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2000
Volume 26, Issue 9
  • ISSN: 1389-2010
  • E-ISSN: 1873-4316

Abstract

The utilization of subtractive genomic analysis has emerged as an important and essential method in modern drug discovery and development since it significantly improves the process of identifying and validating potential targets for discovering novel therapeutic compounds to treat severe infections caused by Antimicrobial-resistant (AMR) - pathogenic species. This review provides a complete overview of the methodology, advantages, disadvantages, and prospects, associated with subtractive genomic research in the context of drug discovery and development. The initial phase of analysis encompasses the retrieval of data, which serves as a foundation for the subsequent data mining process in Phase 1. After data mining, Phase 2 utilizes subtractive channels for the target's non-homology and essentiality analysis. Phase 3 of the study aims to provide a comprehensive understanding of prospective targets by their qualitative characterization. Further, Phase 4 of the study emphasizes on conducting structure-based analyses, which involves the determination, refinement, evaluation, and validation of three-dimensional structures of the target proteins, along with their active site prediction and selection of the novel therapeutic compounds against that active site on the obtained targets through virtual screening and docking studies by utilizing various databases and servers. The therapeutic compounds obtained can be then validated by and testing, thereby establishing a connection between the computational predictions and real applications.

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2024-05-28
2025-09-08
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References

  1. GylesC. In silico identification of putative drug targets in methicillin resistant Staphylococcus aureus: A subtractive genomic approach.Aquac Res2019103
    [Google Scholar]
  2. MukherjeeS. GangopadhyayK. MukherjeeS.B. Identification of potential new vaccine candidates in Salmonella typhi using reverse vaccinology and subtractive genomics-based approach.bioRxiv2019
    [Google Scholar]
  3. SakharkarM. RajamanickamK. ChandraR. KhanH.A. AlhomidaA.S. YangJ. Identification of novel drug targets in bovine respiratory disease: An essential step in applying biotechnologic techniques to develop more effective therapeutic treatments.Drug Des. Devel. Ther.2018121135114610.2147/DDDT.S163476 29765203
    [Google Scholar]
  4. AhmadS. NavidA. AkhtarA.S. AzamS.S. WadoodA. Pérez-SánchezH. Subtractive genomics, molecular docking and molecular dynamics simulation revealed LpxC as a potential drug target against multi-drug resistant klebsiella pneumoniae.Interdiscip. Sci.201911350852610.1007/s12539‑018‑0299‑y 29721784
    [Google Scholar]
  5. MarquesP.H. PradoL.C.S. FeliceA.G. RodriguesT.C.V. PereiraU.P. JaiswalA.K. AzevedoV. OliveiraC.J.F. SoaresS. Insights into the Vibrio genus: A one health perspective from host adaptability and antibiotic resistance to In silico identification of drug targets.Antibiotics20221110139910.3390/antibiotics11101399 36290057
    [Google Scholar]
  6. UddinR. SiddiquiQ.N. AzamS.S. SaimaB. WadoodA. Identification and characterization of potential druggable targets among hypothetical proteins of extensively drug resistant Mycobacterium tuberculosis (XDR KZN 605) through subtractive genomics approach.Eur. J. Pharm. Sci.2018114132310.1016/j.ejps.2017.11.014 29174549
    [Google Scholar]
  7. ShanmughamB. PanA. Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: A novel hierarchical in silico approach.PLoS One201383e5912610.1371/journal.pone.0059126 23527108
    [Google Scholar]
  8. KhanK. JalalK. UddinR. An integrated in silico based subtractive genomics and reverse vaccinology approach for the identification of novel vaccine candidate and chimeric vaccine against XDR Salmonella typhi H58.Genomics2022114211030110.1016/j.ygeno.2022.110301 35149170
    [Google Scholar]
  9. SubhashiniR. JeyamM. Computational identification of putative drug targets in malassezia globosa by subtractive genomics and protein cluster network approach.Int. J. Pharm. Pharm. Sci.20179921510.22159/ijpps.2017v9i9.20609
