138920307781369427
Sadybekov AV, Katritch V (2023) Computational approaches streamlining drug discovery. Nature 616(7958):673–685. https://doi.org/10.1038/s41586-023-05905-z
Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP (2019) Key topics in molecular docking for drug design. Int J Mol Sci. https://doi.org/10.3390/IJMS20184574
Pinzi L, Rastelli G (2019) Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci 20:4331. https://doi.org/10.3390/IJMS20184331
Salo-Ahen OMH, Alanko I, Bhadane R et al (2020) Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 2021 9:71. https://doi.org/10.3390/PR9010071
Salmaso V, Moro S (2018) Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: an overview. Front Pharmacol 9:923. https://doi.org/10.3389/FPHAR.2018.00923/BIBTEX
Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:1–9. https://doi.org/10.1186/1741-7007-9-71/FIGURES/4
Shaheena R (2022) Role of DFT in drug design: a mini review. Drug Des 11:1–4. https://doi.org/10.35248/2169-0138.22.11.216
van de Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2(3):192–204. https://doi.org/10.1038/nrd1032
Wan H (2013) What ADME tests should be conducted for preclinical studies? ADMET DMPK 1:19–28. https://doi.org/10.5599/ADMET.1.3.9
Stouch TR, Kenyon JR, Johnson SR et al (2003) In silico ADME/Tox: why models fail. J Comput Aided Mol Des 17:83–92
Wang Y, Xing J, Xu Y et al (2015) In silico ADME/T modelling for rational drug design. Q Rev Biophys 48:488–515. https://doi.org/10.1017/S0033583515000190
Rathod S, Chavan P, Mahuli D et al (2023) Exploring biogenic chalcones as DprE1 inhibitors for antitubercular activity via in silico approach. J Mol Model 29:1–23. https://doi.org/10.1007/S00894-023-05521-8
Rathod S, Shinde K, Porlekar J et al (2022) Computational exploration of anti-cancer potential of flavonoids against cyclin-dependent kinase 8: an in silico molecular docking and dynamic approach. ACS Omega 8:391–409. https://doi.org/10.1021/acsomega.2c04837
Nitulescu M, Alves de Oliveira T, Pires da Silva M et al (2023) Virtual screening algorithms in drug discovery: a review focused on machine and deep learning methods. Drugs Drug Candidates 2:311–334. https://doi.org/10.3390/DDC2020017
Schaduangrat N, Lampa S, Simeon S et al (2020) (2020) Towards reproducible computational drug discovery. J Cheminform 12(1):1–30. https://doi.org/10.1186/S13321-020-0408-X
Xiang M, Cao Y, Fan W et al (2012) Computer-aided drug design: lead discovery and optimization. Comb Chem High Throughput Screen 15:328–337. https://doi.org/10.2174/138620712799361825
O’boyleBanckJames NMMCA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:1–14. https://doi.org/10.1186/1758-2946-3-33
Dora EG, Rudin N, Martell JR et al (1999) RPD3 (REC3) mutations affect mitotic recombination in Saccharomyces cerevisiae. Curr Genet 35:68–76. https://doi.org/10.1007/S002940050434/METRICS
Apweiler R, Martin MJ, O’Donovan C et al (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40:D71–D75. https://doi.org/10.1093/NAR/GKR981
Bordoli L, Kiefer F, Arnold K et al (2009) Protein structure homology modeling using SWISS-MODEL workspace. Nat Protoc 4:1–13. https://doi.org/10.1038/nprot.2008.197
Abdullahi M, Adeniji SE, Arthur DE, Haruna A (2021) Homology modeling and molecular docking simulation of some novel imidazo[1,2-a]pyridine-3-carboxamide (IPA) series as inhibitors of Mycobacterium tuberculosis. J Genet Eng Biotechnol. https://doi.org/10.1186/s43141-020-00102-1
Roman Laskowski BA, Macarthur MW, Thornton JM (1983) Computer Programs PROCHECK: a program to check the stereochemicai quality of protein structures. J Appl Crystallogr 26:283–291
Wiederstein M, Sippl MJ (2007) ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. https://doi.org/10.1093/nar/gkm290
Bagal VK, Rathod SS, Mulla MM et al (2023) Exploration of bioactive molecules from Tinospora cordifolia and Actinidia deliciosa as an immunity modulator via molecular docking and molecular dynamics simulation study. Nat Prod Res. https://doi.org/10.1080/14786419.2023.2165076
BIOVIA (2020) Discovery studio visualizer. Dassault Systemes, San Diego
Pol-Fachin L, Fernandes CL, Verli H (2009) GROMOS96 43a1 performance on the characterization of glycoprotein conformational ensembles through molecular dynamics simulations. Carbohydr Res 344:491–500. https://doi.org/10.1016/j.carres.2008.12.025
Abraham MJ, Murtola T, Schulz R et al (2015) Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
NosÉ S (2002) A molecular dynamics method for simulations in the canonical ensemble. Mol Phys 100:191–198. https://doi.org/10.1080/00268970110089108
Huang C, Li C, Choi PYK et al (2011) A novel method for molecular dynamics simulation in the isothermal-isobaric ensemble. Mol Phys 109:191–202. https://doi.org/10.1080/00268976.2010.513345
Bepari AK, Reza HM (2021) Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation. PeerJ 9:e11261. https://doi.org/10.7717/peerj.11261
