Recent Advances and Techniques for Identifying Novel Antibacterial Targets


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Background:With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

Methods:In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

Results:Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

Conclusion:The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Об авторах

Jingyi Qiu

, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences

Email: info@benthamscience.net

Ziyi Tang

, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences

Email: info@benthamscience.net

Yun He

Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University

Автор, ответственный за переписку.
Email: info@benthamscience.net

Adila Nazli

Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University

Email: info@benthamscience.net

Список литературы

  1. Feigenbaum, J.J.; Muller, C.; Wrigley-Field, E. Regional and racial inequality in infectious disease mortality in US cities, 1900-1948. Demography, 2019, 56(4), 1371-1388. doi: 10.1007/s13524-019-00789-z PMID: 31197611
  2. Malathi, K.; Ramaiah, S.; Reviews, G.E. Bioinformatics approaches for new drug discovery: A review. Biotechnol. Genet. Eng. Rev., 2018, 34(2), 243-260. doi: 10.1080/02648725.2018.1502984 PMID: 30064294
  3. Nazli, A.; He, D.L.; Xu, H.; Wang, Z.P.; He, Y. A comparative insight on the newly emerging rifamycins: Rifametane, rifalazil, TNP-2092 and TNP-2198. Curr. Med. Chem., 2022, 29(16), 2846-2862. doi: 10.2174/0929867328666210806114949 PMID: 34365945
  4. Zhao, S.; Wang, Z.P.; Lin, Z.; Wei, G.; Wen, X.; Li, S.; Yang, X.; Zhang, Q.; Jing, C.; Dai, Y.; Guo, J.; He, Y. Drug repurposing by siderophore conjugation: Synthesis and biological evaluation of siderophore-methotrexate conjugates as antibiotics. Angew. Chem. Int. Ed., 2022, 61(36), e202204139. doi: 10.1002/anie.202204139 PMID: 35802518
  5. Peng, H.; Xie, B.; Cen, X.; Dai, J.; Dai, Y.; Yang, X.; He, Y. Glutathione-responsive multifunctional nanoparticles based on mannose-modified pillar5arene for targeted antibiotic delivery against intracellular methicillin-resistant S. aureus. Mater. Chem. Front., 2022, 6(3), 360-367. doi: 10.1039/D1QM01459E
  6. Peng, H.; Xie, B.; Yang, X.; Dai, J.; Wei, G.; He, Y. Pillar5arene-based, dual pH and enzyme responsive supramolecular vesicles for targeted antibiotic delivery against intracellular MRSA. Chem. Commun., 2020, 56(58), 8115-8118. doi: 10.1039/D0CC02522D PMID: 32691784
  7. He, Y.; Yang, J.; Wu, B.; Risen, L.; Swayze, E.E. Synthesis and biological evaluations of novel benzimidazoles as potential antibacterial agents. Bioorg. Med. Chem. Lett., 2004, 14(5), 1217-1220. doi: 10.1016/j.bmcl.2003.12.051 PMID: 14980669
  8. Simpkin, V.L.; Renwick, M.J.; Kelly, R.; Mossialos, E. Incentivising innovation in antibiotic drug discovery and development: Progress, challenges and next steps. J. Antibiot., 2017, 70(12), 1087-1096. doi: 10.1038/ja.2017.124 PMID: 29089600
  9. Nazli, A.; He, D.L.; Liao, D.; Khan, M.Z.I.; Huang, C.; He, Y. Strategies and progresses for enhancing targeted antibiotic delivery. Adv. Drug Deliv. Rev., 2022, 189(11), 114502-114535. doi: 10.1016/j.addr.2022.114502 PMID: 35998828
  10. Wei, G.; He, Y. Antibacterial and antibiofilm activities of novel cyclic peptides against methicillin-resistant staphylococcus aureus. Int. J. Mol. Sci., 2022, 23(14), 8029-8045. doi: 10.3390/ijms23148029 PMID: 35887376
  11. Yang, X.; Xie, B.; Peng, H.; Shi, G.; Sreenivas, B.; Guo, J.; Wang, C.; He, Y. Eradicating intracellular MRSA via targeted delivery of lysostaphin and vancomycin with mannose-modified exosomes. J. Control. Release, 2021, 329(1), 454-467. doi: 10.1016/j.jconrel.2020.11.045 PMID: 33253805
  12. McDowell, L.L.; Quinn, C.L.; Leeds, J.A.; Silverman, J.A.; Silver, L.L. Perspective on antibacterial lead identification challenges and the role of hypothesis-driven strategies. SLAS Discov., 2019, 24(4), 440-456. doi: 10.1177/2472555218818786 PMID: 30890054
  13. He, Y.; Wu, B.; Yang, J.; Robinson, D.; Risen, L.; Ranken, R.; Blyn, L.; Sheng, S.; Swayze, E.E. 2-Piperidin-4-yl-benzimidazoles with broad spectrum antibacterial activities. Bioorg. Med. Chem. Lett., 2003, 13(19), 3253-3256. doi: 10.1016/S0960-894X(03)00661-9 PMID: 12951103
  14. Scheffler, R.J.; Colmer, S.; Tynan, H.; Demain, A.L.; Gullo, V.P. Antimicrobials, drug discovery, and genome mining. Appl. Microbiol. Biotechnol., 2013, 97(3), 969-978. doi: 10.1007/s00253-012-4609-8 PMID: 23233204
  15. Gould, I.M. Antibiotic resistance: The perfect storm. Int. J. Antimicrob. Agents, 2009, 34(8), S2-S5. doi: 10.1016/S0924-8579(09)70549-7 PMID: 19596110
  16. Yang, X.; Shi, G.; Guo, J.; Wang, C.; He, Y. Exosome-encapsulated antibiotic against intracellular infections of methicillin-resistant Staphylococcus aureus. Int. J. Nanomedicine, 2018, 13(4), 8095-8104. doi: 10.2147/IJN.S179380 PMID: 30555228
  17. Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M.T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J., 2016, 14(1), 177-184. doi: 10.1016/j.csbj.2016.04.004 PMID: 27293534
  18. Paananen, J.; Fortino, V. An omics perspective on drug target discovery platforms. Brief. Bioinform., 2020, 21(6), 1937-1953. doi: 10.1093/bib/bbz122 PMID: 31774113
  19. Rao, V.S.; Srinivas, K. Modern drug discovery process: an in-silico approach. J. Bioinform. Seq. Anal., 2011, 3(5), 89-94.
  20. Jiang, Z.; Zhou, Y. Using bioinformatics for drug target identification from the genome. Am. J. Pharmacogenom., 2005, 5(6), 387-396. doi: 10.2165/00129785-200505060-00005 PMID: 16336003
  21. Coates, A.R.M.; Hu, Y. Novel approaches to developing new antibiotics for bacterial infections. Br. J. Pharmacol., 2007, 152(8), 1147-1154. doi: 10.1038/sj.bjp.0707432 PMID: 17704820
  22. Shangguan, Z. A review of target identification strategies for drug discovery: From database to machine-based methods. J. Phys. Conf. Ser., 2021, 1893(1), 012013-012020. doi: 10.1088/1742-6596/1893/1/012013
  23. Singh, V.; Mizrahi, V. Identification and validation of novel drug targets in Mycobacterium tuberculosis. Drug Discov. Today, 2017, 22(3), 503-509. doi: 10.1016/j.drudis.2016.09.010 PMID: 27649943
  24. Buysse, J. The role of genomics in antibacterial target discovery. Curr. Med. Chem., 2001, 8(14), 1713-1726. doi: 10.2174/0929867013371699 PMID: 11562290
  25. Brötz-Oesterhelt, H.; Bandow, J.E.; Labischinski, H. Bacterial proteomics and its role in antibacterial drug discovery. Mass Spectrom. Rev., 2005, 24(4), 549-565. doi: 10.1002/mas.20030 PMID: 15389844
  26. Tounta, V.; Liu, Y.; Cheyne, A.; Larrouy-Maumus, G. Metabolomics in infectious diseases and drug discovery. Mol. Omics, 2021, 17(3), 376-393. doi: 10.1039/D1MO00017A PMID: 34125125
  27. Plaimas, K.; Eils, R.; König, R. Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst. Biol., 2010, 4(1), 56. doi: 10.1186/1752-0509-4-56 PMID: 20438628
  28. Perumal, D.; Lim, C.S.; Sakharkar, M.K. Biocomputational strategies for microbial drug target identification: New antibiotic targets. Springer Link: Berlin, 2008, 142, pp. 1-9.
  29. Joshi, H.; Verma, A.; Soni, D.K. Impact of microbial genomics approaches for novel antibiotic target: Microbial genomics in sustainable agroecosystems. Springer Link: Berlin, 2019, 2, pp. 75-88. doi: 10.1007/978-981-32-9860-6_5
  30. George, R.; Jacob, S.; Thomas, S.; Georrge, J.J. Approaches for novel drug target identification. Proceedings of International Science Symposium on Recent Trends in Science and Technology, New Delhi, India, August 10-13, 2017, pp. 399-421.
  31. Russell, C.; Rahman, A.; Mohammed, A.R. Application of genomics, proteomics and metabolomics in drug discovery, development and clinic. Ther. Deliv., 2013, 4(3), 395-413. doi: 10.4155/tde.13.4 PMID: 23442083
  32. Burbaum, J.; Tobal, G.M. Proteomics in drug discovery. Curr. Opin. Chem. Biol., 2002, 6(4), 427-433. doi: 10.1016/S1367-5931(02)00337-X PMID: 12133716