    [Google Scholar]
  10. BarhD. TiwariS. JainN. AliA. SantosA.R. MisraA.N. AzevedoV. KumarA. In silico subtractive genomics for target identification in human bacterial pathogens.Drug Dev. Res.201172216217710.1002/ddr.20413
    [Google Scholar]
  11. NoorF. AhmadS. SaleemM. AlshayaH. QasimM. RehmanA. EhsanH. TalibN. SaleemH. JardanB.Y.A. AslamS. Designing a multi-epitope vaccine against Chlamydia pneumoniae by integrating the core proteomics, subtractive proteomics and reverse vaccinology-based immunoinformatics approaches.Comput. Biol. Med.202214510550710.1016/j.compbiomed.2022.105507 35429833
    [Google Scholar]
  12. VetrivelU. SubramanianG. DorairajS. A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens.HUGO J.201151-4253410.1007/s11568‑011‑9152‑7 23205162
    [Google Scholar]
  13. SinghS.K. Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design.Springer202110.1007/978‑981‑15‑8936‑2
    [Google Scholar]
  14. KeshriV. SinghD.P. PrabhaR. RaiA. SharmaA.K. Genome subtraction for the identification of potential antimicrobial targets in Xanthomonas oryzae pv. oryzae PXO99A pathogenic to rice.3 Biotech201441919510.1007/s13205‑013‑0131‑7
    [Google Scholar]
  15. BadieO.H. BasyonyA.F. SamirR. Computer-based identification of potential druggable targets in multidrug-resistant Acinetobacter baumannii: A combined in silico, in vitro and in vivo study.Microorganisms20221010197310.3390/microorganisms10101973 36296249
    [Google Scholar]
  16. ReddyR.A. Membrane drug target identification in mycoplasma pneumonia-A subtractive genomic approach.Res. J. Pharm. Biol. Chem. Sci.201672
    [Google Scholar]
  17. BatoolN. WaqarM. BatoolS. Comparative genomics study for identification of putative drug targets in Salmonella typhi Ty2.Gene2016576154455910.1016/j.gene.2015.11.007 26555890
    [Google Scholar]
  18. KhanK. UddinR. Integrated bioinformatics based subtractive genomics approach to decipher the therapeutic function of hypothetical proteins from Salmonella typhi XDR H-58 strain.Biotechnol. Lett.202244227929810.1007/s10529‑021‑03219‑6 35037232
    [Google Scholar]
  19. JaiswalA.K. Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis.J. Venom. Anim. Toxins Incl. Trop. Dis.2021202127e20200027
    [Google Scholar]
  20. AfzalM. HassanS.S. SohailS. CampsI. KhanY. BasharatZ. KarimA. AurongzebM. IrfanM. SalmanM. MorelC.M. Genomic landscape of the emerging XDR Salmonella Typhi for mining druggable targets clpP, hisH, folP and gpmI and screening of novel TCM inhibitors, molecular docking and simulation analyses.BMC Microbiol.20232312510.1186/s12866‑023‑02756‑6 36681806
    [Google Scholar]
  21. OmeershffudinU.N.M. KumarS. Antimicrobial resistance in Klebsiella pneumoniae: identification of bacterial DNA adenine methyltransferase as a novel drug target from hypothetical proteins using subtractive genomics.Genomics Inform.2022204e4710.5808/gi.22067 36617654
    [Google Scholar]
  22. Kumar JaiswalA. TiwariS. JamalS. BarhD. AzevedoV. SoaresS. An in silico identification of common putative vaccine candidates against treponema pallidum: A reverse vaccinology and subtractive genomics based approach.Int. J. Mol. Sci.201718240210.3390/ijms18020402 28216574
    [Google Scholar]
  23. GoyalM. CituC. SinghN. In silico identification of novel drug targets in Acinetobacter baumannii by subtractive genomic approach.Asian J. Pharm. Clin. Res.201811323010.22159/ajpcr.2018.v11i3.22105
    [Google Scholar]
  24. FaveroL.M. ChideroliR.T. FerrariN.A. AzevedoV.A.D.C. TiwariS. BarreroL.N.M. PereiraU.P. In silico prediction of new drug candidates against the multidrug-resistant and potentially zoonotic fish pathogen serotype III Streptococcus agalactiae.Front. Genet.202011102410.3389/fgene.2020.01024 33005185
    [Google Scholar]
  25. SayersE.W. BoltonE.E. BristerJ.R. CaneseK. ChanJ. ComeauD.C. ConnorR. FunkK. KellyC. KimS. MadejT. Marchler-BauerA. LanczyckiC. LathropS. LuZ. NissenT.F. MurphyT. PhanL. SkripchenkoY. TseT. WangJ. WilliamsR. TrawickB.W. PruittK.D. SherryS.T. Database resources of the national center for biotechnology information.Nucleic Acids Res.202250D1D20D2610.1093/nar/gkab1112 34850941
