Identification and characterization of potential druggable targets among Essential Hypothetical Proteins of A. baumannii

Yıl 2022, Cilt: 5 Sayı: 2, 145 - 165, 15.08.2022

Bydaa Atron

https://doi.org/10.38001/ijlsb.1009800

Öz

Acinetobacter baumannii, a gram negative bacteria, has emerged as a critical pathogen responsible for nosocomial and other infections. A. baumannii exhibits resistance to a variety of antibiotic classes, emphasizing that new therapeutic targets are urgently needed. In A. baumannii, ATCC 179778, 458 genes have been identified as essential genes, indispensable for growth and survival of the pathogen. The functions of 47 proteins encoded by A.baumannii essential genes were found to be hypothetical and thus referred as essential hypothetical proteins (EHPs). The present study aims to carry out functional characterization of EHPs using bioinformatics tools/databases. Evaluation of physicochemical parameters, homology search against known proteins, domain analysis, subcellular localization analysis, 3D structure prediction and virulence prediction assisted us to characterize EHPs. They belong to different functional classes like enzymes, binding proteins, helicases, transporters, miscellaneous proteins and virulence factors. Around 47% of EHPs were enzymes. A group of EHPs (17.6%) were predicted as virulence factors. Proteins present in the pathogen but absent in the host were identified using host non-homology analysis. Further druggability analysis examined the druggable property of the proteins. Of 34, 27 essential pathogen-specific proteins which could serve as potential novel drug and vaccine targets. Druggability analysis was performed to examined the druggable property of the proteins. One target was found to be druggable and others were novel targets. The study's findings might assist in the development of new drugs for the treatment of Acinetobacter baumannii infections.

Anahtar Kelimeler

Acinetobacter baumannii, Essential hypothetical proteins, Functional annotation, Drug targets

