FAS-II pathway targeted integrative deep learning based identification of potential anti-tubercular agents

Sharma A, Vadodariya PK, Vaddoriya VN, Dhameliya TM (2025) Comprehensive updates on antitubercular endeavors identified in 2023. Synlett 36:2393–2410. https://doi.org/10.1055/a-2595-8032

Patel KI, Saha N, Dhameliya TM, Chakraborti AK (2025) Recent advancements in the quest of Benzazoles as anti-Mycobacterium tuberculosis agents. Bioorg Chem 155:108093. https://doi.org/10.1016/j.bioorg.2024.108093

Article  PubMed  CAS  Google Scholar 

Dhameliya TM, Bhakhar KA, Gajjar ND, Patel KA, Devani AA, Hirani RV (2022) Recent advancements and developments in search of anti-tuberculosis agents: a quinquennial update and future directions. J Mol Struct 1248:131473. https://doi.org/10.1016/j.molstruc.2021.131473

Article  CAS  Google Scholar 

WHO Global Tuberculosis Report (2023) https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023. Accessed August 1

Nagarajan S, Whitaker P (2018) Management of adverse reactions to first-line tuberculosis antibiotics. Curr Opin Allergy Clin Immunol 18(4):333–341. https://doi.org/10.1097/ACI.0000000000000462

Article  PubMed  CAS  Google Scholar 

Unissa AN, Subbian S, Hanna LE, Selvakumar N (2016) Overview on mechanisms of Isoniazid action and resistance in Mycobacterium tuberculosis. Infect Genet Evol 45:474–492. https://doi.org/10.1016/j.meegid.2016.09.004

Article  PubMed  CAS  Google Scholar 

Theuretzbacher U, Bush K, Harbarth S et al (2020) Critical analysis of antibacterial agents in clinical development. Nat Rev Microbiol 18(5):286–298. https://doi.org/10.1038/s41579-020-0340-0

Article  PubMed  CAS  Google Scholar 

Puhl AC, Lane TR, Vignaux PA et al (2020) Computational approaches to identify molecules binding to Mycobacterium tuberculosis KasA. ACS Omega 5(46):29935–29942. https://doi.org/10.1021/acsomega.0c04271

Article  PubMed  PubMed Central  CAS  Google Scholar 

Chiaradia L, Lefebvre C, Parra J et al (2017) Dissecting the mycobacterial cell envelope and defining the composition of the native mycomembrane. Sci Rep 7(1):12807. https://doi.org/10.1038/s41598-017-12718-4

Article  PubMed  PubMed Central  CAS  Google Scholar 

Pan P, Tonge PJ (2012) Targeting InhA, the FASII enoyl-ACP reductase: SAR studies on novel inhibitor scaffolds. Curr Top Med Chem 12(7):672–693. https://doi.org/10.2174/156802612799984535

Article  PubMed  PubMed Central  CAS  Google Scholar 

Glickman MS, Cox JS, Jacobs WR Jr. (2000) A novel mycolic acid cyclopropane synthetase is required for cording, persistence, and virulence of Mycobacterium tuberculosis. Mol Cell 5(4):717–727. https://doi.org/10.1016/s1097-2765(00)80250-6

Article  PubMed  CAS  Google Scholar 

Dubnau E, Chan J, Raynaud C et al (2000) Oxygenated mycolic acids are necessary for virulence of Mycobacterium tuberculosis in mice. Mol Microbiol 36(3):630–637. https://doi.org/10.1046/j.1365-2958.2000.01882.x

Article  PubMed  CAS  Google Scholar 

Bhatt A, Molle V, Besra GS, Jacobs WR Jr, Kremer L (2007) The Mycobacterium tuberculosis FAS-II condensing enzymes: their role in mycolic acid biosynthesis, acid-fastness, pathogenesis and in future drug development. Mol Microbiol 64(6):1442–1454. https://doi.org/10.1111/j.1365-2958.2007.05761.x