    [Google Scholar]
  26. MdA.M.H.H. Identification of potential novel therapeutic drug target against Elizabethkingia anophelis by integrative pan and subtractive genomic analysis: An in silico approach.Comput. Biol. Med.2023165107436
    [Google Scholar]
  27. NayakS. PradhanD. SinghH. ReddyM.S. Computational screening of potential drug targets for pathogens causing bacterial pneumonia.Microb. Pathog.201913027128210.1016/j.micpath.2019.03.024 30914386
    [Google Scholar]
  28. DhananjayM. In silico identification and characterization of potential drug.Europ. J. Biomed. Pharm. Sci.201855
    [Google Scholar]
  29. FatobaA.J. OkpekuM. AdelekeM.A. Subtractive genomics approach for identification of novel therapeutic drug targets in Mycoplasma genitalium.Pathogens202110892110.3390/pathogens10080921 34451385
    [Google Scholar]
  30. HosenM.I. TanmoyA.M. MahbubaD.A. SalmaU. NazimM. IslamM.T. AkhteruzzamanS. Application of a subtractive genomics approach for in silico identification and characterization of novel drug targets in Mycobacterium tuberculosis F11.Interdiscip. Sci.201461485610.1007/s12539‑014‑0188‑y 24464704
    [Google Scholar]
  31. JalalK. IzneidA.T. KhanK. AbbasM. HayatA. BawazeerS. UddinR. Identification of vaccine and drug targets in Shigella dysenteriae sd197 using reverse vaccinology approach.Sci. Rep.202212125110.1038/s41598‑021‑03988‑0 34997046
    [Google Scholar]
  32. ChenL. VFDB: A reference database for bacterial virulence factors.Nucleic Acids Res.200533D325D32810.1093/nar/gki008
    [Google Scholar]
  33. AshrafB. AtiqN. KhanK. WadoodA. UddinR. Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis.PLoS One2022178e027325210.1371/journal.pone.0273252 36006987
    [Google Scholar]
  34. AlcockB.P. RaphenyaA.R. LauT.T.Y. TsangK.K. BouchardM. EdalatmandA. HuynhW. NguyenA.L.V. ChengA.A. LiuS. MinS.Y. MiroshnichenkoA. TranH.K. WerfalliR.E. NasirJ.A. OloniM. SpeicherD.J. FlorescuA. SinghB. FaltynM. KoutouchevaH.A. SharmaA.N. BordeleauE. PawlowskiA.C. ZubykH.L. DooleyD. GriffithsE. MaguireF. WinsorG.L. BeikoR.G. BrinkmanF.S.L. HsiaoW.W.L. DomselaarG.V. McArthurA.G. CARD 2020: Antibiotic resistome surveillance with the comprehensive antibiotic resistance database.Nucleic Acids Res.201948D1gkz93510.1093/nar/gkz935 31665441
    [Google Scholar]
  35. SzklarczykD. GableA.L. LyonD. JungeA. WyderS. Huerta-CepasJ. SimonovicM. DonchevaN.T. MorrisJ.H. BorkP. JensenL.J. MeringC. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.Nucleic Acids Res.201947D1D607D61310.1093/nar/gky1131 30476243
    [Google Scholar]
  36. KhanM.T. MahmudA. IqbalA. HoqueS.F. HasanM. Subtractive genomics approach towards the identification of novel therapeutic targets against human Bartonella bacilliformis.Inform. Med. Unlocked20202010038510.1016/j.imu.2020.100385
    [Google Scholar]
  37. PrabhaR. SinghD.P. AhmadK. KumarS.P.J. KumarP. Subtractive genomics approach for identification of putative antimicrobial targets in Xanthomonas oryzae pv. oryzae KACC10331.Arch. Phytopathol. Pflanzenschutz2019527-886387210.1080/03235408.2018.1562674
    [Google Scholar]
  38. SubramanianG. VetrivelU. MohamedyousuffM.I. Deciphering novel potential antibacterial targets in tomato pathogen Ralstonia solanacearum GMI1000 through integration of in silico subtractive genomics, codon usage and protein–protein interaction analyses.Australas. Plant Pathol.202251112313310.1007/s13313‑021‑00845‑6
    [Google Scholar]
  39. KhanM.T. MahmudA. HasanM. AzimK.F. BegumM.K. RolinM.H. AkterA. MondalS.I. Proteome exploration of Legionella pneumophila to identify novel therapeutics: A hierarchical subtractive genomics and reverse vaccinology approach.Microbiol. Spectr.2022104e00373e2210.1128/spectrum.00373‑22 35863001
    [Google Scholar]
  40. AlhamhoomY. HaniU. BennaniF.E. RahmanN. RashidM.A. AbbasM.N. RastrelliL. Identification of new drug target in Staphylococcus lugdunensis by subtractive genomics analysis and their inhibitors through molecular docking and molecular dynamic simulation studies.Bioengineering20229945110.3390/bioengineering9090451 36134997
    [Google Scholar]
