Recent Advances and Techniques for Identifying Novel Antibacterial Targets

Adila      Nazli; Jingyi      Qiu; Ziyi      Tang; Yun      He
doi:10.2174/0929867330666230123143458
Abstract

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.
Keywords: Antibacterial, omics, machine learning, bioinformatics, drug target, bacterial cytological profiling.
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[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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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 pillar[5]arene for targeted antibiotic delivery against intracellular methicillin-resistant S. aureus. Mater. Chem. Front.,  2022, 6(3), 360-367.
 [http://dx.doi.org/10.1039/D1QM01459E]

[6]
Peng, H.; Xie, B.; Yang, X.; Dai, J.; Wei, G.; He, Y. Pillar[5]arene-based, dual pH and enzyme responsive supramolecular vesicles for targeted antibiotic delivery against intracellular MRSA. Chem. Commun.,  2020, 56(58), 8115-8118.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/10.3791/54529] [PMID: 27684515]

[136]
Chuang, V.; Otagiri, M. Photoaffinity labeling of plasma proteins. Molecules,  2013, 18(11), 13831-13859.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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, 
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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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DOI https://dx.doi.org/10.2174/0929867330666230123143458	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
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