Gorai S, Junghare V, Kundu K et al (2022) Synthesis of dihydrobenzofuro[3,2-b]chromenes as potential 3CLpro inhibitors of SARS-CoV-2: a molecular docking and molecular dynamics study. ChemMedChem 17:e202100782. https://doi.org/10.1002/cmdc.202100782
Berendsen HJC, Postma JPM, Van Gunsteren WF et al (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. https://doi.org/10.1063/1.448118
Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190. https://doi.org/10.1063/1.328693
Kushwaha PP, Singh AK, Bansal T et al (2021) Identification of natural inhibitors against SARS-CoV-2 drugable targets using molecular docking, molecular dynamics simulation, and MM-PBSA approach. Front Cell Infect Microbiol 11:730288. https://doi.org/10.3389/fcimb.2021.730288
Miar M, Shiroudi A, Pourshamsian K et al (2021) Theoretical investigations on the HOMO–LUMO gap and global reactivity descriptor studies, natural bond orbital, and nucleus-independent chemical shifts analyses of 3-phenylbenzo[d]thiazole-2(3H)-imine and its para-substituted derivatives: solvent and substituent effects. J Chem Res 45:147–158
Rathod S, Dey S, Pawar S et al (2023) Identification of potential biogenic chalcones against antibiotic resistant efflux pump (AcrB) via computational study. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2023.2225099
Elkaeed EB, Yousef RG, Elkady H et al (2022) Design, synthesis, docking, DFT, MD simulation studies of a new nicotinamide-based derivative: in vitro anticancer and VEGFR-2 inhibitory effects. Molecules. https://doi.org/10.3390/molecules27144606
Rochlani S, Bhatia M, Rathod S et al (2023) Exploration of limonoids for their broad spectrum antiviral potential via DFT, molecular docking and molecular dynamics simulation approach. Nat Prod Res. https://doi.org/10.1080/14786419.2023.2202398
Neese F (2012) The ORCA program system. Wiley Interdiscip Rev Comput Mol Sci 2:73–78. https://doi.org/10.1002/wcms.81
Snyder HD, Kucukkal TG (2021) Computational chemistry activities with avogadro and ORCA. J Chem Educ 98:1335–1341. https://doi.org/10.1021/acs.jchemed.0c00959
Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules OPEN. Sci Rep 7:42717. https://doi.org/10.1038/srep42717
DE Pires V, Blundell TL, Ascher DB, 1ga UK, (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
Corso G, Jing B, Stark H et al (2023) Blind protein-ligand docking with diffusion-based deep generative models. Biophys J 122:143a. https://doi.org/10.1016/j.bpj.2022.11.937
Yu Y, Lu S, Gao Z, et al (2023) Do deep learning models really outperform traditional approaches in molecular docking? Biomolecules 07134
Hetényi C, Van Der Spoel D (2006) Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett 580:1447–1450. https://doi.org/10.1016/J.FEBSLET.2006.01.074
Alex A, Millan DS, Perez M et al (2011) Intramolecular hydrogen bonding to improve membrane permeability and absorption in beyond rule of five chemical space. Medchemcomm 2:669–674. https://doi.org/10.1039/c1md00093d
Bitencourt-Ferreira G, Veit-Acosta M, de Azevedo WF (2019) Hydrogen bonds in protein-ligand complexes. Methods Mol Biol 2053:93–107. https://doi.org/10.1007/978-1-4939-9752-7_7
Pantsar T, Poso A (2018) Binding affinity via docking: fact and fiction. Molecules 23:1899. https://doi.org/10.3390/MOLECULES23081899
Pace CN, Fu H, Fryar KL et al (2014) Contribution of hydrogen bonds to protein stability. Protein Sci 23:652–661. https://doi.org/10.1002/pro.2449
Karplus M, Petsko GA (1990) Molecular dynamics simulations in biology. Nature 347:6294. https://doi.org/10.1038/347631a0. (347:631–639)
Martin Karplus J, McCammon A (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652
Zhao H, Caflisch A (2015) Molecular dynamics in drug design. Eur J Med Chem 91:4–14. https://doi.org/10.1016/j.ejmech.2014.08.004
Bopp PA, Hawlicka E, Fritzsche S (2018) The Hitchhiker’s guide to molecular dynamics: a lecture companion, mostly for master’s and PhD students interested in using molecular dynamics simulations. ChemTexts. https://doi.org/10.1007/s40828-018-0056-1
Adcock SA, McCammon JA (2006) Molecular dynamics: Survey of methods for simulating the activity of proteins. Chem Rev 106:1589–1615. https://doi.org/10.1021/cr040426m
De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061. https://doi.org/10.1021/acs.jmedchem.5b01684
Mumit MA, Pal TK, Alam MA et al (2020) DFT studies on vibrational and electronic spectra, HOMO–LUMO, MEP, HOMA, NBO and molecular docking analysis of benzyl-3-N-(2,4,5-trimethoxyphenylmethylene)hydrazinecarbodithioate. J Mol Struct. https://doi.org/10.1016/j.molstruc.2020.128715
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Rathod, S., Bhande, D., Pawar, S. et al. Identification of Potential Hits against Fungal Lysine Deacetylase Rpd3 via Molecular Docking, Molecular Dynamics Simulation, DFT, In-Silico ADMET and Drug-Likeness Assessment. Chemistry Africa 7, 1151–1164 (2024). https://doi.org/10.1007/s42250-023-00766-5
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DOI: https://doi.org/10.1007/s42250-023-00766-5