  41. UddinR. AzamS.S. WadoodA. KhanW. FarooqU. KhanA. Computational identification of potential drug targets against Mycobacterium leprae.Med. Chem. Res.201625347348110.1007/s00044‑016‑1501‑6
    [Google Scholar]
  42. LuoH. LinY. LiuT. LaiF.L. ZhangC.T. GaoF. ZhangR. DEG 15, an update of the database of essential genes that includes built-in analysis tools.Nucleic Acids Res.202149D1D677D68610.1093/nar/gkaa917 33095861
    [Google Scholar]
  43. WenQ.F. LiuS. DongC. GuoH.X. GaoY.Z. GuoF.B. Geptop 2.0: An updated, more precise, and faster geptop server for identification of prokaryotic essential genes.Front. Microbiol.201910123610.3389/fmicb.2019.01236 31214154
    [Google Scholar]
  44. TanwerP. KoloraS.R.R. BabbarA. SalujaD. ChaudhryU. Identification of potential therapeutic targets in Neisseria gonorrhoeae by an in-silico approach.J. Theor. Biol.202049011017210.1016/j.jtbi.2020.110172 31972174
    [Google Scholar]
  45. KaurH. KaliaM. TanejaN. Identification of novel non-homologous drug targets against Acinetobacter baumannii using subtractive genomics and comparative metabolic pathway analysis.Microb. Pathog.202115210460810.1016/j.micpath.2020.104608 33166618
    [Google Scholar]
  46. BeiranvandS. DoostiA. MirzaeiS.A. Putative novel B-cell vaccine candidates identified by reverse vaccinology and genomics approaches to control Acinetobacter baumannii serotypes.Infect. Genet. Evol.20219610513810.1016/j.meegid.2021.105138 34793968
    [Google Scholar]
  47. TrevisanR.O. SantosM.M. DesidérioC.S. AlvesL.G. de SousaJ.T. de OliveiraC.L. JaiswalA.K. TiwariS. BoviW.G. de SilvaO.M. MadeiraC.J.C. CastellanoL.R.C. SilvaM.V. AzevedoV. JuniorR.V. OliveiraC.J.F. de SoaresC.S. In silico identification of new targets for diagnosis, vaccine, and drug candidates against Trypanosoma cruzi.Dis. Markers2020202011510.1155/2020/9130719 33488847
    [Google Scholar]
  48. SolankiV. TiwariM. TiwariV. Prioritization of potential vaccine targets using comparative proteomics and designing of the chimeric multi-epitope vaccine against Pseudomonas aeruginosa.Sci. Rep.201991524010.1038/s41598‑019‑41496‑4 30918289
    [Google Scholar]
  49. YuC.S. ChengC.W. SuW.C. ChangK.C. HuangS.W. HwangJ.K. LuC.H. CELLO2GO: A web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.PLoS One201496e9936810.1371/journal.pone.0099368 24911789
    [Google Scholar]
  50. JalalK. KhanK. BasharatZ. AbbasM.N. UddinR. AliF. KhanS.A. HassanS.S. Reverse vaccinology approach for multi-epitope centered vaccine design against delta variant of the SARS-CoV-2.Environ. Sci. Pollut. Res. Int.20222940600356005310.1007/s11356‑022‑19979‑1 35414157
    [Google Scholar]
  51. DorostiH. ZareiM. NezafatN. Proteome exploration of human coronaviruses for identifying novel vaccine candidate: A hierarchical subtractive genomics and reverse vaccinology approach.Recent Pat. Biotechnol.202317216317510.2174/1872208316666220504234800 35538841
    [Google Scholar]
  52. HeY. XiangZ. MobleyH.L.T. Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development.J. Biomed. Biotechnol.2010201011510.1155/2010/297505 20671958
    [Google Scholar]
  53. DhandaS.K. MahajanS. PaulS. YanZ. KimH. JespersenM.C. JurtzV. AndreattaM. GreenbaumJ.A. MarcatiliP. SetteA. NielsenM. PetersB. IEDB-AR: Immune epitope database—analysis resource in 2019.Nucleic Acids Res.201947W1W502W50610.1093/nar/gkz452 31114900
    [Google Scholar]
  54. BarhD. Narayan MisraA. KumarA. In silico identification of dual ability of n. gonorrhoeae ddl for developing drug and vaccine against pathogenic neisseria and other human pathogens.J. Proteomics Bioinform.20103308209010.4172/jpb.1000125
    [Google Scholar]
  55. LarsenM.V. LundegaardC. LamberthK. BuusS. LundO. NielsenM. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction.BMC Bioinformatics20078142410.1186/1471‑2105‑8‑424 17973982
    [Google Scholar]
  56. SharmaN. PatiyalS. DhallA. PandeA. AroraC. RaghavaG.P.S. AlgPred 2.0: An improved method for predicting allergenic proteins and mapping of IgE epitopes.Brief. Bioinform.2021224bbaa29410.1093/bib/bbaa294 33201237
    [Google Scholar]