  33. Sarker, M.; Talcott, C.; Galande, A.K. In silico systems biology approaches for the identification of antimicrobial targets: In silico models for drug discovery. Springer Link: Berlin, 2013; 993, pp. 13-30.
  34. López-Gomollón, S. Detecting sRNAs by northern blotting: In MicroRNAs in development. Springer Link: Berlin, 2011; 732, pp. 25-38.
  35. Eissa, N.; Hussein, H.; Wang, H.; Rabbi, M.F.; Bernstein, C.N.; Ghia, J.E. Stability of reference genes for messenger RNA quantification by real-time PCR in mouse dextran sodium sulfate experimental colitis. PLoS One, 2016, 11(5), e0156289. doi: 10.1371/journal.pone.0156289 PMID: 27244258
  36. Moustafa, K.; Cross, J. Genetic approaches to study plant responses to environmental stresses: An overview. Biology, 2016, 5(2), 20-48. doi: 10.3390/biology5020020 PMID: 27196939
  37. Mackay, I.M.; Arden, K.E.; Nitsche, A. Real-time PCR in virology. Nucleic Acids Res., 2002, 30(6), 1292-1305. doi: 10.1093/nar/30.6.1292 PMID: 11884626
  38. K’osuri, M.A.; Kalei, A.; Onyango, R. Microbiology of hospital wastewater. In: current developments in biotechnology and bioengineering; Elsevier: Amsterdam, 2018; 404, pp. 103-148.
  39. Chen, X.; Yin, L.; Peng, L.; Liang, Y.; Lv, H.; Ma, T. Synergistic effect and mechanism of plumbagin with gentamicin against carbapenem-resistant Klebsiella pneumoniae. Infect. Drug Resist., 2020, 13(1), 2751-2759. doi: 10.2147/IDR.S265753 PMID: 32884304
  40. Martin, J.K., II; Sheehan, J.P.; Bratton, B.P.; Moore, G.M.; Mateus, A.; Li, S.H.J.; Kim, H.; Rabinowitz, J.D.; Typas, A.; Savitski, M.M.; Wilson, M.Z.; Gitai, Z. A dual-mechanism antibiotic kills gram-negative bacteria and avoids drug resistance. Cell, 2020, 181(7), 1518-1532.e14. doi: 10.1016/j.cell.2020.05.005 PMID: 32497502
  41. Lin, X.; Li, X.; Lin, X. A review on applications of computational methods in drug screening and design. Molecules, 2020, 25(6), 1375-1392. doi: 10.3390/molecules25061375 PMID: 32197324
  42. Rao, V.S.; Das, S.K.; Rao, V.J.; Srinubabu, G. Recent developments in life sciences research: Role of bioinformatics. Afr. J. Biotechnol., 2008, 7(5), 495-503.
  43. Pulido, M.R.; García-Quintanilla, M.; Gil-Marqués, M.L.; McConnell, M.J. Identifying targets for antibiotic development using omics technologies. Drug Discov. Today, 2016, 21(3), 465-472. doi: 10.1016/j.drudis.2015.11.014 PMID: 26691873
  44. Barh, D.; Tiwari, S.; Jain, N.; Ali, A.; Santos, A.R.; Misra, A.N.; Azevedo, V.; Kumar, A. In silico subtractive genomics for target identification in human bacterial pathogens. Drug Dev. Res., 2011, 72(2), 162-177. doi: 10.1002/ddr.20413
  45. Fields, F.R.; Lee, S.W.; McConnell, M.J. Using bacterial genomes and essential genes for the development of new antibiotics. Biochem. Pharmacol., 2017, 134(6), 74-86. doi: 10.1016/j.bcp.2016.12.002 PMID: 27940263
  46. Dembek, M.; Barquist, L.; Boinett, C.J.; Cain, A.K.; Mayho, M.; Lawley, T.D.; Fairweather, N.F.; Fagan, R.P. High-throughput analysis of gene essentiality and sporulation in Clostridium difficile. MBio, 2015, 6(2), e02383-14. doi: 10.1128/mBio.02383-14 PMID: 25714712
  47. Gawronski, J.D.; Wong, S.M.S.; Giannoukos, G.; Ward, D.V.; Akerley, B.J. Tracking insertion mutants within libraries by deep sequencing and a genome-wide screen for Haemophilus genes required in the lung. Proc. Natl. Acad. Sci., 2009, 106(38), 16422-16427. doi: 10.1073/pnas.0906627106 PMID: 19805314
  48. Barquist, L.; Boinett, C.J.; Cain, A.K. Approaches to querying bacterial genomes with transposon-insertion sequencing. RNA Biol., 2013, 10(7), 1161-1169. doi: 10.4161/rna.24765 PMID: 23635712
  49. Butt, A.M.; Tahir, S.; Nasrullah, I.; Idrees, M.; Lu, J.; Tong, Y. Mycoplasma genitalium: A comparative genomics study of metabolic pathways for the identification of drug and vaccine targets. Infect. Genet. Evol., 2012, 12(1), 53-62. doi: 10.1016/j.meegid.2011.10.017 PMID: 22057004
  50. Raskin, D.M.; Seshadri, R.; Pukatzki, S.U.; Mekalanos, J.J. Bacterial genomics and pathogen evolution. Cell, 2006, 124(4), 703-714. doi: 10.1016/j.cell.2006.02.002 PMID: 16497582
  51. Wadood, A.; Jamal, A.; Riaz, M.; Khan, A.; Uddin, R.; Jelani, M.; Azam, S.S. Subtractive genome analysis for in silico identification and characterization of novel drug targets in Streptococcus pneumonia strain JJA. Microb. Pathog., 2018, 115, 194-198. doi: 10.1016/j.micpath.2017.12.063 PMID: 29277475
  52. Vetrivel, U.; Subramanian, G.; Dorairaj, S. A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens. HUGO J., 2011, 5(1-4), 25-34. doi: 10.1007/s11568-011-9152-7 PMID: 23205162
  53. Sadhasivam, A.; Vetrivel, U. Genome-wide codon usage profiling of ocular infective Chlamydia trachomatis serovars and drug target identification. J. Biomol. Struct. Dyn., 2018, 36(8), 1979-2003. doi: 10.1080/07391102.2017.1343685 PMID: 28627970
  54. Lee, S.; Weon, S.; Lee, S.; Kang, C. Relative codon adaptation index, a sensitive measure of codon usage bias. Evol. Bioinform. Online, 2010, 6(1), EBO.S4608. doi: 10.4137/EBO.S4608 PMID: 20535230
  55. Ng, C.; Tay, M.; Tan, B.; Le, T.H.; Haller, L.; Chen, H.; Koh, T.H.; Barkham, T.M.S.; Thompson, J.R.; Gin, K.Y.H. Characterization of metagenomes in urban aquatic compartments reveals high prevalence of clinically relevant antibiotic resistance genes in wastewaters. Front. Microbiol., 2017, 8(1), 2200-2212. doi: 10.3389/fmicb.2017.02200 PMID: 29201017
  56. Singh, B.K.; Macdonald, C.A. Drug discovery from uncultivable microorganisms. Drug Discov. Today, 2010, 15(17-18), 792-799. doi: 10.1016/j.drudis.2010.07.002 PMID: 20656054
  57. Schmieder, R.; Edwards, R. Insights into antibiotic resistance through metagenomic approaches. Future Microbiol., 2012, 7(1), 73-89. doi: 10.2217/fmb.11.135 PMID: 22191448
  58. Torres-Cortés, G.; Millán, V.; Ramírez-Saad, H.C.; Nisa- Martínez, R.; Toro, N.; Martínez-Abarca, F. Characterization of novel antibiotic resistance genes identified by functional metagenomics on soil samples. Environ. Microbiol., 2011, 13(4), 1101-1114. doi: 10.1111/j.1462-2920.2010.02422.x PMID: 21281423
  59. Uddin, R.; Sufian, M. Core proteomic analysis of unique metabolic pathways of Salmonella enterica for the identification of potential drug targets. PLoS One, 2016, 11(1), e0146796. doi: 10.1371/journal.pone.0146796 PMID: 26799565
  60. Naz, A.; Obaid, A.; Shahid, F.; Dar, H.A.; Naz, K.; Ullah, N.; Ali, A. Reverse vaccinology and drug target identification through pan-genomics. In: Pan-Genomics: Applications, Challenges, and Future Prospects, 1st ed; Debmalya, B., Ed.; Elsevier: Amsterdam, 2020, 321, pp. 317-333. doi: 10.1016/B978-0-12-817076-2.00016-0
  61. Chao, M.C.; Abel, S.; Davis, B.M.; Waldor, M.K. The design and analysis of transposon insertion sequencing experiments. Nat. Rev. Microbiol., 2016, 14(2), 119-128. doi: 10.1038/nrmicro.2015.7 PMID: 26775926
  62. Cain, A.K.; Barquist, L.; Goodman, A.L.; Paulsen, I.T.; Parkhill, J.; van Opijnen, T. A decade of advances in transposon-insertion sequencing. Nat. Rev. Genet., 2020, 21(9), 526-540. doi: 10.1038/s41576-020-0244-x PMID: 32533119
  63. Fabian, B.K.; Foster, C.; Asher, A.J.; Elbourne, L.D.H.; Cain, A.K.; Hassan, K.A.; Tetu, S.G.; Paulsen, I.T. Elucidating essential genes in plant-associated Pseudomonas protegens Pf-5 using transposon insertion sequencing. J. Bacteriol., 2021, 203(7), 1-17. doi: 10.1128/JB.00432-20 PMID: 33257523
  64. DeJesus, M.A.; Zhang, Y.J.; Sassetti, C.M.; Rubin, E.J.; Sacchettini, J.C.; Ioerger, T.R. Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries. Bioinformatics, 2013, 29(6), 695-703. doi: 10.1093/bioinformatics/btt043 PMID: 23361328
  65. Bachman, M.A.; Breen, P.; Deornellas, V.; Mu, Q.; Zhao, L.; Wu, W.; Cavalcoli, J.D.; Mobley, H.L.T. Genome-wide identification of Klebsiella pneumoniae fitness genes during lung infection. MBio, 2015, 6(3), e00775-15. doi: 10.1128/mBio.00775-15 PMID: 26060277
