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2000
Volume 21, Issue 19
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

Patients with COVID-19 often have an impact on populations with underlying co-infection. METTL3 plays a crucial role in numerous infectious diseases, making the research on broad-spectrum anti-infective drugs targeting METTL3 both captivating and compelling.

Objective

The study aims to identify anti-infection potential compounds targeting METTL3 using a comprehensive virtual screening approach.

Methods

The approach involves ensemble docking and molecular dynamics simulations to predict and validate the potential of natural compounds. This was complemented by the application of deep learning, specifically CNN binary classification and regression models, to predict the anti-infection capabilities of the compounds identified through docking. Experimental validation through Surface Plasmon Resonance (SPR) further confirmed the computational predictions. Network and gene ontology analyses provided insights into the compounds' biological targets and pathways.

Results

Ensemble docking approach achieved high prediction accuracy. We identified 17 natural compounds as potential METTL3 inhibitors, with Rosavin and Eriocitrin showing strong anti-infection potential against HIV-1, SARS-CoV-2, Mycobacterium tuberculosis, and Influenza A. Rosavin's low toxicity and strong METTL3 binding affinity were confirmed by SPR experiments and MD simulations. Network and gene ontology analyses suggest Rosavin may enhance immune responses by disrupting interferon degradation.

Conclusion

The multidimensional analysis identified Rosavin as a potent METTL3 inhibitor with significant anti-COVID-19 co-infection diseases potential.

© 2024 Bentham Science Publishers
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References