  57. GawadeP. GhoshP. Genomics driven approach for identification of novel therapeutic targets in Salmonella enterica.Gene201866821122010.1016/j.gene.2018.05.058 29778427
    [Google Scholar]
  58. MukherjeeS. KunduI. AskariM. BaraiR.S. VenkateshK.V. ThomasI.S. Exploring the druggable proteome of Candida species through comprehensive computational analysis.Genomics2021113272873910.1016/j.ygeno.2020.12.040 33484798
    [Google Scholar]
  59. OtasekD. MorrisJ.H. BouçasJ. PicoA.R. DemchakB. Cytoscape automation: Empowering workflow-based network analysis.Genome Biol.201920118510.1186/s13059‑019‑1758‑4 31477170
    [Google Scholar]
  60. WuJ. ValleniusT. OvaskaK. WestermarckJ. MäkeläT.P. HautaniemiS. Integrated network analysis platform for protein-protein interactions.Nat. Methods200961757710.1038/nmeth.1282 19079255
    [Google Scholar]
  61. ShannonP. MarkielA. OzierO. BaligaN.S. WangJ.T. RamageD. AminN. SchwikowskiB. IdekerT. Cytoscape: A software environment for integrated models of biomolecular interaction networks.Genome Res.200313112498250410.1101/gr.1239303 14597658
    [Google Scholar]
  62. DarH.A. IsmailS. WaheedY. AhmadS. JamilZ. AzizH. HettaH.F. MuhammadK. Designing a multi-epitope vaccine against Mycobacteroides abscessus by pangenome-reverse vaccinology.Sci. Rep.20211111119710.1038/s41598‑021‑90868‑2 34045649
    [Google Scholar]
  63. BhardwajT. HaqueS. SomvanshiP. Comparative assessment of the therapeutic drug targets of C. botulinum ATCC 3502 and C. difficile str. 630 using in silico subtractive proteomics approach.J. Cell. Biochem.20191209161601618410.1002/jcb.28897 31081164
    [Google Scholar]
  64. TatusovR.L. GalperinM.Y. NataleD.A. KooninE.V. The COG database: A tool for genome-scale analysis of protein functions and evolution.Nucleic Acids Res.2000281333610.1093/nar/28.1.33 10592175
    [Google Scholar]
  65. KumarA. ThotakuraP.L. TiwaryB.K. KrishnaR. Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions.BMC Microbiol.20161618410.1186/s12866‑016‑0700‑0 27176600
    [Google Scholar]
  66. WishartD.S. DrugBank: A knowledgebase for drugs, drug actions and drug targets.Nucleic Acids Res.200836D901D90610.1093/nar/gkm958
    [Google Scholar]
  67. MeshramR.J. GoundgeM.B. KolteB.S. GaccheR.N. An in silico approach in identification of drug targets in Leishmania: A subtractive genomic and metabolic simulation analysis.Parasitol. Int.201969597010.1016/j.parint.2018.11.006 30503238
    [Google Scholar]
  68. GuptaR. RaiC.S. Review on computational techniques to identify drug targets from whole proteome of fungi and bacteria.Communications in Computer and Information Science.SingaporeSpringer202010.1007/978‑981‑15‑5827‑6_28
    [Google Scholar]
  69. AlmeidaP.C.S. RoqueB.S. FeliceA.G. JaiswalA.K. TiwariS. AzevedoV. Silva-VergaraM.L. de SoaresC.S. PaimF.K. FonsecaF.M. Comparative genomics of Histoplasma capsulatum and prediction of new vaccines and drug targets.J. Fungi20239219310.3390/jof9020193 36836308
    [Google Scholar]
  70. MessaoudiA. BelguithH. Ben HamidaJ. Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase.Theor. Biol. Med. Model.20131012210.1186/1742‑4682‑10‑22 23547944
    [Google Scholar]
  71. DesaiS. TahilramaniP. PatelD. MeshramD. PatelP. In silico prediction and docking of tertiary structure of multifunctional protein x of hepatitis B virus.IBBJ201734169180
    [Google Scholar]
  72. GuerlerA. GovindarajooB. ZhangY. Mapping monomeric threading to protein-protein structure prediction.J. Chem. Inf. Model.201353371772510.1021/ci300579r 23413988
    [Google Scholar]
  73. SiqueiraL. VenskeS. Ab initio protein structure prediction using evolutionary approach: A survey.Rev. Inform. Teor. Apl.2021282112410.22456/2175‑2745.111993
    [Google Scholar]
  74. HeoL. ParkH. SeokC. GalaxyRefine: Protein structure refinement driven by side-chain repacking.Nucleic Acids Res.201341W384W38810.1093/nar/gkt458
    [Google Scholar]
  75. BasharatZ. KhanK. JalalK. AhmadD. HayatA. AlotaibiG. MouslemA.A. AlkhaylA.F.F. AlmatroudiA. An in silico hierarchal approach for drug candidate mining and validation of natural product inhibitors against pyrimidine biosynthesis enzyme in the antibiotic-resistant Shigella flexneri.Infect. Genet. Evol.20229810523310.1016/j.meegid.2022.105233 35104682