  66. Zhao, L.; Anderson, M.T.; Wu, W.; T Mobley, H.L.; Bachman, M.A. TnseqDiff: Identification of conditionally essential genes in transposon sequencing studies. BMC Bioinformatics, 2017, 18(1), 326. doi: 10.1186/s12859-017-1745-2 PMID: 28683752
  67. Schoolnik, G. Functional and comparative genomics of pathogenic bacteria. Curr. Opin. Microbiol., 2002, 5(1), 20-26. doi: 10.1016/S1369-5274(02)00280-1 PMID: 11834364
  68. Shahid, F.; Shehroz, M.; Zaheer, T.; Ali, A. Subtractive genomics approaches: Towards anti-bacterial drug discovery. Front. Anti-infect. Drug Discov., 2020, 8(1), 144-145.
  69. Redon, R.; Carter, N.P. Comparative genomic hybridization: Microarray design and data interpretation. In: DNA Microarrays for Biomedical Research, 1st ed; Martin, D., Ed.; Springer Link: Berlin, 2009; 529, pp. 37-49. doi: 10.1007/978-1-59745-538-1_3
  70. Gillespie, S. Current status of molecular microbiological techniques for the analysis of drinking water. In: Molecular Microbial Diagnostic Methods, 1st ed; Nigel, C. Elsevier: Amsterdam, 2016; Vol. 264, pp. 39-58. doi: 10.1016/B978-0-12-416999-9.00003-4
  71. Torshizi, A.D.; Wang, K. Next-generation sequencing in drug development: Target identification and genetically stratified clinical trials. Drug Discov., 2018, 23(10), 1776-1783.
  72. Endrullat, C.; Glökler, J.; Franke, P.; Frohme, M. Standardization and quality management in next-generation sequencing. Appl. Transl. Genomics, 2016, 10(9), 2-9. doi: 10.1016/j.atg.2016.06.001 PMID: 27668169
  73. Unamba, C.I.N.; Nag, A.; Sharma, R.K. Next generation sequencing technologies: The doorway to the unexplored genomics of non-model plants. Front. Plant Sci., 2015, 6(12), 1074-1090. doi: 10.3389/fpls.2015.01074 PMID: 26734016
  74. Cantu, D.; Govindarajulu, M.; Kozik, A.; Wang, M.; Chen, X.; Kojima, K.K.; Jurka, J.; Michelmore, R.W.; Dubcovsky, J. Next generation sequencing provides rapid access to the genome of Puccinia striiformis f. sp. tritici, the causal agent of wheat stripe rust. PLoS One, 2011, 6(8), e24230. doi: 10.1371/journal.pone.0024230 PMID: 21909385
  75. Behjati, S.; Tarpey, P.S. What is next generation sequencing? Arch. Dis. Child. Educ. Pract. Ed., 2013, 98(6), 236-238. doi: 10.1136/archdischild-2013-304340 PMID: 23986538
  76. Ramanathan, B.; Jindal, H.M.; Le, C.F.; Gudimella, R.; Anwar, A.; Razali, R.; Poole-Johnson, J.; Manikam, R.; Sekaran, S.D. Next generation sequencing reveals the antibiotic resistant variants in the genome of Pseudomonas aeruginosa. PLoS One, 2017, 12(8), e0182524. doi: 10.1371/journal.pone.0182524 PMID: 28797043
  77. Kumar Jaiswal, A.; Tiwari, S.; Jamal, S.; Barh, D.; Azevedo, V.; Soares, S. An in-silico identification of common putative vaccine candidates against Treponema pallidum: A reverse vaccinology and subtractive genomics based approach. Int. J. Mol. Sci., 2017, 18(2), 402-417. doi: 10.3390/ijms18020402 PMID: 28216574
  78. Uddin, R.; Siraj, B.; Rashid, M.; Khan, A.; Ahsan Halim, S.; Al-Harrasi, A. Genome subtraction and comparison for the identification of novel drug targets against Mycobacterium avium subsp. hominissuis. Pathogens, 2020, 9(5), 368-382. doi: 10.3390/pathogens9050368 PMID: 32408506
  79. Asalone, K.C.; Nelson, M.M.; Bracht, J.R. Novel sequence discovery by subtractive genomics. J. Vis. Exp., 2019, 143(143), 1-7. PMID: 30735163
  80. Agron, P.G.; Macht, M.; Radnedge, L.; Skowronski, E.W.; Miller, W.; Andersen, G.L. Use of subtractive hybridization for comprehensive surveys of prokaryotic genome differences. FEMS Microbiol. Lett., 2002, 211(2), 175-182. doi: 10.1111/j.1574-6968.2002.tb11221.x PMID: 12076809
  81. dos Santos, D.F.K.; Istvan, P.; Quirino, B.F.; Kruger, R.H. Functional metagenomics as a tool for identification of new antibiotic resistance genes from natural environments. Microb. Ecol., 2017, 73(2), 479-491. doi: 10.1007/s00248-016-0866-x PMID: 27709246
  82. Mullany, P. Functional metagenomics for the investigation of antibiotic resistance. Virulence, 2014, 5(3), 443-447. doi: 10.4161/viru.28196 PMID: 24556726
  83. Kaur, R.; Yadav, B.; Tyagi, R. Microbiology of hospital wastewater. In: Current Developments in Biotechnology and Bioengineering, 1st ed; Ashok, P., Ed.; Elsevier: Amsterdam, 2020; Vol. 404, pp. 103-148. doi: 10.1016/B978-0-12-819722-6.00004-3
  84. Yang, H.; Chen, J.; Tang, S.; Li, Z.; Zhen, Y.; Huang, L.; Yi, J. New drug R&D of traditional Chinese medicine: Role of data mining approaches. J. Biol. Syst., 2009, 17(3), 329-347. doi: 10.1142/S0218339009002971
  85. Uchiyama, T.; Abe, T.; Ikemura, T.; Watanabe, K. Substrate-induced gene-expression screening of environmental metagenome libraries for isolation of catabolic genes. Nat. Biotechnol., 2005, 23(1), 88-93. doi: 10.1038/nbt1048 PMID: 15608629
  86. Podar, M.; Abulencia, C.B.; Walcher, M.; Hutchison, D.; Zengler, K.; Garcia, J.A.; Holland, T.; Cotton, D.; Hauser, L.; Keller, M. Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl. Environ. Microbiol., 2007, 73(10), 3205-3214. doi: 10.1128/AEM.02985-06 PMID: 17369337
  87. Ferrer, M.; Beloqui, A.; Timmis, K.N.; Golyshin, P.N. Metagenomics for mining new genetic resources of microbial communities. J. Mol. Microbiol. Biotechnol., 2009, 16(1-2), 109-123. PMID: 18957866
  88. Yun, J.; Ryu, S. Screening for novel enzymes from metagenome and SIGEX, as a way to improve it. Microb. Cell Fact., 2005, 4(1), 8. doi: 10.1186/1475-2859-4-8 PMID: 15790425
  89. Dash, H.R.; Das, S. Molecular methods for studying microorganisms from atypical environments. Methods Microbiol., 2018, 45, 89-122. doi: 10.1016/bs.mim.2018.07.005
  90. Zou, Y.; Xue, W.; Luo, G.; Deng, Z.; Qin, P.; Guo, R.; Sun, H.; Xia, Y.; Liang, S.; Dai, Y.; Wan, D.; Jiang, R.; Su, L.; Feng, Q.; Jie, Z.; Guo, T.; Xia, Z.; Liu, C.; Yu, J.; Lin, Y.; Tang, S.; Huo, G.; Xu, X.; Hou, Y.; Liu, X.; Wang, J.; Yang, H.; Kristiansen, K.; Li, J.; Jia, H.; Xiao, L. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol., 2019, 37(2), 179-185. doi: 10.1038/s41587-018-0008-8 PMID: 30718868
  91. Naz, K.; Naz, A.; Ashraf, S.T.; Rizwan, M.; Ahmad, J.; Baumbach, J.; Ali, A.; Pan, R.V. PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome. BMC Bioinformatics, 2019, 20(1), 123-133. doi: 10.1186/s12859-019-2713-9 PMID: 30871454
  92. Ding, W.; Baumdicker, F.; Neher, R.A. panX: Pan-genome analysis and exploration. Nucleic Acids Res., 2018, 46(1), e5. doi: 10.1093/nar/gkx977 PMID: 29077859
  93. Mira, A.; Martín-Cuadrado, A.B.; D’Auria, G.; Rodríguez- Valera, F. The bacterial pan-genome:A new paradigm in microbiology. Int. Microbiol., 2010, 13(2), 45-57. PMID: 20890839
  94. Read, T.D.; Ussery, D.W. Opening the pan-genomics box. Curr. Opin. Microbiol., 2006, 9(5), 496-498. doi: 10.1016/j.mib.2006.08.010
  95. Hassan, A.; Naz, A.; Obaid, A.; Paracha, R.Z.; Naz, K.; Awan, F.M.; Muhmmad, S.A.; Janjua, H.A.; Ahmad, J.; Ali, A. Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets. BMC Genomics, 2016, 17(1), 732. doi: 10.1186/s12864-016-2951-4 PMID: 27634541
  96. Gadd, G.M. Metals and microorganisms: A problem of definition. FEMS Microbiol. Lett., 1992, 100(1-3), 197-203. doi: 10.1111/j.1574-6968.1992.tb05703.x PMID: 1478456
  97. Feder, M.E.; Walser, J.C. The biological limitations of transcriptomics in elucidating stress and stress responses. J. Evol. Biol., 2005, 18(4), 901-910. doi: 10.1111/j.1420-9101.2005.00921.x PMID: 16033562
  98. Yang, X.L.; Shi, Y.; Zhang, D.D.; Xin, R.; Deng, J.; Wu, T.M.; Wang, H.M.; Wang, P.Y.; Liu, J.B.; Li, W.; Ma, Y.S.; Fu, D. Quantitative proteomics characterization of cancer biomarkers and treatment. Mol. Ther. Oncolytics, 2021, 21, 255-263. doi: 10.1016/j.omto.2021.04.006 PMID: 34095463
  99. Yakkioui, Y.; Temel, Y.; Chevet, E.; Negroni, L. Integrated and quantitative proteomics of human tumors. In: Methods in Enzymology, 1st ed; Arun, K.S. Elsevier: Amsterdam, 2017, Vol. 586, pp. 229-246. doi: 10.1016/bs.mie.2016.09.034