  1. TamuziJ.L. AyeleB.T. ShumbaC.S. AdetokunbohO.O. Uwimana-NicolJ. HaileZ.T. InuguJ. NyasuluP.S. Implications of COVID-19 in high burden countries for HIV/TB: A systematic review of evidence.BMC Infect. Dis.202020174410.1186/s12879‑020‑05450‑4 33036570
     [Google Scholar]
  2. SterlingT.R. PhamP.A. ChaissonR.E. HIV infection-related tuberculosis: Clinical manifestations and treatment.Clin. Infect. Dis.201050s3Suppl. 3S223S23010.1086/651495 20397952
     [Google Scholar]
  3. LiuL. LiH. HuD. WangY. ShaoW. ZhongJ. YangS. LiuJ. ZhangJ. Insights into N6-methyladenosine and programmed cell death in cancer.Mol. Cancer20222113210.1186/s12943‑022‑01508‑w 35090469
     [Google Scholar]
  4. LiN. HuiH. BrayB. GonzalezG.M. ZellerM. AndersonK.G. KnightR. SmithD. WangY. CarlinA.F. RanaT.M. METTL3 regulates viral m6A RNA modification and host cell innate immune responses during SARS-CoV-2 infection.Cell Rep.202135610909110.1016/j.celrep.2021.109091 33961823
     [Google Scholar]
  5. LiH.B. TongJ. ZhuS. BatistaP.J. DuffyE.E. ZhaoJ. BailisW. CaoG. KroehlingL. ChenY. WangG. BroughtonJ.P. ChenY.G. KlugerY. SimonM.D. ChangH.Y. YinZ. FlavellR.A. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways.Nature2017548766733834210.1038/nature23450 28792938
     [Google Scholar]
  6. WangX. LuZ. GomezA. HonG.C. YueY. HanD. FuY. ParisienM. DaiQ. JiaG. RenB. PanT. HeC. N6-methyladenosine-dependent regulation of messenger RNA stability.Nature2014505748111712010.1038/nature12730 24284625
     [Google Scholar]
  7. XuJ. CaiY. MaZ. JiangB. LiuW. ChengJ. GuoN. WangZ. SealyJ.E. SongC. WangX. LiY. The RNA helicase DDX5 promotes viral infection via regulating N6-methyladenosine levels on the DHX58 and NFκB transcripts to dampen antiviral innate immunity.PLoS Pathog.2021174e100953010.1371/journal.ppat.1009530 33909701
     [Google Scholar]
  8. LuM. XueM. WangH.T. KairisE.L. AhmadS. WeiJ. ZhangZ. LiuQ. ZhangY. GaoY. GarcinD. PeeplesM.E. SharmaA. HurS. HeC. LiJ. Nonsegmented negative-sense RNA viruses utilize N6 -Methyladenosine (m 6 A) as a common strategy to evade host innate immunity.J. Virol.2021959e01939e2010.1128/JVI.01939‑20 33536170
     [Google Scholar]
  9. NiZ. SunP. ZhengJ. WuM. YangC. ChengM. YinM. CuiC. WangG. YuanL. GaoQ. LiY. JNK signaling promotes bladder cancer immune escape by regulating METTL3-mediated m6A modification of PD-L1 mRNA.Cancer Res.20228291789180210.1158/0008‑5472.CAN‑21‑1323 35502544
     [Google Scholar]
  10. KimG.W. ImamH. KhanM. SiddiquiA. N6-Methyladenosine modification of hepatitis B and C viral RNAs attenuates host innate immunity via RIG-I signaling.J. Biol. Chem.202029537131231313310.1074/jbc.RA120.014260 32719095
     [Google Scholar]
  11. LuM. ZhangZ. XueM. ZhaoB.S. HarderO. LiA. LiangX. GaoT.Z. XuY. ZhouJ. FengZ. NiewieskS. PeeplesM.E. HeC. LiJ. N6-methyladenosine modification enables viral RNA to escape recognition by RNA sensor RIG-I.Nat. Microbiol.20205458459810.1038/s41564‑019‑0653‑9 32015498
     [Google Scholar]
  12. WilliamsG.D. GokhaleN.S. HornerS.M. Regulation of viral infection by the RNA modification N6 -Methyladenosine.Annu. Rev. Virol.20196123525310.1146/annurev‑virology‑092818‑015559 31283446
     [Google Scholar]
  13. GeY. LingT. WangY. JiaX. XieX. ChenR. ChenS. YuanS. XuA. Degradation of WTAP blocks antiviral responses by reducing the m 6 A levels of IRF3 and IFNAR1 mRNA.EMBO Rep.20212211e5210110.15252/embr.202052101 34467630
     [Google Scholar]
  14. ChenJ. WeiX. WangX. LiuT. ZhaoY. ChenL. LuoY. DuH. LiY. LiuT. CaoL. ZhouZ. ZhangZ. LiangL. LiL. YanX. ZhangX. DengX. YangG. YinP. HaoJ. YinZ. YouF. TBK1-METTL3 axis facilitates antiviral immunity.Cell Rep.202238711037310.1016/j.celrep.2022.110373 35172162
     [Google Scholar]