Article  PubMed  CAS  Google Scholar 

Vilchèze C, Wang F, Arai M et al (2006) Transfer of a point mutation in Mycobacterium tuberculosis InhA resolves the target of Isoniazid. Nat Med 12(9):1027–1029. https://doi.org/10.1038/nm1466

Article  PubMed  CAS  Google Scholar 

Vilchèze C, Morbidoni HR, Weisbrod TR et al (2000) Inactivation of the inhA-encoded fatty acid synthase II (FASII) enoyl-acyl carrier protein reductase induces accumulation of the FASI end products and cell lysis of Mycobacterium smegmatis. J Bacteriol 182(14):4059–4067. https://doi.org/10.1128/JB.182.14.4059-4067.2000

Article  PubMed  PubMed Central  Google Scholar 

Zhang W, Pei J, Lai L (2017) Computational multitarget drug design. J Chem Inf Model 57(3):403–412. https://doi.org/10.1021/acs.jcim.6b00491

Article  PubMed  CAS  Google Scholar 

Sager, Asma A et al (2018) Design, synthesis and biological evaluation of some triazole schiff’s base derivatives as potential antitubercular agents. Open Med Chem J 12:48–59. https://doi.org/10.2174/1874104501812010048

Nguyen PC, Delorme V, Bénarouche A et al (2018) Oxadiazolone derivatives, new promising multi-target inhibitors against M. tuberculosis. Bioorg Chem 81:414–424. https://doi.org/10.1016/j.bioorg.2018.08.025

Article  PubMed  CAS  Google Scholar 

Ballell L, Bates RH, Young RJ et al (2013) Fueling open-source drug discovery: 177 small-molecule leads against tuberculosis. ChemMedChem 8(2):313–321. https://doi.org/10.1002/cmdc.201200428

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kumar P, Capodagli GC, Awasthi D et al (2018) Synergistic lethality of a binary inhibitor of Mycobacterium tuberculosis KasA. mBio 9(6):e02101–e02117. https://doi.org/10.1128/mBio.02101-17

Article  PubMed  PubMed Central  Google Scholar 

Inoyama D, Awasthi D, Capodagli GC et al (2020) A preclinical candidate targeting Mycobacterium tuberculosis KasA. Cell Chem Biol 27(5):560–570e10. https://doi.org/10.1016/j.chembiol.2020.02.007

Article  PubMed  PubMed Central  CAS  Google Scholar 

Zhu J, Wang J, Wang X et al (2021) Prediction of drug efficacy from transcriptional profiles with deep learning. Nat Biotechnol 39(11):1444–1452. https://doi.org/10.1038/s41587-021-00946-z

Article  PubMed  CAS  Google Scholar 

Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M (2019) eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol 20(1):2. https://doi.org/10.1186/s40360-018-0282-6

Article  PubMed  PubMed Central  Google Scholar 

You J, McLeod RD, Hu P (2019) Predicting drug-target interaction network using deep learning model. Comput Biol Chem 80:90–101. https://doi.org/10.1016/j.compbiolchem.2019.03.016

Article  PubMed  CAS  Google Scholar 

Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235

Article  PubMed  PubMed Central  CAS  Google Scholar 

Schiebel J, Kapilashrami K, Fekete A et al (2013) Structural basis for the recognition of mycolic acid precursors by KasA, a condensing enzyme and drug target from Mycobacterium tuberculosis. J Biol Chem 288(47):34190–34204. https://doi.org/10.1074/jbc.M113.511436

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kim S, Chen J, Cheng T et al (2023) PubChem 2023 update. Nucleic Acids Res 51(D1):D1373–D1380. https://doi.org/10.1093/nar/gkac956

Article  PubMed  Google Scholar 

Mendez D, Gaulton A, Bento AP et al (2019) ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res 47(D1):D930–D940. https://doi.org/10.1093/nar/gky1075

Article  PubMed  CAS  Google Scholar 

Friesner RA, Murphy RB, Repasky MP et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49(21):6177–6196. https://doi.org/10.1021/jm051256o

Article  PubMed  CAS  Google Scholar 

Jorgensen WL, Tirado-Rives J (1988) The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110(6):1657–1666. https://doi.org/10.1021/ja00214a001

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