  100. Shiio, Y.; Aebersold, R. Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry. Nat. Protoc., 2006, 1(1), 139-145. doi: 10.1038/nprot.2006.22 PMID: 17406225
  101. Sethuraman, M.; McComb, M.E.; Heibeck, T.; Costello, C.E.; Cohen, R.A. Isotope-coded affinity tag approach to identify and quantify oxidant-sensitive protein thiols. Mol. Cell. Proteomics, 2004, 3(3), 273-278. doi: 10.1074/mcp.T300011-MCP200 PMID: 14726493
  102. Gygi, S.P.; Rist, B.; Gerber, S.A.; Turecek, F.; Gelb, M.H.; Aebersold, R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol., 1999, 17(10), 994-999. doi: 10.1038/13690 PMID: 10504701
  103. Colangelo, C.M.; Williams, K.R. Isotope-coded affinity tags for protein quantification. In: New and Emerging Proteomic Techniques, 1st ed; Dobrin, N., Ed.; Springer: Berlin, 2006; Vol. 328, pp. 151-158. doi: 10.1385/1-59745-026-X:151
  104. Cho, S.H.; Goodlett, D.; Franzblau, S. ICAT-based comparative proteomic analysis of non-replicating persistent Mycobacterium tuberculosis. Tuberculosis, 2006, 86(6), 445-460. doi: 10.1016/j.tube.2005.10.002 PMID: 16376151
  105. Rai, A.K.; Satija, N.K. Importance of targeted therapies in acute myeloid leukemia. In: Translational Biotechnology, 1st ed; Yasha, H., Ed.; Elsevier: Amsterdam, 2021; 341, pp. 107-133. doi: 10.1016/B978-0-12-821972-0.00017-4
  106. Wdowiak, A.P.; Duong, M.N.; Joyce, R.D.; Boyatzis, A.E.; Walkey, M.C.; Nealon, G.L.; Arthur, P.G.; Piggott, M.J. Isotope-coded maleimide affinity tags for proteomics applications. Bioconjug. Chem., 2021, 32(8), 1652-1666. doi: 10.1021/acs.bioconjchem.1c00206 PMID: 34160215
  107. Beretov, J.; Wasinger, V.C.; Graham, P.H.; Millar, E.K.; Kearsley, J.H.; Li, Y. Proteomics for breast cancer urine biomarkers. Adv. Clin. Chem., 2014, 63(1), 123-167. doi: 10.1016/B978-0-12-800094-6.00004-2 PMID: 24783353
  108. Elliott, M.H.; Smith, D.S.; Parker, C.E.; Borchers, C. Current trends in quantitative proteomics. J. Mass Spectrom., 2009, 44(12), 1637-1660. PMID: 19957301
  109. Du, C.; Weng, Y.; Lou, J.; Zeng, G.; Liu, X.; Jin, H.; Lin, S.; Tang, L. Isobaric tags for relative and absolute quantitation‑based proteomics reveals potential novel biomarkers for the early diagnosis of acute myocardial infarction within 3h. Int. J. Mol. Med., 2019, 43(5), 1991-2004. doi: 10.3892/ijmm.2019.4137 PMID: 30896787
  110. Wang, Y.; Cong, S.; Zhang, Q.; Li, R.; Wang, K. iTRAQ-based proteomics reveals potential anti-virulence targets for ESBL-producing Klebsiella pneumoniae. Infect. Drug Resist., 2020, 13(1), 2891-2899. doi: 10.2147/IDR.S259894 PMID: 32903891
  111. Wang, Z.; Liu, G.; Jiang, J. Profiling of apoptosis- and autophagy-associated molecules in human lung cancer A549 cells in response to cisplatin treatment using stable isotope labeling with amino acids in cell culture. Int. J. Oncol., 2019, 54(3), 1071-1085. doi: 10.3892/ijo.2019.4690 PMID: 30664195
  112. Hoedt, E.; Zhang, G.; Neubert, T.A. Stable isotope labeling by amino acids in cell culture (SILAC) for quantitative proteomics: Advancements of mass spectrometry in biomedical research, 1st ed; Alisa, G.W., Ed.; Springer: Berlin, 2019, 806, pp. 31-539. doi: 10.1007/978-3-030-15950-4_31
  113. Zhang, H.; Li, X.; Martin, D.B.; Aebersold, R. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol., 2003, 21(6), 660-666. doi: 10.1038/nbt827 PMID: 12754519
  114. Soufi, B.; Macek, B. Stable isotope labeling by amino acids applied to bacterial cell culture.Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC), 1st ed; Bettina, W., Ed.; Springer: Berlin, 2014, Vol. 1188, pp. 9-22. doi: 10.1007/978-1-4939-1142-4_2
  115. Kratchmarova, I. Stable isotope labeling by amino acids in cell culture (SILAC), in 2D PAGE: Sample preparation and fractionation. Mol. Cell. Proteomics, 2008, 1(5), 101-111.
  116. Mann, M. Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol., 2006, 7(12), 952-958. doi: 10.1038/nrm2067 PMID: 17139335
  117. Chen, X.; Wei, S.; Ji, Y.; Guo, X.; Yang, F. Quantitative proteomics using SILAC: Principles, applications, and developments. Proteomics, 2015, 15(18), 3175-3192. doi: 10.1002/pmic.201500108 PMID: 26097186
  118. Boysen, A.; Borch, J.; Krogh, T.J.; Hjernø, K.; Møller-Jensen, J. SILAC-based comparative analysis of pathogenic Escherichia coli secretomes. J. Microbiol. Methods, 2015, 116(1), 66-79. doi: 10.1016/j.mimet.2015.06.015 PMID: 26143086
  119. Zimmer, J.S.D.; Monroe, M.E.; Qian, W.J.; Smith, R.D. Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrom. Rev., 2006, 25(3), 450-482. doi: 10.1002/mas.20071 PMID: 16429408
  120. Zhou, J.Y.; Schepmoes, A.A.; Zhang, X.; Moore, R.J.; Monroe, M.E.; Lee, J.H.; Camp, D.G., II; Smith, R.D.; Qian, W.J.; Improved, L.C. Improved LC-MS/MS spectral counting statistics by recovering low-scoring spectra matched to confidently identified peptide sequences. J. Proteome Res., 2010, 9(11), 5698-5704. doi: 10.1021/pr100508p PMID: 20812748
  121. Li, C.; Xiong, Q.; Zhang, J.; Ge, F.; Bi, L.J. Quantitative proteomic strategies for the identification of microRNA targets. Expert Rev. Proteomics, 2012, 9(5), 549-559. doi: 10.1586/epr.12.49 PMID: 23194271
  122. Phong, T.Q.; Ha, D.T.T.; Volker, U.; Hammer, E. Using a label free quantitative proteomics approach to identify changes in protein abundance in multidrug-resistant Mycobacterium tuberculosis. Indian J. Microbiol., 2015, 55(2), 219-230. doi: 10.1007/s12088-015-0511-2 PMID: 25805910
  123. Minden, J. Comparative proteomics and difference gel electrophoresis. Biotechniques, 2007, 43(6), 739-745, 741, 743 passim. doi: 10.2144/000112653 PMID: 18251249
  124. Burchmore, R. Identification of anti-infective targets through comparative proteomics. Expert Rev. Anti Infect. Ther., 2006, 4(2), 163-165. doi: 10.1586/14787210.4.2.163 PMID: 16597196
  125. Hammami, R.; Zouhir, A.; Ben Hamida, J.; Fliss, I. BACTIBASE: A new web-accessible database for bacteriocin characterization. BMC Microbiol., 2007, 7(1), 89-95. doi: 10.1186/1471-2180-7-89 PMID: 17941971
  126. Ciborowski, P.; Silberring, J. Quantitative measurements in proteomics: Proteomic profiling and analytical chemistry.Elsevier: Amsterdam, 2013, 206, pp. 135-150.
  127. Shiny, M.C.; Madhusudan, I.; Gaurav, I.R.; Shanthi, C. Potential of proteomics to probe microbes. J. Basic Microbiol., 2020, 60(6), 471-483. doi: 10.1002/jobm.201900628 PMID: 32212201
  128. Serpa, J.J.; Parker, C.E.; Petrotchenko, E.V.; Han, J.; Pan, J.; Borchers, C.H. Mass spectrometry-based structural proteomics. Eur. J. Mass Spectrom., 2012, 18(2), 251-267. doi: 10.1255/ejms.1178 PMID: 22641729
  129. Navare, A.T.; Chavez, J.D.; Zheng, C.; Weisbrod, C.R.; Eng, J.K.; Siehnel, R.; Singh, P.K.; Manoil, C.; Bruce, J.E. Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry. Structure, 2015, 23(4), 762-773. doi: 10.1016/j.str.2015.01.022 PMID: 25800553
  130. Leitner, A. Cross-linking and other structural proteomics techniques: How chemistry is enabling mass spectrometry applications in structural biology. Chem. Sci., 2016, 7(8), 4792-4803. doi: 10.1039/C5SC04196A PMID: 30155128
  131. Mahdavi, A.; Szychowski, J.; Ngo, J.T.; Sweredoski, M.J.; Graham, R.L.J.; Hess, S.; Schneewind, O.; Mazmanian, S.K.; Tirrell, D.A. Identification of secreted bacterial proteins by noncanonical amino acid tagging. Proc. Natl. Acad. Sci. USA, 2014, 111(1), 433-438. doi: 10.1073/pnas.1301740111 PMID: 24347637
  132. Barker, C.A.; Farha, M.A.; Brown, E.D. Chemical genomic approaches to study model microbes. Chem. Biol., 2010, 17(6), 624-632. doi: 10.1016/j.chembiol.2010.05.010 PMID: 20609412
  133. Levine, S.R.; Beatty, K.E. Investigating β-lactam drug targets in Mycobacterium tuberculosis using chemical probes. ACS Infect. Dis., 2021, 7(2), 461-470. doi: 10.1021/acsinfecdis.0c00809 PMID: 33470787