  15. BurgessH.M. DepledgeD.P. ThompsonL. SrinivasK.P. GrandeR.C. VinkE.I. AbebeJ.S. BlackabyW.P. HendrickA. AlbertellaM.R. KouzaridesT. StaplefordK.A. WilsonA.C. MohrI. Targeting the m 6 A RNA modification pathway blocks SARS-CoV-2 and HCoV-OC43 replication.Genes Dev.20213513-141005101910.1101/gad.348320.121 34168039
     [Google Scholar]
  16. SrinivasK.P. DepledgeD.P. AbebeJ.S. RiceS.A. MohrI. WilsonA.C. Widespread remodeling of the m 6 A RNA-modification landscape by a viral regulator of RNA processing and export.Proc. Natl. Acad. Sci. USA202111830e210480511810.1073/pnas.2104805118 34282019
     [Google Scholar]
  17. ZhaoL. ZhaoY. LiuQ. HuangJ. LuY. PingJ. DDX5/METTL3-METTL14/YTHDF2 axis regulates replication of influenza a virus.Microbiol. Spectr.2022103e01098e2210.1128/spectrum.01098‑22 35583334
     [Google Scholar]
  18. UdoakangA.J. Djomkam ZuneA.L. TapelaK. NganyewoN.N. OlisakaF.N. AnyigbaC.A. Tawiah-EshunS. OwusuI.A. PaemkaL. AwandareG.A. QuashieP.K. The COVID-19, tuberculosis and HIV/AIDS: Ménage à Trois.Front. Immunol.202314110482810.3389/fimmu.2023.1104828 36776887
     [Google Scholar]
  19. ZhangT.P. LiR. WangL.J. HuangQ. LiH.M. Roles of the m6A methyltransferases METTL3, METTL14, and WTAP in pulmonary tuberculosis.Front. Immunol.20221399262810.3389/fimmu.2022.992628 36569923
     [Google Scholar]
  20. ShenS. RuiY. WangY. SuJ. YuX.F. SARS‐CoV‐2, HIV, and HPV: Convergent evolution of selective regulation of cGAS–STING signaling.J. Med. Virol.2023951e2822010.1002/jmv.28220 36229923
     [Google Scholar]
  21. Pereira-MontecinosC. Toro-AscuyD. Ananías-SáezC. Gaete-ArgelA. Rojas-FuentesC. Riquelme-BarriosS. Rojas-ArayaB. García-de-GraciaF. Aguilera-CortésP. ChnaidermanJ. AcevedoM.L. Valiente-EcheverríaF. Soto-RifoR. Epitranscriptomic regulation of HIV-1 full-length RNA packaging.Nucleic Acids Res.20225042302231810.1093/nar/gkac062 35137199
     [Google Scholar]
  22. BayoumiM. RohaimM.A. MunirM. Structural and virus regulatory insights into avian N6-Methyladenosine (m6A) machinery.Front. Cell Dev. Biol.2020854310.3389/fcell.2020.00543 32760718
     [Google Scholar]
  23. WinklerR. GillisE. LasmanL. SafraM. GeulaS. SoyrisC. NachshonA. Tai-SchmiedelJ. FriedmanN. Le-TrillingV.T.K. TrillingM. MandelboimM. HannaJ.H. SchwartzS. Stern-GinossarN. m6A modification controls the innate immune response to infection by targeting type I interferons.Nat. Immunol.201920217318210.1038/s41590‑018‑0275‑z 30559377
     [Google Scholar]
  24. Musarra-PizzoM. PennisiR. Ben-AmorI. MandalariG. SciortinoM.T. Antiviral activity exerted by natural products against human viruses.Viruses202113582810.3390/v13050828 34064347
     [Google Scholar]
  25. SongM. LiuY. LiT. LiuX. HaoZ. DingS. PanichayupakaranantP. ZhuK. ShenJ. Plant natural flavonoids against multidrug resistant pathogens.Adv. Sci.2021815210074910.1002/advs.202100749 34041861
     [Google Scholar]
  26. RifaiogluA.S. AtasH. MartinM.J. Cetin-AtalayR. AtalayV. DoğanT. Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases.Brief. Bioinform.20192051878191210.1093/bib/bby061 30084866
     [Google Scholar]
  27. Nguyen-VoT.H. NguyenL. DoN. LeP.H. NguyenT.N. NguyenB.P. LeL. Predicting drug-induced liver injury using convolutional neural network and molecular fingerprint-embedded features.ACS Omega2020539254322543910.1021/acsomega.0c03866 33043223
     [Google Scholar]
  28. TangX. ZhouY. YangM. LiW.T.C-D.T.A. Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks.IEEE Trans. Nanobioscience.202423457257810.1109/TNB.2024.3441590
     [Google Scholar]
  29. PalhamkhaniF. AlipourM. DehnadA. AbbasiK. RazzaghiP. GhasemiJ.B. DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks.J. Biomol. Struct. Dyn.20231211010.1080/07391102.2023.2291829
     [Google Scholar]
  30. StokesJ.M. YangK. SwansonK. JinW. Cubillos-RuizA. DonghiaN.M. MacNairC.R. FrenchS. CarfraeL.A. Bloom-AckermannZ. TranV.M. Chiappino-PepeA. BadranA.H. AndrewsI.W. ChoryE.J. ChurchG.M. BrownE.D. JaakkolaT.S. BarzilayR. CollinsJ.J. A deep learning approach to antibiotic discovery.Cell20201804688702.e1310.1016/j.cell.2020.01.021 32084340