    [Google Scholar]
  76. HaywardS. GrootB.L. Normal modes and essential dynamics.Methods Mol. Biol.20084438910610.1007/978‑1‑59745‑177‑2_5 18446283
    [Google Scholar]
  77. GrantB.J. SkjærvenL. YaoX.Q. The BIO3D packages for structural bioinformatics.Protein Sci.2021301203010.1002/pro.3923 32734663
    [Google Scholar]
  78. YuW. MackerellA.D. Computer-aided drug design methods.Methods Mol. Biol.201715208510610.1007/978‑1‑4939‑6634‑9_5
    [Google Scholar]
  79. HollingsworthS.A. DrorR.O. Molecular dynamics simulation for all.Neuron20189961129114310.1016/j.neuron.2018.08.011 30236283
    [Google Scholar]
  80. JalalK. KhanK. HassamM. AbbasM.N. UddinR. KhusroA. SahibzadaM.U.K. GajdácsM. Identification of a novel therapeutic target against XDR salmonella typhi H58 using genomics driven approach followed up by natural products virtual screening.Microorganisms2021912251210.3390/microorganisms9122512 34946114
    [Google Scholar]
  81. DoraP. Important databases and tools to identify promising drug targets by subtractive genomics approach – A review.Int. J. Res. Eng. Technol.20154645345510.15623/ijret.2015.0406077
    [Google Scholar]
  82. KaushikA.C. MehmoodA. DaiX. WeiD.Q. A comparative chemogenic analysis for predicting drug-target pair via machine learning approaches.Sci. Rep.2020101687010.1038/s41598‑020‑63842‑7 32322011
    [Google Scholar]
  83. ShiragannavarS.S. ShettarA.K. MadagiS.B. SarawadS. Subtractive genomics approach in identifying polysacharide biosynthesis protein as novel drug target against Eubacterium nodatum.Asian J. Pharm. Pharmacol.20195238239210.31024/ajpp.2019.5.2.24
    [Google Scholar]
  84. NogueiraW.G. JaiswalA.K. TiwariS. RamosR.T.J. GhoshP. BarhD. AzevedoV. SoaresS.C. Computational identification of putative common genomic drug and vaccine targets in Mycoplasma genitalium.Genomics202111342730274310.1016/j.ygeno.2021.06.011 34118385
    [Google Scholar]
  85. ZhouJ. YangZ. HeY. JiJ. LinQ. LiJ. A novel molecular docking program based on a multi-swarm competitive algorithm.Swarm Evol. Comput.20237810129210.1016/j.swevo.2023.101292
    [Google Scholar]
  86. GentileF. AgrawalV. HsingM. TonA.T. BanF. NorinderU. GleaveM.E. CherkasovA. Deep docking: A deep learning platform for augmentation of structure based drug discovery.ACS Cent. Sci.20206693994910.1021/acscentsci.0c00229 32607441
    [Google Scholar]
  87. PradoL.C.S. FeliceG.A. RodriguesT.C.V. TiwariS. AndradeB.S. KatoR.B. OliveiraC.J.F. SilvaM.V. BarhD. AzevedoV.A.C. JaiswalA.K. SoaresS.C. New putative therapeutic targets against Serratia marcescens using reverse vaccinology and subtractive genomics.J. Biomol. Struct. Dyn.20224020101061012110.1080/07391102.2021.1942211 34192477
    [Google Scholar]
  88. UddinR. ArifA. Potential drug targets identification against clostridioides difficile (CD) and characterization of indispensable proteins by a subtractive genomics approach followed by virtual screening.Lett. Drug Des. Discov.20221929210710.2174/1570180818666210930160128
    [Google Scholar]
  89. SaleemH. AshfaqU.A. NadeemH. ZubairM. SiddiqueM.H. RasulI. Subtractive genomics and molecular docking approach to identify drug targets against Stenotrophomonas maltophilia.PLoS One20211612e026111110.1371/journal.pone.0261111 34910751
    [Google Scholar]
  90. VilarS. CozzaG. MoroS. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery.Curr. Top. Med. Chem.20088181555157210.2174/156802608786786624 19075767
    [Google Scholar]
  91. DwivediU.N. TiwariS. SinghP. SinghS. AwasthiM. PandeyV.P. Treponema pallidum putative novel drug target identification and validation: Rethinking syphilis therapeutics with plant-derived terpenoids.OMICS201519210411410.1089/omi.2014.0154 25683888
    [Google Scholar]
  92. KumarS. FatimaM.A. Computational drug target and toxicity analysis among hypothetical proteins of mycobacterium tuberculosis h37rv strain. Int. J. Med. Toxicol. Legal.Med.2020233–410.5958/0974‑4614.2020.00062.5
    [Google Scholar]
  93. PiresD.E.V. BlundellT.L. AscherD.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures.J. Med. Chem.20155894066407210.1021/acs.jmedchem.5b00104 25860834
    [Google Scholar]