  134. Baker, Y.R.; Hodgkinson, J.T.; Florea, B.I.; Alza, E.; Galloway, W.R.J.D.; Grimm, L.; Geddis, S.M.; Overkleeft, H.S.; Welch, M.; Spring, D.R. Identification of new quorum sensing autoinducer binding partners in Pseudomonas aeruginosa using photoaffinity probes. Chem. Sci., 2017, 8(11), 7403-7411. doi: 10.1039/C7SC01270E PMID: 29163891
  135. Head, S.A.; Liu, J.O. Identification of small molecule-binding proteins in a native cellular environment by live-cell photoaffinity labeling. J. Vis. Exp., 2016, 115(115), 1-9. doi: 10.3791/54529 PMID: 27684515
  136. Chuang, V.; Otagiri, M. Photoaffinity labeling of plasma proteins. Molecules, 2013, 18(11), 13831-13859. doi: 10.3390/molecules181113831 PMID: 24217326
  137. Maurya, S.; Akhtar, S.; Siddiqui, M.H.; Khan, M.K.A. Subtractive proteomics for identification of drug targets in bacterial pathogens: A review. Int. J. Eng. Technol., 2020, 9(1), 262-273.
  138. Solanki, V.; Tiwari, V. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Sci. Rep., 2018, 8(1), 9044. doi: 10.1038/s41598-018-26689-7 PMID: 29899345
  139. Lowe, R.; Shirley, N.; Bleackley, M.; Dolan, S.; Shafee, T. Transcriptomics technologies. PLOS Comput. Biol., 2017, 13(5), e1005457. doi: 10.1371/journal.pcbi.1005457 PMID: 28545146
  140. Russo, G.; Zegar, C.; Giordano, A. Advantages and limitations of microarray technology in human cancer. Oncogene, 2003, 22(42), 6497-6507. doi: 10.1038/sj.onc.1206865 PMID: 14528274
  141. Jaluria, P.; Konstantopoulos, K.; Betenbaugh, M.; Shiloach, J. A perspective on microarrays: Current applications, pitfalls, and potential uses. Microb. Cell Fact., 2007, 6(1), 4. doi: 10.1186/1475-2859-6-4 PMID: 17254338
  142. Dennis, P.; Edwards, E.A.; Liss, S.N.; Fulthorpe, R. Monitoring gene expression in mixed microbial communities by using DNA microarrays. Appl. Environ. Microbiol., 2003, 69(2), 769-778. doi: 10.1128/AEM.69.2.769-778.2003 PMID: 12570994
  143. Zhang, Q.; Hu, Y.; Wei, P.; Shi, L.; Shi, L.; Li, J.; Zhao, Y.; Chen, Y.; Zhang, X.; Ye, F.; Liu, X.; Lin, S. Identification of hub genes for adult patients with sepsis via RNA sequencing. Sci. Rep., 2022, 12(1), 5128. doi: 10.1038/s41598-022-09175-z PMID: 34992227
  144. Febrer, M.; McLay, K.; Caccamo, M.; Twomey, K.B.; Ryan, R.P. Advances in bacterial transcriptome and transposon insertion-site profiling using second-generation sequencing. Trends Biotechnol., 2011, 29(11), 586-594. doi: 10.1016/j.tibtech.2011.06.004 PMID: 21764162
  145. Kogenaru, S.; Yan, Q.; Guo, Y.; Wang, N. RNA-seq and microarray complement each other in transcriptome profiling. BMC Genomics, 2012, 13(1), 629. doi: 10.1186/1471-2164-13-629 PMID: 23153100
  146. Alonso, A.; Marsal, S.; JuliÃ, A. Analytical methods in untargeted metabolomics: State of the art in 2015. Front. Bioeng. Biotechnol., 2015, 3(1), 23-43. doi: 10.3389/fbioe.2015.00023 PMID: 25798438
  147. da Cunha, B.R.; Zoio, P.; Fonseca, L.P.; Calado, C.R.C. Technologies for high-throughput identification of antibiotic mechanism of action. Antibiotics, 2021, 10(5), 565-585. doi: 10.3390/antibiotics10050565 PMID: 34065815
  148. Scalbert, A.; Brennan, L.; Fiehn, O.; Hankemeier, T.; Kristal, B.S.; van Ommen, B.; Pujos-Guillot, E.; Verheij, E.; Wishart, D.; Wopereis, S. Mass-spectrometry-based metabolomics: Limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics, 2009, 5(4), 435-458. doi: 10.1007/s11306-009-0168-0 PMID: 20046865
  149. Jump, R.L.P.; Polinkovsky, A.; Hurless, K.; Sitzlar, B.; Eckart, K.; Tomas, M.; Deshpande, A.; Nerandzic, M.M.; Donskey, C.J. Metabolomics analysis identifies intestinal microbiota-derived biomarkers of colonization resistance in clindamycin-treated mice. PLoS One, 2014, 9(7), e101267. doi: 10.1371/journal.pone.0101267 PMID: 24988418
  150. Maček, B.; Carpy, A.; Koch, A.; Bicho, C.C.; Borek, W.E.; Hauf, S.; Sawin, K.E. Stable isotope labeling by amino acids in cell culture (SILAC) technology in fission yeast. Cold Spring Harb. Protoc., 2017, 2017(6), pdb.top079814. doi: 10.1101/pdb.top079814 PMID: 28572211
  151. Deng, J.; Erdjument-Bromage, H.; Neubert, T.A.; Quan, M. B. titative comparison of proteomes using SILAC. Curr. Protoc. Protein Sci., 2019, 95(1), e74. doi: 10.1002/cpps.74 PMID: 30238645
  152. Zhu, W.; Smith, J.W.; Huang, C.-M. Mass spectrometry-based label-free quantitative proteomics. J. Biotechnol. Biomed., 2009, 2010(1), 1-6.
  153. Asara, J.M.; Christofk, H.R.; Freimark, L.M.; Cantley, L.C. A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen. Proteomics, 2008, 8(5), 994-999. doi: 10.1002/pmic.200700426 PMID: 18324724
  154. Neilson, K.A.; Ali, N.A.; Muralidharan, S.; Mirzaei, M.; Mariani, M.; Assadourian, G.; Lee, A.; van Sluyter, S.C.; Haynes, P.A. Less label, more free: Approaches in label-free quantitative mass spectrometry. Proteomics, 2011, 11(4), 535-553. doi: 10.1002/pmic.201000553 PMID: 21243637
  155. Renaud, J.B.; Sabourin, L.; Topp, E.; Sumarah, M.W. Spectral counting approach to measure selectivity of high-resolution LC–MS methods for environmental analysis. Anal. Chem., 2017, 89(5), 2747-2754. doi: 10.1021/acs.analchem.6b03475 PMID: 28194977
  156. Rappsilber, J.; Ryder, U.; Lamond, A.I.; Mann, M. Large-scale proteomic analysis of the human spliceosome. Genome Res., 2002, 12(8), 1231-1245. doi: 10.1101/gr.473902 PMID: 12176931
  157. Ishihama, Y.; Oda, Y.; Tabata, T.; Sato, T.; Nagasu, T.; Rappsilber, J.; Mann, M. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell. Proteomics, 2005, 4(9), 1265-1272. doi: 10.1074/mcp.M500061-MCP200 PMID: 15958392
  158. Lu, P.; Vogel, C.; Wang, R.; Yao, X.; Marcotte, E.M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol., 2007, 25(1), 117-124. doi: 10.1038/nbt1270 PMID: 17187058
  159. Chandramouli, K.; Qian, P.Y. Proteomics: Challenges, techniques and possibilities to overcome biological sample complexity. Hum. Genomics Proteomics, 2009, 1(1), 1-22. doi: 10.4061/2009/239204 PMID: 20948568
  160. Haqqani, A.S.; Kelly, J.F.; Stanimirovic, D.B. Quantitative protein profiling by mass spectrometry using isotope-coded affinity tags. In: Genomics Protocols, 1st ed; Mike, S., Ed.; Springer: Berlin, 2008; 439, pp. 225-240. doi: 10.1007/978-1-59745-188-8_16
  161. Yamamoto, S.; Ishihara, T. Resolution and retention of proteins near isoelectric points in ion-exchange chromatography. Molecular recognition in electrostatic interaction chromatography. Sep. Sci. Technol., 2000, 35(11), 1707-1717. doi: 10.1081/SS-100102489
  162. Rosenberg, I.M. Protein analysis and purification: Benchtop techniques. Springer Science & Business Media: Berlin, 2013.
  163. Piras, C.; Soggiu, A.; Bonizzi, L.; Gaviraghi, A.; Deriu, F.; De Martino, L.; Iovane, G.; Amoresano, A.; Roncada, P. Comparative proteomics to evaluate multi drug resistance in Escherichia coli. Mol. Biosyst., 2012, 8(4), 1060-1067. doi: 10.1039/C1MB05385J PMID: 22120138
  164. Petrotchenko, E.V.; Serpa, J.J.; Borchers, C.H. Cross-linking applications in structural proteomics: Proteomics for biological discovery.Veenstra, 2019, 548, pp. 175-196. doi: 10.1002/9781119081661.ch7
  165. Subbotin, R.I.; Chait, B.T. A pipeline for determining protein-protein interactions and proximities in the cellular milieu. Mol. Cell. Proteomics, 2014, 13(11), 2824-2835. doi: 10.1074/mcp.M114.041095 PMID: 25172955
  166. Petrotchenko, E.V.; Borchers, C.H. Crosslinking combined with mass spectrometry for structural proteomics. Mass Spectrom. Rev., 2010, 29(6), 862-876. doi: 10.1002/mas.20293 PMID: 20730915
  167. Götze, M.; Iacobucci, C.; Ihling, C.H.; Sinz, A. A simple cross-linking/mass spectrometry workflow for studying system-wide protein interactions. Anal. Chem., 2019, 91(15), 10236-10244. doi: 10.1021/acs.analchem.9b02372 PMID: 31283178
  168. Mendoza, V.L.; Vachet, R.W. Probing protein structure by amino acid-specific covalent labeling and mass spectrometry. Mass Spectrom. Rev., 2009, 28(5), 785-815. doi: 10.1002/mas.20203 PMID: 19016300