     [Google Scholar]
  31. KumariM. SubbaraoN. Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.Comput. Biol. Med.202113210431710.1016/j.compbiomed.2021.104317
     [Google Scholar]
  32. QiuM. LiangX. DengS. LiY. KeY. WangP. MeiH. A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism.Comput. Biol. Med.202215010617710.1016/j.compbiomed.2022.106177 36242811
     [Google Scholar]
  33. ZhangQ. HanJ. ZhuY. YuF. HuX. TongH.H.Y. LiuH. Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays.J. Comput. Aided Mol. Des.2023371269570610.1007/s10822‑023‑00530‑4 37642861
     [Google Scholar]
  34. CampbellA.J. LambM.L. Joseph-McCarthyD. Ensemble-based docking using biased molecular dynamics.J. Chem. Inf. Model.20145472127213810.1021/ci400729j 24881672
     [Google Scholar]
  35. AlogheliH. OlandersG. SchaalW. BrandtP. KarlénA. Docking of macrocycles: Comparing rigid and flexible docking in glide.J. Chem. Inf. Model.201757219020210.1021/acs.jcim.6b00443 28079375
     [Google Scholar]
  36. FatimaS. GuptaP. SharmaS. SharmaA. AgarwalS.M. ADMET profiling of geographically diverse phytochemical using chemoinformatic tools.Future Med. Chem.2020121698710.4155/fmc‑2019‑0206 31793338
     [Google Scholar]
  37. KaramiT.K. HailuS. FengS. GrahamR. GukasyanH.J. Eyes on lipinski’s rule of five: A new “rule of thumb” for physicochemical design space of ophthalmic drugs.J. Ocul. Pharmacol. Ther.2022381435510.1089/jop.2021.0069 34905402
     [Google Scholar]
  38. MendezD. GaultonA. BentoA.P. ChambersJ. De VeijM. FélixE. MagariñosM.P. MosqueraJ.F. MutowoP. NowotkaM. Gordillo-MarañónM. HunterF. JuncoL. MugumbateG. Rodriguez-LopezM. AtkinsonF. BoscN. RadouxC.J. Segura-CabreraA. HerseyA. LeachA.R. ChEMBL: Towards direct deposition of bioassay data.Nucleic Acids Res.201947D1D930D94010.1093/nar/gky1075 30398643
     [Google Scholar]
  39. WangL. ZhangQ. WangZ. ZhuW. TanN. Design, synthesis, docking, molecular dynamics and bioevaluation studies on novel N-methylpicolinamide and thienopyrimidine derivatives with inhibiting NF-κB and TAK1 activities: Cheminformatics tools RDKit applied in drug design.Eur. J. Med. Chem.202122311357610.1016/j.ejmech.2021.113576 34153577
     [Google Scholar]
  40. DingY. MaZ. WenS. XieJ. ChangD. SiZ. WuM. LingH.A.P-C.N.N. Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification.IEEE Trans. Image Process.2021302826283610.1109/TIP.2021.3055617 33556008
     [Google Scholar]
  41. KashyapK. MahapatraP.P. AhmedS. BuyukbingolE. SiddiqiM.I. Identification of potential aldose reductase inhibitors using convolutional neural network-based in silico screening.J. Chem. Inf. Model.202363206261628210.1021/acs.jcim.3c00547 37788831
     [Google Scholar]
  42. LeM.T. PhanT.V. Tran-NguyenV.K. TranT.D. ThaiK.M. Prediction model of human ABCC2/MRP2 efflux pump inhibitors: A QSAR study.Mol. Divers.202125274175110.1007/s11030‑020‑10047‑9 32048150
     [Google Scholar]
  43. XingJ. LuW. LiuR. WangY. XieY. ZhangH. ShiZ. JiangH. LiuY.C. ChenK. JiangH. LuoC. ZhengM. Machine-learning-assisted approach for discovering novel inhibitors targeting bromodomain-containing protein 4.J. Chem. Inf. Model.20175771677169010.1021/acs.jcim.7b00098 28636361
     [Google Scholar]
  44. GuptaS. GuptaM.K. ShabazM. SharmaA. Deep learning techniques for cancer classification using microarray gene expression data.Front. Physiol.20221395270910.3389/fphys.2022.952709 36246115
     [Google Scholar]
  45. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.2017714271710.1038/srep42717 28256516
     [Google Scholar]
  46. HajamT.A. H, S.; Mashood Ahamed, F.M. Structural, vibrational spectroscopy, molecular docking, DFT studies and antibacterial activity of (E)-N1-(3-chlorobenzylidene)benzene-1,4-diamine.J. Biomol. Struct. Dyn.202341136295631210.1080/07391102.2022.2106516 35916271