  94. 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]
  95. ShahidF. AlghamdiY.S. MashraqiM. KhurshidM. AshfaqU.A. Proteome based mapping and molecular docking revealed DnaA as a potential drug target against Shigella sonnei.Saudi J. Biol. Sci.20222921147115910.1016/j.sjbs.2021.09.051 35241965
    [Google Scholar]
  96. AlturkiN.A. MashraqiM.M. JalalK. KhanK. BasharatZ. AlzamamiA. Therapeutic target identification and inhibitor screening against riboflavin synthase of colorectal cancer associated Fusobacterium nucleatum.Cancers20221424626010.3390/cancers14246260 36551744
    [Google Scholar]
  97. MondalS.I. FerdousS. AkterA. MahmudZ. KarimN. IslamM.M. JewelN.A. AfrinT. Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: An in silico approach.Adv. Appl. Bioinform. Chem.201581496310.2147/AABC.S88522 26677339
    [Google Scholar]
  98. AsaloneK.C. NelsonM.M. BrachtJ.R. Novel sequence discovery by subtractive genomics.J. Vis. Exp.2019201914310.3791/58877‑v 30735163
    [Google Scholar]
  99. KhanK. BasharatZ. JalalK. MashraqiM.M. AlzamamiA. AlshamraniS. UddinR. Identification of therapeutic targets in an emerging gastrointestinal pathogen Campylobacter ureolyticus and possible intervention through natural products.Antibiotics202211568010.3390/antibiotics11050680 35625323
    [Google Scholar]
  100. AgronP.G. MachtM. RadnedgeL. SkowronskiE.W. MillerW. AndersenG.L. Use of subtractive hybridization for comprehensive surveys of prokaryotic genome differences.FEMS Microbiol. Lett.2002211217518210.1111/j.1574‑6968.2002.tb11221.x 12076809
    [Google Scholar]
  101. BairochA. ApweilerR. WuC.H. The universal protein resource (UniProt).Nucleic Acids Res.200533D154D15910.1093/nar/gki070
    [Google Scholar]
  102. MoriyaY. ItohM. OkudaS. YoshizawaA.C. KanehisaM. KAAS: An automatic genome annotation and pathway reconstruction server.Nucleic Acids Res.200735W182W18510.1093/nar/gkm321
    [Google Scholar]
  103. HuangY. NiuB. GaoY. FuL. LiW. CD-HIT Suite: A web server for clustering and comparing biological sequences.Bioinformatics201026568068210.1093/bioinformatics/btq003 20053844
    [Google Scholar]
  104. YuN.Y. WagnerJ.R. LairdM.R. MelliG. ReyS. LoR. DaoP. SahinalpS.C. EsterM. FosterL.J. BrinkmanF.S.L. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.Bioinformatics201026131608161510.1093/bioinformatics/btq249 20472543
    [Google Scholar]
  105. KingB.R. VuralS. PandeyS. BarteauA. GudaC. ngLOC: Software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes.BMC Res. Notes20125135110.1186/1756‑0500‑5‑351 22780965
    [Google Scholar]
  106. DoytchinovaI.A. FlowerD.R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines.BMC Bioinformatics200781410.1186/1471‑2105‑8‑4 17207271
    [Google Scholar]
  107. RammenseeH.G. BachmannJ. EmmerichN.P.N. BachorO.A. StevanovićS. SYFPEITHI: Database for MHC ligands and peptide motifs.Immunogenetics1999503-421321910.1007/s002510050595 10602881
    [Google Scholar]
  108. SinghH. RaghavaG.P.S. ProPred1: Prediction of promiscuous MHC Class-I binding sites.Bioinformatics20031981009101410.1093/bioinformatics/btg108 12761064
    [Google Scholar]
  109. NielsenM. LundegaardC. LundO. KeşmirC. The role of the proteasome in generating cytotoxic T-cell epitopes: Insights obtained from improved predictions of proteasomal cleavage.Immunogenetics2005571-2334110.1007/s00251‑005‑0781‑7 15744535
    [Google Scholar]
  110. SahaS. RaghavaG.P.S. BcePred: Prediction of continuous b-cell epitopes in antigenic sequences using physico-chemical properties.Artificial Immune Systems. ICARIS 2004 NicosiaG. CutelloV. BentleyP.J. TimmisJ. Berlin, HeidelbergSpringer200410.1007/978‑3‑540‑30220‑9_16
    [Google Scholar]
  111. SahaS. RaghavaG.P.S. Prediction methods for B-cell epitopes.Methods Mol. Biol.200740938739410.1007/978‑1‑60327‑118‑9_29 18450017
    [Google Scholar]
  112. ZhouY. ZhangY. ZhaoD. YuX. ShenX. ZhouY. WangS. QiuY. ChenY. ZhuF. TTD: Therapeutic target database describing target druggability information.Nucleic Acids Res.202452D1D1465D147710.1093/nar/gkad751 37713619
    [Google Scholar]
  113. LambertC. LéonardN. De BolleX. DepiereuxE. ESyPred3D: Prediction of proteins 3D structures.Bioinformatics20021891250125610.1093/bioinformatics/18.9.1250 12217917