  169. Liuni, P.; Zhu, S.; Wilson, D.J. Oxidative protein labeling with analysis by mass spectrometry for the study of structure, folding, and dynamics. Antioxid. Redox Signal., 2014, 21(3), 497-510. doi: 10.1089/ars.2014.5850 PMID: 24512178
  170. Chen, X.; Wang, Y.; Ma, N.; Tian, J.; Shao, Y.; Zhu, B.; Wong, Y.K.; Liang, Z.; Zou, C.; Wang, J. Target identification of natural medicine with chemical proteomics approach: Probe synthesis, target fishing and protein identification. Signal Transduct. Target. Ther., 2020, 5(1), 72. doi: 10.1038/s41392-020-0186-y PMID: 32435053
  171. Piazza, I.; Beaton, N.; Bruderer, R.; Knobloch, T.; Barbisan, C.; Chandat, L.; Sudau, A.; Siepe, I.; Rinner, O.; de Souza, N.; Picotti, P.; Reiter, L. A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes. Nat. Commun., 2020, 11(1), 4200. doi: 10.1038/s41467-020-18071-x PMID: 32826910
  172. Rix, U.; Superti-Furga, G. Target profiling of small molecules by chemical proteomics. Nat. Chem. Biol., 2009, 5(9), 616-624. doi: 10.1038/nchembio.216 PMID: 19690537
  173. Deng, H.; Lei, Q.; Wu, Y.; He, Y.; Li, W. Activity-based protein profiling: Recent advances in medicinal chemistry. Eur. J. Med. Chem., 2020, 191(1), 112151-112219. doi: 10.1016/j.ejmech.2020.112151 PMID: 32109778
  174. Kok, B.P.; Ghimire, S.; Kim, W.; Chatterjee, S.; Johns, T.; Kitamura, S.; Eberhardt, J.; Ogasawara, D.; Xu, J.; Sukiasyan, A.; Kim, S.M.; Godio, C.; Bittencourt, J.M.; Cameron, M.; Galmozzi, A.; Forli, S.; Wolan, D.W.; Cravatt, B.F.; Boger, D.L.; Saez, E. Discovery of small- molecule enzyme activators by activity-based protein profiling. Nat. Chem. Biol., 2020, 16(9), 997-1005. doi: 10.1038/s41589-020-0555-4 PMID: 32514184
  175. Wang, S.; Tian, Y.; Wang, M.; Wang, M.; Sun, G.; Sun, X. Advanced activity-based protein profiling application strategies for drug development. Front. Pharmacol., 2018, 9(1), 353-362. doi: 10.3389/fphar.2018.00353 PMID: 29686618
  176. Fonović, M.; Bogyo, M. Activity-based probes as a tool for functional proteomic analysis of proteases. Expert Rev. Proteomics, 2008, 5(5), 721-730. doi: 10.1586/14789450.5.5.721 PMID: 18937562
  177. Yang, Y.; Yang, X.; Verhelst, S. Comparative analysis of click chemistry mediated activity-based protein profiling in cell lysates. Molecules, 2013, 18(10), 12599-12608. doi: 10.3390/molecules181012599 PMID: 24126377
  178. Smith, E.; Collins, I. Photoaffinity labeling in target- and binding-site identification. Future Med. Chem., 2015, 7(2), 159-183. doi: 10.4155/fmc.14.152 PMID: 25686004
  179. Burton, N.R.; Kim, P.; Backus, K.M. Photoaffinity labelling strategies for mapping the small molecule–protein interactome. Org. Biomol. Chem., 2021, 19(36), 7792-7809. doi: 10.1039/D1OB01353J PMID: 34549230
  180. Geoghegan, K.F.; Johnson, D.S. Chemical proteomic technologies for drug target identification. In: Annual Reports in Medicinal Chemistry, 2nd ed; John, E.M. Elsevier: Amsterdam, 2010; 45, pp. 345-360. doi: 10.1016/S0065-7743(10)45021-6
  181. Robinette, D.; Neamati, N.; Tomer, K.B.; Borchers, C.H. Photoaffinity labeling combined with mass spectrometric approaches as a tool for structural proteomics. Expert Rev. Proteomics, 2006, 3(4), 399-408. doi: 10.1586/14789450.3.4.399 PMID: 16901199
  182. Lin, J. Development of photoaffinity probes to identify protein-protein interactions and map binding regions.2019, doi: 10.5353/th_991044146571003414
  183. Huang, Y.; Niu, B.; Gao, Y.; Fu, L.; Li, W. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics, 2010, 26(5), 680-682. doi: 10.1093/bioinformatics/btq003 PMID: 20053844
  184. Jordan, I.K.; Rogozin, I.B.; Wolf, Y.I.; Koonin, E.V. Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res., 2002, 12(6), 962-968. doi: 10.1101/gr.87702 PMID: 12045149
  185. Zhang, R.; Ou, H.Y.; Zhang, C.T. DEG: A database of essential genes. Nucleic Acids Res., 2004, 32(90001), 271D-272. doi: 10.1093/nar/gkh024 PMID: 14681410
  186. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol., 1990, 215(3), 403-410. doi: 10.1016/S0022-2836(05)80360-2 PMID: 2231712
  187. Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A.C.; Wishart, D.S. DrugBank 3.0: A comprehensive resource for ‘Omics’ research on drugs. Nucleic Acids Res., 2011, 39(Database), D1035-D1041. doi: 10.1093/nar/gkq1126 PMID: 21059682
  188. Chen, L.; Yang, J.; Yu, J.; Yao, Z.; Sun, L.; Shen, Y.; Jin, Q. VFDB: A reference database for bacterial virulence factors. Nucleic Acids Res., 2004, 33(Database issue), D325-D328. doi: 10.1093/nar/gki008 PMID: 15608208
  189. Apweiler, R.; Bairoch, A.; Wu, C.H.; Barker, W.C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M.; Martin, M.J.; Natale, D.A.; O’Donovan, C.; Redaschi, N.; Yeh, L.S. UniProt: The universal protein knowledgebase. Nucleic Acids Res., 2004, 32(90001), 115D-119. doi: 10.1093/nar/gkh131 PMID: 14681372
  190. Yang, X.; Kui, L.; Tang, M.; Li, D.; Wei, K.; Chen, W.; Miao, J.; Dong, Y. High-throughput transcriptome profiling in drug and biomarker discovery. Front. Genet., 2020, 11(1), 19-31. doi: 10.3389/fgene.2020.00019 PMID: 32117438
  191. Singh, A.; Kumar, N. A review on DNA microarray technology. Int. J. Curr. Res. Rev., 2013, 5(22), 1-5.
  192. Eijkelkamp, B.A.; Hassan, K.A.; Paulsen, I.T.; Brown, M.H. Investigation of the human pathogen Acinetobacter baumannii under iron limiting conditions. BMC Genomics, 2011, 12(1), 126. doi: 10.1186/1471-2164-12-126 PMID: 21342532
  193. LaBauve, A.E.; Wargo, M.J. Detection of host-derived sphingosine by Pseudomonas aeruginosa is important for survival in the murine lung. PLoS Pathog., 2014, 10(1), e1003889. doi: 10.1371/journal.ppat.1003889 PMID: 24465209
  194. Bischler, T.; Tan, H.S.; Nieselt, K.; Sharma, C.M. Differential RNA-seq (dRNA-seq) for annotation of transcriptional start sites and small RNAs in Helicobacter pylori. Methods, 2015, 86, 89-101. doi: 10.1016/j.ymeth.2015.06.012 PMID: 26091613
  195. Popella, L.; Jung, J.; Popova, K.; Ðurica-Mitić, S.; Barquist, L.; Vogel, J. Global RNA profiles show target selectivity and physiological effects of peptide-delivered antisense antibiotics. Nucleic Acids Res., 2021, 49(8), 4705-4724. doi: 10.1093/nar/gkab242 PMID: 33849070
  196. Futamura, Y.; Muroi, M.; Osada, H. Target identification of small molecules based on chemical biology approaches. Mol. Biosyst., 2013, 9(5), 897-914. doi: 10.1039/c2mb25468a PMID: 23354001
  197. Alarcon-Barrera, J.C.; Kostidis, S.; Ondo-Mendez, A.; Giera, M. Recent advances in metabolomics analysis for early drug development. Drug Discov. Today, 2022, 27(6), 1763-1773. doi: 10.1016/j.drudis.2022.02.018 PMID: 35218927
  198. Rabinowitz, J.; Purdy, J.; Vastag, L.; Shenk, T.; Koyuncu, E. Metabolomics in drug target discovery. Cold Spring Harb. Symp., 2011, 76(1), 235-246.
  199. Aretz, I.; Meierhofer, D. Advantages and pitfalls of mass spectrometry-based metabolome profiling in systems biology. Int. J. Mol. Sci., 2016, 17(5), 632-646. doi: 10.3390/ijms17050632 PMID: 27128910
  200. Lakin, S.M.; Dean, C.; Noyes, N.R.; Dettenwanger, A.; Ross, A.S.; Doster, E.; Rovira, P.; Abdo, Z.; Jones, K.L.; Ruiz, J.; Belk, K.E.; Morley, P.S.; Boucher, C. MEGARes: An antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res., 2017, 45(D1), D574-D580. doi: 10.1093/nar/gkw1009 PMID: 27899569
  201. Kushwaha, S.K.; Shakya, M. Protein interaction network analysis-approach for potential drug target identification in mycobacterium tuberculosis. J. Theor. Biol., 2010, 262(2), 284-294. doi: 10.1016/j.jtbi.2009.09.029 PMID: 19833135
  202. Wishart, D.S.; Wu, A. Using drug bank for in silico drug exploration and discovery. Curr. Protoc. Bioinform., 2016, 54(1), 1. doi: 10.1002/cpbi.1
  203. Zhu, F.; Shi, Z.; Qin, C.; Tao, L.; Liu, X.; Xu, F.; Zhang, L.; Song, Y.; Liu, X.; Zhang, J.; Han, B.; Zhang, P.; Chen, Y. Therapeutic target database update 2012: A resource for facilitating target-oriented drug discovery. Nucleic Acids Res., 2012, 40(D1), D1128-D1136. doi: 10.1093/nar/gkr797 PMID: 21948793