     [Google Scholar]
  47. Van Der SpoelD. LindahlE. HessB. GroenhofG. MarkA.E. BerendsenH.J.C. GROMACS: Fast, flexible, and free.J. Comput. Chem.200526161701171810.1002/jcc.20291 16211538
     [Google Scholar]
  48. AbrahamM.J. GreadyJ.E. Optimization of parameters for molecular dynamics simulation using smooth particle‐mesh Ewald in GROMACS 4.5.J. Comput. Chem.20113292031204010.1002/jcc.21773 21469158
     [Google Scholar]
  49. GenhedenS. RydeU. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities.Expert Opin. Drug Discov.201510544946110.1517/17460441.2015.1032936 25835573
     [Google Scholar]
  50. LiuX. PengL. ZhouY. ZhangY. ZhangJ.Z.H. Computational alanine scanning with interaction entropy for protein–ligand binding free energies.J. Chem. Theory Comput.20181431772178010.1021/acs.jctc.7b01295 29406753
     [Google Scholar]
  51. ZhangH. LiuW. LiuZ. JuY. XuM. ZhangY. WuX. GuQ. WangZ. XuJ. Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening.MedChemComm20189693794510.1039/C7MD00642J 30108982
     [Google Scholar]
  52. DainaA. MichielinO. ZoeteV. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules.Nucleic Acids Res.201947W1W357W36410.1093/nar/gkz382 31106366
     [Google Scholar]
  53. ZhaoQ. PanW. ShiH. QiF. LiuY. YangT. SiH. SiG. Network pharmacology and molecular docking analysis on the mechanism of Baihe Zhimu decoction in the treatment of postpartum depression.Medicine 202210143e2932310.1097/MD.0000000000029323 36316904
     [Google Scholar]
  54. ZhangJ. ZhouY. MaZ. Multi-target mechanism of Tripteryguim wilfordii hook for treatment of ankylosing spondylitis based on network pharmacology and molecular docking.Ann. Med.20215311091109910.1080/07853890.2021.1918345 34259096
     [Google Scholar]
  55. ShenS. KongJ. QiuY. YangX. WangW. YanL. Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis.J. Cell. Biochem.20191206100691008110.1002/jcb.28290 30525236
     [Google Scholar]
  56. KumariM. SubbaraoN. Development of a deep learning-based quantitative structure-activity relationship model to identify potential inhibitors against the 3C-like protease of SARS-CoV-2.Future Med. Chem.202214211541155910.4155/fmc‑2021‑0063 36177879
     [Google Scholar]
  57. YankovaE. BlackabyW. AlbertellaM. RakJ. De BraekeleerE. TsagkogeorgaG. PilkaE.S. AsprisD. LeggateD. HendrickA.G. WebsterN.A. AndrewsB. FosbearyR. GuestP. IrigoyenN. EleftheriouM. GozdeckaM. DiasJ.M.L. BannisterA.J. VickB. JeremiasI. VassiliouG.S. RauschO. TzelepisK. KouzaridesT. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia.Nature2021593786059760110.1038/s41586‑021‑03536‑w 33902106
     [Google Scholar]
  58. ZhuY.X. ShengY.J. MaY.Q. DingH.M. Assessing the performance of screening MM/PBSA in protein–ligand interactions.J. Phys. Chem. B202212681700170810.1021/acs.jpcb.1c09424 35188781
     [Google Scholar]
  59. ZhaoX. ZhangL. WangJ. ZhangM. SongZ. NiB. YouY. Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis.J. Transl. Med.20211913510.1186/s12967‑020‑02698‑x 33468161
     [Google Scholar]
  60. KhannaK. MishraK.P. GanjuL. SinghS.B. Golden root: A wholesome treat of immunity.Biomed. Pharmacother.20178749650210.1016/j.biopha.2016.12.132 28073099
     [Google Scholar]
/content/journals/lddd/10.2174/0115701808337903241224065746
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Deep Learning-driven Identification and Evaluation of Potential Therapeutic Agents for COVID-19 Co-infection Diseases Targeting METTL3
Letters in Drug Design & Discovery 21, 4756 (2024); https://doi.org/10.2174/0115701808337903241224065746
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  • Article Type:     Research Article
Keyword(s): Convolutional neural networks; highly pathogenic infectious diseases; mettl3; molecular dynamics simulation; multi-conformational virtual screening; natural products

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