    [Google Scholar]
  114. KelleyL.A. MezulisS. YatesC.M. WassM.N. SternbergM.J.E. The Phyre2 web portal for protein modeling, prediction and analysis.Nat. Protoc.201510684585810.1038/nprot.2015.053 25950237
    [Google Scholar]
  115. SchymkowitzJ. BorgJ. StricherF. NysR. RousseauF. SerranoL. The FoldX web server: An online force field.Nucleic Acids Res.200533W382W38810.1093/nar/gki387
    [Google Scholar]
  116. SödingJ. BiegertA. LupasA.N. The HHpred interactive server for protein homology detection and structure prediction.Nucleic Acids Res.200533W244W24810.1093/nar/gki408 15980461
    [Google Scholar]
  117. BaekM. Accurate prediction of protein structures and interactions using a three-track neural network.Science2021373655787187610.1126/science.abj8754
    [Google Scholar]
  118. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. ParedesR.B. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑2 34265844
    [Google Scholar]
  119. YangZ. ZengX. ZhaoY. ChenR. AlphaFold2 and its applications in the fields of biology and medicine.Signal Transduct. Target. Ther.20238111510.1038/s41392‑023‑01381‑z 36918529
    [Google Scholar]
  120. AdhikariB. ChengJ. CONFOLD2: Improved contact-driven ab initio protein structure modeling.BMC Bioinformatics20181912210.1186/s12859‑018‑2032‑6 29370750
    [Google Scholar]
  121. ZhouX. ZhengW. LiY. PearceR. ZhangC. BellE.W. ZhangG. ZhangY. I-TASSER-MTD: A deep-learning-based platform for multi-domain protein structure and function prediction.Nat. Protoc.202217102326235310.1038/s41596‑022‑00728‑0 35931779
    [Google Scholar]
  122. BerjanskiiM. PROSESS: A protein structure evaluation suite and server.Nucleic Acids Res.201038W633W64010.1093/nar/gkq375
    [Google Scholar]
  123. McGuffinL.J. BrysonK. JonesD.T. The PSIPRED protein structure prediction server.Bioinformatics200016440440510.1093/bioinformatics/16.4.404 10869041
    [Google Scholar]
  124. BuchanD.W.A. MinneciF. NugentT.C.O. BrysonK. JonesD.T. Scalable web services for the PSIPRED protein analysis workbench.Nucleic Acids Res.201341W349W35710.1093/nar/gkt381
    [Google Scholar]
  125. WiedersteinM. SipplM.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins.Nucleic Acids Res.200735W407W41010.1093/nar/gkm290
    [Google Scholar]
  126. IrwinJ.J. ShoichetB.K. ZINC-A free database of commercially available compounds for virtual screening.J. Chem. Inf. Model.200545117718210.1021/ci049714+ 15667143
    [Google Scholar]
  127. KimS. ThiessenP.A. BoltonE.E. ChenJ. FuG. GindulyteA. HanL. HeJ. HeS. ShoemakerB.A. WangJ. YuB. ZhangJ. BryantS.H. PubChem substance and compound databases.Nucleic Acids Res.201644D1D1202D121310.1093/nar/gkv951 26400175
    [Google Scholar]
  128. GaultonA. BellisL.J. BentoA.P. ChambersJ. DaviesM. HerseyA. LightY. McGlincheyS. MichalovichD. Al-LazikaniB. OveringtonJ.P. ChEMBL: A large-scale bioactivity database for drug discovery.Nucleic Acids Res.201240D1D1100D110710.1093/nar/gkr777 21948594
    [Google Scholar]
  129. CavallaD. Web alert: Using the internet for medicinal chemistry.The Practice of Medicinal Chemistry.Academic Press201525527210.1016/B978‑0‑12‑417205‑0.00035‑3
    [Google Scholar]
  130. LiuT. LinY. WenX. JorissenR.N. GilsonM.K. BindingD.B. A web-accessible database of experimentally determined protein-ligand binding affinities.Nucleic Acids Res.20073519820110.1093/nar/gkl999
    [Google Scholar]
  131. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.200931245546110.1002/jcc.21334 19499576
    [Google Scholar]
  132. DuhovnyS.D. DrorO. InbarY. NussinovR. WolfsonH.J. PharmaGist: A webserver for ligand-based pharmacophore detection.Nucleic Acids Res.200836W223W22810.1093/nar/gkn187
    [Google Scholar]
  133. WangX. ShenY. WangS. LiS. ZhangW. LiuX. LaiL. PeiJ. LiH. PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database.Nucleic Acids Res.201745W1W356W36010.1093/nar/gkx374 28472422
    [Google Scholar]
  134. WolberG. LangerT. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters.J. Chem. Inf. Model.200545116016910.1021/ci049885e
    [Google Scholar]
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