Kaynakça

• 1. Sievert, D., Ricks, P., Edwards, J., Schneider, A., Patel, J., Srinivasan, A. Fridkin, S. Antimicrobial-Resistant Pathogens Associated with Healthcare-Associated Infections Summary of Data Reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infection Control & Hospital Epidemiology.2013. 34(1):P.1-14.
• 2. Fishbain, J., Peleg, A. Y. Treatment of Acinetobacter infections. Clinical and Infectious Disease Journal, 2010. 51: P.79–84.
• 3. Butler, D.A., Biagi, M., Tan, X. et al. Multidrug Resistant Acinetobacter baumannii: Resistance by Any Other Name Would Still be Hard to Treat. Current Infectious Diseases Reports, 2019. 21: 46.
• 4. H. Luo, Y. Lin, F. Gao, C.-T. Zhang, R. Zhang. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements, Nucleic Acids Research, 2013.42 P.574–580.
• 5. M. Shahbaaz, K. Bisetty, F. Ahmad, M.I. Hassan. Current Advances in the Identification and Characterization of Putative Drug and Vaccine Targets in the Bacterial Genomes, Current Topics in Medicine and Chemistry, 2016. 16 :P. 1040–1069.
• 6. M.S. Islam, S.M. Shahik, M. Sohel, N.I.A. Patwary, M.A. Hasan, In Silico Structural and Functional Annotation of Hypothetical Proteins of Vibrio cholerae O139, Genomics Informatics, 2015. 13:P. 53–59.
• 7. K. Kumar, A. Prakash, M. Tasleem, A. Islam, F. Ahmad, M.I. Hassan, Functional annotation of putative hypothetical proteins from Candida dubliniensis. Gene, 2014. 543: 93–100.
• 8. A.A. Turab Naqvi, S. Rahman, Rubi, F. Zeya, K. Kumar, H. Choudhary, M.S. Jamal, Kim, M.I. Hassan, Genome analysis of Chlamydia trachomatis for functional characterization of hypothetical proteins to discover novel drug targets, International Journal of Biological Macromolecule, 2017.96: P.234–240.
• 9. A. P Bidkar, A.P. Bidkar, In-silico Structural and Functional Analysis of Hypothetical Proteins of Leptospira Interrogans, Biochemistry & Pharmacology: Open Access. 2014.3
• 10. J. Hoskeri. H, J.H. H, Functional Annotation of Conserved Hypothetical Proteins in Rickettsia Massiliae MTU5, Journal of Computational Science and Systemic Biology, 2010.3
• 11. G.K. Mazandu, N.J. Mulder, Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins. International Journal of Molecular Science, 2012. 13: P.7283–7302.
• 12. M. Shahbaaz, M. ImtaiyazHassan, F. Ahmad, Functional Annotation of Conserved Hypothetical Proteins from Haemophilus influenzae Rd KW20, PLoS One, 2013.8 (8) 42-63.
• 13. M. Shahbaaz, K. Bisetty, F. Ahmad, M. Hassan, Functional Insight into Putative Conserved Proteins of Rickettsia rickettsii and their Virulence Characterization, Current Proteomics, 2015.12: P.101–116.
• 14. S. Kumar, Computational functional and structural annotation of hypothetical proteins of Neisseria Meningitidis MC58. Conference: International conference on Biochemsitry, At Kuala Lumpur Malaysia.2016, 5:3. 15. A.A.T. Naqvi, F. Ahmad, M.I. Hassan, Identification of functional candidates amongst hypothetical proteins of Mycobacterium leprae Br4923, a causative agent of leprosy, Genome, 2015. 33.P. 25–42.
• 16. A.A.T. Naqvi, M. Shahbaaz, F. Ahmad, M.I. Hassan, Identification of functional candidates amongst hypothetical proteins of Treponema pallidum ssp. pallidum, PLoS One, 2015. 10: P.124-177.
• 17. S. Khan, M.S. Jamal, F. Anjum, M. Rasool, A. Ansari, A. Islam, F. Ahmad, M.I. Hassan. Functional annotation of putative conserved proteins from Borrelia burgdorferi to find potential drug targets, International Journal of Computational Biology. Drug Research, 2016. 9: 295.
• 18. Ye, Jian et al. “BLAST: improvements for better sequence analysis.” Nucleic acids research, 2006.34: P.6-9.
• 19. R.D. Finn, P. Coggill, R.Y. Eberhardt, S.R. Eddy, J. Mistry, A.L. Mitchell, S.C. Potter, M. Punta, M. Qureshi, A. Sangrador-Vegas, G.A. Salazar, J. Tate, A. Bateman, The Pfam protein families database: towards a more sustainable future, Nucleic Acids Res, 2016.94: P. 279–85.
• 20. P. Jones, D. Binns, H.-Y. Chang, M. Fraser, W. Li, C. McAnulla, H. McWilliam, J. Maslen, A. Mitchell, G. Nuka, S. Pesseat, A.F. Quinn, A. Sangrador-Vegas, M. Scheremetjew, S.-Y. Yong, R. Lopez, S. Hunter, InterProScan 5: genome-scale protein function classification, Bioinformatics, 2014. 30: P. 1236–1240.