  204. Damte, D.; Suh, J.-W.; Lee, S.-J.; Yohannes, S.B.; Hossain, M.A.; Park, S.-C. Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae. Genomics, 2013, 2013, 11. doi: 10.1016/j.ygeno.2013.04.011
  205. Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213. doi: 10.1093/nar/gkv951 PMID: 26400175
  206. Forst, C.V. Host-pathogen systems biology. In: Infectious Disease Informatics, 1st ed; Vitali, S. Springer: Berlin, 2010, Vol. 367, pp. 123-147. doi: 10.1007/978-1-4419-1327-2_6
  207. C. Activities at the universal protein resource (UniProt). Nucleic Acids Res., 2014, 42(D1), D191-D198. doi: 10.1093/nar/gkt1140
  208. Hecker, N.; Ahmed, J.; von Eichborn, J.; Dunkel, M.; Macha, K.; Eckert, A.; Gilson, M.K.; Bourne, P.E.; Preissner, R. SuperTarget goes quantitative: Update on drug-target interactions. Nucleic Acids Res., 2012, 40(D1), D1113-D1117. doi: 10.1093/nar/gkr912 PMID: 22067455
  209. Kalathur, R.K.R.; Pinto, J.P.; Hernández-Prieto, M.A.; Machado, R.S.R.; Almeida, D.; Chaurasia, G.; Futschik, M.E. UniHI 7: An enhanced database for retrieval and interactive analysis of human molecular interaction networks. Nucleic Acids Res., 2014, 42(D1), D408-D414. doi: 10.1093/nar/gkt1100 PMID: 24214987
  210. Mazandu, G.K.; Mulder, N.J. Generation and analysis of large-scale data-driven Mycobacterium tuberculosis functional networks for drug target identification. Adv. Bioinforma., 2011, 2011(1), 1-14. doi: 10.1155/2011/801478 PMID: 22190924
  211. Zhang, G.; Wang, H.; Zhu, K.; Yang, Y.; Li, J.; Jiang, H.; Liu, Z. Investigation of candidate molecular biomarkers for expression profile analysis of the Gene expression omnibus (GEO) in acute lymphocytic leukemia (ALL). Biomed. Pharmacother., 2019, 120(1), 109530-109540. doi: 10.1016/j.biopha.2019.109530 PMID: 31606621
  212. Agüero, F.; Al-Lazikani, B.; Aslett, M.; Berriman, M.; Buckner, F.S.; Campbell, R.K.; Carmona, S.; Carruthers, I.M.; Chan, A.W.E.; Chen, F.; Crowther, G.J.; Doyle, M.A.; Hertz-Fowler, C.; Hopkins, A.L.; McAllister, G.; Nwaka, S.; Overington, J.P.; Pain, A.; Paolini, G.V.; Pieper, U.; Ralph, S.A.; Riechers, A.; Roos, D.S.; Sali, A.; Shanmugam, D.; Suzuki, T.; Van Voorhis, W.C.; Verlinde, C.L.M.J. Genomic-scale prioritization of drug targets: The TDR targets database. Nat. Rev. Drug Discov., 2008, 7(11), 900-907. doi: 10.1038/nrd2684 PMID: 18927591
  213. Kuhn, M.; Szklarczyk, D.; Pletscher-Frankild, S.; Blicher, T.H.; von Mering, C.; Jensen, L.J.; Bork, P. STITCH 4: Integration of protein–chemical interactions with user data. Nucleic Acids Res., 2014, 42(D1), D401-D407. doi: 10.1093/nar/gkt1207 PMID: 24293645
  214. Rosenthal, A.; Gabrielian, A.; Engle, E.; Hurt, D.E.; Alexandru, S.; Crudu, V.; Sergueev, E.; Kirichenko, V.; Lapitskii, V.; Snezhko, E.; Kovalev, V.; Astrovko, A.; Skrahina, A.; Taaffe, J.; Harris, M.; Long, A.; Wollenberg, K.; Akhundova, I.; Ismayilova, S.; Skrahin, A.; Mammadbayov, E.; Gadirova, H.; Abuzarov, R.; Seyfaddinova, M.; Avaliani, Z.; Strambu, I.; Zaharia, D.; Muntean, A.; Ghita, E.; Bogdan, M.; Mindru, R.; Spinu, V.; Sora, A.; Ene, C.; Vashakidze, S.; Shubladze, N.; Nanava, U.; Tuzikov, A.; Tartakovsky, M. The TB portals: An open-access, web- based platform for global drug-resistant-tuberculosis data sharing and analysis. J. Clin. Microbiol., 2017, 55(11), 3267-3282. doi: 10.1128/JCM.01013-17 PMID: 28904183
  215. Gao, Z.; Li, H.; Zhang, H.; Liu, X.; Kang, L.; Luo, X.; Zhu, W.; Chen, K.; Wang, X.; Jiang, H. PDTD: A web-accessible protein database for drug target identification. BMC Bioinformatics, 2008, 9(1), 104-111. doi: 10.1186/1471-2105-9-104 PMID: 18282303
  216. Loots, D.T. An altered mycobacterium tuberculosis metabolome induced by katG mutations resulting in isoniazid resistance. Antimicrob. Agents Chemother., 2014, 58(4), 2144-2149. doi: 10.1128/AAC.02344-13 PMID: 24468786
  217. Bansal, P.; Arora, M.; Gupta, V.; Maithani, M. Bioinformatics-based tools and software in clinical research: A new emerging area. In: Bioinformatics and Drug Discovery, 1st ed; Richard, S.L., Ed.; Springer: New York, 2019; Vol. 1939, pp. 215-230. doi: 10.1007/978-1-4939-9089-4_12
  218. Hammami, R.; Fliss, I. Current trends in antimicrobial agent research: Chemo- and bioinformatics approaches. Drug Discov. Today, 2010, 15(13-14), 540-546. doi: 10.1016/j.drudis.2010.05.002 PMID: 20546918
  219. Mandal, R.S.; Das, S. In silico approaches toward combating antibiotic resistance. In: Drug Resistance in Bacteria, Fungi, Malaria, and Cancer, 2nd ed; Gunjan, A., Ed.; Springer: Berlin, 2017; Vol. 369, pp. 577-593. doi: 10.1007/978-3-319-48683-3_25
  220. Merigueti, T.C.; Carneiro, M.W.; Carvalho-Assef, A.P.D.A.; Silva-Jr, F.P.; Silva, F.A.B. FindTargetsWeb: A user-friendly tool for identification of potential therapeutic targets in metabolic networks of bacteria. Front. Genet., 2019, 10(1), 633-647. doi: 10.3389/fgene.2019.00633 PMID: 31333719
  221. Chanumolu, S.K.; Rout, C.; Chauhan, R.S. UniDrug-target: A computational tool to identify unique drug targets in pathogenic bacteria. PLoS One, 2012, 7(3), e32833. doi: 10.1371/journal.pone.0032833 PMID: 22431985
  222. Gupta, R.; Pradhan, D.; Jain, A.K.; Rai, C.S. TiD: Standalone software for mining putative drug targets from bacterial proteome. Genomics, 2017, 109(1), 51-57. doi: 10.1016/j.ygeno.2016.11.005 PMID: 27856224
  223. Nayak, S.; Pradhan, D.; Singh, H.; Reddy, M.S. Computational screening of potential drug targets for pathogens causing bacterial pneumonia. Microb. Pathog., 2019, 130(1), 271-282. doi: 10.1016/j.micpath.2019.03.024 PMID: 30914386
  224. Sudha, R.; Prasad, P. Dtar-Finder: Program for drug target identification and characterization in bacteria. Bioinformation, 2019, 15(3), 209-213. doi: 10.6026/97320630015209 PMID: 31354197
  225. Tang, Y.; Zhu, W.; Chen, K.; Jiang, H. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discov. Today. Technol., 2006, 3(3), 307-313. doi: 10.1016/j.ddtec.2006.09.004 PMID: 24980533
  226. Li, H.; Gao, Z.; Kang, L.; Zhang, H.; Yang, K.; Yu, K.; Luo, X.; Zhu, W.; Chen, K.; Shen, J.; Wang, X.; Jiang, H. TarFisDock: A web server for identifying drug targets with docking approach. Nucleic Acids Res., 2006, 34(Web Server), W219-W224. doi: 10.1093/nar/gkl114 PMID: 16844997
  227. Zhang, S.; Lu, W.; Liu, X.; Diao, Y.; Bai, F.; Wang, L.; Shan, L.; Huang, J.; Li, H.; Zhang, W. Fast and effective identification of the bioactive compounds and their targets from medicinal plants via computational chemical biology approach. Med. Chem. Comm.,2011, 2(6), 471-477. doi: 10.1039/C0MD00245C
  228. Li, H.; Zheng, M.; Luo, X.; Zhu, W.; Jiang, H. Computational Approaches to Drug Discovery and Development. Chemical Biology: Approaches to Drug Discovery and Development to Targeting Disease, 1st ed; Natanya CIVJAN. Wiley: New York, 2012, pp.23-40. doi: 10.1002/9781118435762
  229. Kim, S.S.; Aprahamian, M.L.; Lindert, S. Improving inverse docking target identification with Z-score selection. Chem. Biol. Drug Des.,2019, 93(6), 1105-1116. doi: 10.1111/cbdd.13453
  230. Kumar, A.; Thotakura, P.L.; Tiwary, B.K.; Krishna, R. Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host- pathogen protein-protein interactions. BMC Microbiol., 2016, 16(1), 84-96. doi: 10.1186/s12866-016-0700-0 PMID: 27176600
  231. Gupta, S.K.; Padmanabhan, B.R.; Diene, S.M.; Lopez-Rojas, R.; Kempf, M.; Landraud, L.; Rolain, J.M. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother., 2014, 58(1), 212-220. doi: 10.1128/AAC.01310-13 PMID: 24145532
  232. Yu, C.S.; Lin, C.J.; Hwang, J.K. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n -peptide compositions. Protein Sci., 2004, 13(5), 1402-1406. doi: 10.1110/ps.03479604 PMID: 15096640
  233. Shao, Y.; He, X.; Harrison, E.M.; Tai, C.; Ou, H.Y.; Rajakumar, K.; Deng, Z. mGenomeSubtractor: A web-based tool for parallel in silico subtractive hybridization analysis of multiple bacterial genomes. Nucleic Acids Res., 2010, 38(Suppl. 2), W194-W200. doi: 10.1093/nar/gkq326 PMID: 20435682
  234. Krogh, A.; Larsson, B.; von Heijne, G.; Sonnhammer, E.L.L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes11Edited by F. Cohen. J. Mol. Biol., 2001, 305(3), 567-580. doi: 10.1006/jmbi.2000.4315 PMID: 11152613
  235. Demchenko, Y.; Turkmen, F.; de Laat, C.; Hsu, C-H.; Blanchet, C.; Loomis, C. Cloud computing infrastructure for data intensive applications. In: Big Data Analytics for Sensor-Network Collected Intelligence, 1st ed; Hui-Huang, H., Ed.; Elsevier: Amsterdam, 2017; Vol. 429, pp. 21-62. doi: 10.1016/B978-0-12-809393-1.00002-7
  236. Parmar, K.M.; Gaikwad, S.L.; Dhakephalkar, P.K.; Kothari, R.; Singh, R.P. Intriguing interaction of bacteriophage-host association: An understanding in the era of omics. Front. Microbiol., 2017, 8(1), 559-665. doi: 10.3389/fmicb.2017.00559 PMID: 28439260
  237. Azam, A.H.; Tanji, Y. Bacteriophage-host arm race: An update on the mechanism of phage resistance in bacteria and revenge of the phage with the perspective for phage therapy. Appl. Microbiol. Biotechnol., 2019, 103(5), 2121-2131. doi: 10.1007/s00253-019-09629-x PMID: 30680434
  238. De Smet, J.; Hendrix, H.; Blasdel, B.G.; Danis-Wlodarczyk, K.; Lavigne, R. Pseudomonas predators: Understanding and exploiting phage–host interactions. Nat. Rev. Microbiol., 2017, 15(9), 517-530. doi: 10.1038/nrmicro.2017.61 PMID: 28649138
  239. Wan, X.; Hendrix, H.; Skurnik, M.; Lavigne, R. Phage-based target discovery and its exploitation towards novel antibacterial molecules. Curr. Opin. Biotechnol., 2021, 68, 1-7. doi: 10.1016/j.copbio.2020.08.015 PMID: 33007632
  240. Liu, J.; Dehbi, M.; Moeck, G.; Arhin, F.; Bauda, P.; Bergeron, D.; Callejo, M.; Ferretti, V.; Ha, N.; Kwan, T.; McCarty, J.; Srikumar, R.; Williams, D.; Wu, J.J.; Gros, P.; Pelletier, J.; DuBow, M. Antimicrobial drug discovery through bacteriophage genomics. Nat. Biotechnol., 2004, 22(2), 185-191. doi: 10.1038/nbt932 PMID: 14716317
  241. Dehbi, M.; Moeck, G.; Arhin, F.F.; Bauda, P.; Bergeron, D.; Kwan, T.; Liu, J.; McCarty, J.; DuBow, M.; Pelletier, J. Inhibition of transcription in Staphylococcus aureus by a primary sigma factor-binding polypeptide from phage G1. J. Bacteriol., 2009, 191(12), 3763-3771. doi: 10.1128/JB.00241-09 PMID: 19376864
  242. Wagemans, J.; Delattre, A.S.; Uytterhoeven, B.; De Smet, J.; Cenens, W.; Aertsen, A.; Ceyssens, P.J.; Lavigne, R. Antibacterial phage ORFans of Pseudomonas aeruginosa phage LUZ24 reveal a novel MvaT inhibiting protein. Front. Microbiol., 2015, 6(1), 1242-1252. doi: 10.3389/fmicb.2015.01242 PMID: 26594207
  243. Van den Bossche, A.; Ceyssens, P.J.; De Smet, J.; Hendrix, H.; Bellon, H.; Leimer, N.; Wagemans, J.; Delattre, A.S.; Cenens, W.; Aertsen, A.; Landuyt, B.; Minakhin, L.; Severinov, K.; Noben, J.P.; Lavigne, R. Systematic identification of hypothetical bacteriophage proteins targeting key protein complexes of Pseudomonas aeruginosa. J. Proteome Res., 2014, 13(10), 4446-4456. doi: 10.1021/pr500796n PMID: 25185497
  244. Klambauer, G.; Hochreiter, S.; Rarey, M. Machine learning in drug discovery. J. Chem. Inf. Model., 2019, 59(3), 945-946. doi: 10.1021/acs.jcim.9b00136 PMID: 30905159
  245. Ding, Y.; Tang, J.; Guo, F. Identification of drug–target interactions via fuzzy bipartite local model. Neural Comput. Appl., 2020, 32(14), 10303-10319. doi: 10.1007/s00521-019-04569-z
  246. Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via multiple information integration. Inf. Sci., 2017, 418-419, 546-560. doi: 10.1016/j.ins.2017.08.045
  247. Patel, L.; Shukla, T.; Huang, X.; Ussery, D.W.; Wang, S. Machine learning methods in drug discovery. Molecules, 2020, 25(22), 5277-5294. doi: 10.3390/molecules25225277 PMID: 33198233
  248. Giacobbe, D.R.; Mora, S.; Giacomini, M.; Bassetti, M. Machine learning and multidrug-resistant gram-negative bacteria: An interesting combination for current and future research. Antibiotics, 2020, 9(2), 54-62. doi: 10.3390/antibiotics9020054 PMID: 32023986
  249. Zhang, X.; Acencio, M.L.; Lemke, N. Predicting essential genes and proteins based on machine learning and network topological features: A comprehensive review. Front. Physiol., 2016, 7(1), 75-86. PMID: 27014079
  250. Rifaioglu, A.S.; Atas, H.; Martin, M.J.; Cetin-Atalay, R.; Atalay, V.; Doğan, T. Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases. Brief. Bioinform., 2019, 20(5), 1878-1912. doi: 10.1093/bib/bby061 PMID: 30084866
  251. Cano, G.; Garcia-Rodriguez, M.J; Garcia-Garcia, A.; Perez-Sanchez, H.; Benediktsson, J.A.; Thapa, A.; Barr, A. Automatic selection of descriptors using random forest: Application to drug discovery. Expert Syst. Appl., 2017, 72(1), 151-159. doi: 10.1016/j.eswa.2016.12.008
  252. Heikamp, K.; Bajorath, J. Support vector machines for drug discovery. Expert Opin. Drug Discov., 2014, 9(1), 93-104. doi: 10.1517/17460441.2014.866943 PMID: 24304044
  253. Lounkine, E.; Kutchukian, P.S.; Glick, M. Chemoinformatics for Drug Discovery Beyond Compound Ranking; Chemometric Applications of Naïve Bayesian Models in Drug Discovery, 1st ed; Jürgen, B., Ed.; WILEY: United States, 2013, Vol. 473, pp. 131-148. doi: 10.1002/9781118742785.ch7
  254. Madhukar, N.S.; Khade, P.K.; Huang, L.; Gayvert, K.; Galletti, G.; Stogniew, M.; Allen, J.E.; Giannakakou, P.; Elemento, O. A Bayesian machine learning approach for drug target identification using diverse data types. Nat. Commun., 2019, 10(1), 5221. doi: 10.1038/s41467-019-12928-6 PMID: 31745082
  255. Steinmetz, L.M.; Scharfe, C.; Deutschbauer, A.M.; Mokranjac, D.; Herman, Z.S.; Jones, T.; Chu, A.M.; Giaever, G.; Prokisch, H.; Oefner, P.J.; Davis, R.W. Systematic screen for human disease genes in yeast. Nat. Genet., 2002, 31(4), 400-404. doi: 10.1038/ng929 PMID: 12134146
  256. Lu, Y.; Deng, J.; Rhodes, J.C.; Lu, H.; Lu, L.J. Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus. Comput. Biol. Chem., 2014, 50(1), 29-40. doi: 10.1016/j.compbiolchem.2014.01.011 PMID: 24569026
  257. Najm, M.; Azencott, C-A.; Playe, B.; Stoven, V. Target identification of drug candidates with machine-learning algorithms: How to choose negative examples for training. BioRxiv, 2021, 4(3), 1-12. doi: 10.1101/2021.04.06.438561
  258. Kaiser, T.M.; Burger, P.B. Error tolerance of machine learning algorithms across contemporary biological targets. Molecules, 2019, 24(11), 2115-2132. doi: 10.3390/molecules24112115 PMID: 31167452
  259. Nonejuie, P.; Trial, R.M.; Newton, G.L.; Lamsa, A.; Ranmali Perera, V.; Aguilar, J.; Liu, W.T.; Dorrestein, P.C.; Pogliano, J.; Pogliano, K. Application of bacterial cytological profiling to crude natural product extracts reveals the antibacterial arsenal of Bacillus subtilis. J. Antibiot., 2016, 69(5), 353-361. doi: 10.1038/ja.2015.116 PMID: 26648120
  260. Farha, M.A.; Brown, E.D. Strategies for target identification of antimicrobial natural products. Nat. Prod. Rep., 2016, 33(5), 668-680. doi: 10.1039/C5NP00127G PMID: 26806527
  261. Nonejuie, P.; Burkart, M.; Pogliano, K.; Pogliano, J. Bacterial cytological profiling rapidly identifies the cellular pathways targeted by antibacterial molecules. Proc. Natl. Acad. Sci., 2013, 110(40), 16169-16174. doi: 10.1073/pnas.1311066110 PMID: 24046367
  262. Wong, W.R.; Oliver, A.G.; Linington, R.G. Development of antibiotic activity profile screening for the classification and discovery of natural product antibiotics. Chem. Biol., 2012, 19(11), 1483-1495. doi: 10.1016/j.chembiol.2012.09.014 PMID: 23177202
  263. Duay, S.A. Influence of local pH environment and Zn (II) on the Structure of the Antimicrobial Peptide clavanin A and its Dynamics with different membrane models in MD Simulations, PhD Thesis, University of Connecticut, Storr, 2020.

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