• 21. A. Marchler-Bauer, Y. Bo, L. Han, J. He, C.J. Lanczycki, S. Lu, F. Chitsaz, M.K. Derbyshire, R.C. Geer, N.R. Gonzales, M. Gwadz, D.I. Hurwitz, F. Lu, G.H. Marchler, J.S. Song, N. Thanki, Z. Wang, R.A. Yamashita, D. Zhang, C. Zheng, L.Y. Geer, S.H. Bryant, CDD/SPARCLE: functional classification of proteins via subfamily domain architectures, Nucleic Acids Research, 2017.45: P. 200–203
• 22. S. Chuguransky, L. Williams, M. Qureshi, G.A. Salazar, E.L.L. Sonnhammer, S.C.E. Tosatto, L. Paladin, S. Raj, L.J. Richardson, R.D. Finn, A. Bateman. Pfam: The protein families database in 2021: J. Mistry. Nucleic Acids Research (2021)
• 23. T. Hirokawa, S. Boon-Chieng, S. Mitaku, SOSUI: classification and secondary structure prediction system for membrane proteins, Bioinformatics, 1998. 56:P. 378–379.
• 24. S. Saha, G.P.S. Raghava, VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition, Genomics Proteomics Bioinformatics, 2006. 45: P. 42–47.
• 25. A. Jadhav, B. Shanmugham, A. Rajendiran, A. Pan, Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis, Infection and Genetic Evolution, 2014.27:P. 300–308.
• 26. A. Jadhav, V. Ezhilarasan, O. Prakash Sharma, A. Pan, Clostridium-DT(DB): a comprehensive database for potential drug targets of Clostridium difficile, Computational Biology, 2013.43: P. 362–367.
• 27. V. Law, C. Knox, Y. Djoumbou, T. Jewison, A.C. Guo, Y. Liu, A. Maciejewski, D. Arndt, M. Wilson, V. Neveu, A. Tang, G. Gabriel, C. Ly, S. Adamjee, Z.T. Dame, B. Han, Y. Zhou, D.S. Wishart, DrugBank 4.0: shedding new light on drug metabolism, Nucleic Acids Research, 2013. 42: P.1091–1097.
• 28. Shanmugham, A. Pan, Identification and Characterization of Potential Therapeutic Candidates in Emerging Human Pathogen Mycobacterium abscessus: A Novel Hierarchical In Silico Approach, PLoS One, 2013. 8: 59126.
• 29. Friedrich, Anne et al. “SM2PH-db: an interactive system for the integrated analysis of phenotypic consequences of missense mutations in proteins involved in human genetic diseases.” Human mutation vol, 2010.29(2):P. 127-35
• 30. M. R. Rahbar, I. Rasooli, S. L. M. Gargari et al., “A potential in silico antibody-antigen based diagnostic test for precise identification of Acinetobacter baumannii,” Journal of Theoretical Biology, 2012. 294: P. 29–39.
• 31. Eduardo Busto, Vicente Gotor-Fernández, Vicente Goto. Hydrolases: catalytically promiscuous enzymes for non-conventional reactions in organic synthesis. Chemical Society Reviews, 2010.39: P. 4504-4523
• 32. Deutsch, Christopher, El Yacoubi B, de Crécy-Lagard V, Iwata-Reuyl D. Biosynthesis of threonylcarbamoyl adenosine (t6A), a universal tRNA nucleoside. The Journal of biological chemistr, 2012. 287:17
• 33. Kihara A, Akiyama Y, Ito K. Revisiting the lysogenization control of bacteriophage lambda. Identification and characterization of a new host component, HflD. The Journal of Biological Chemistry, 2001. 276(17):13695-13700.
• 34. Fujita, C., Nishimura, A., Iwamoto, R., & Ikehara, K. Guanosine 5'-diphosphate 3'-diphosphate (ppGpp) synthetic activities on Escherichia coli SpoT domains. Bioscience, biotechnology, and biochemistry, 2002. 66(7), 1515–1523.
• 35. Lu, W., Wang, L., Chen, L., Hui, S., & Rabinowitz, J. D. Extraction and Quantitation of Nicotinamide Adenine Dinucleotide Redox Cofactors. Antioxidants & redox signaling, 2018. 28(3), 167–179.
• 36. Macheroux, P., Kappes, B., & Ealick, S. E. Flavogenomics--a genomic and structural view of flavin-dependent proteins. The FEBS journal, 2011. 278(15). P. 2625–2634.
• 37. Dawson A, Trumper P, Chrysostomou G, Hunter WN. Structure of diaminohydroxy phosphoribosyl-aminopyrimidine deaminase/5-amino-6-(5-phosphoribosylamino) uracil reductase from Acinetobacter baumannii. Acta Crystallography Structioral Biology, 2013. 69: P. 611-617.
• 38. Tong L. Structure and function of biotin-dependent carboxylases. Cellular and molecular life sciences : CMLS, 2013. 70(5), 863–891.
• 39. Shen, Y., Volrath, S. L., Weatherly, S. C., Elich, T. D., & Tong, L. . A mechanism for the potent inhibition of eukaryotic acetyl-coenzyme A carboxylase by soraphen A, a macrocyclic polyketide natural product. Molecular and cell, 2004. 16(6): P. 881–891.
• 40. Campbell, J. W., & Cronan, J. E., Jr Bacterial fatty acid biosynthesis: targets for antibacterial drug discovery. Annual review of microbiology,2001. 55: P. 305–332.
• 41. Goenrich M, Bartoschek S, Hagemeier CH, Griesinger C, Vorholt JA. A glutathione-dependent formaldehyde-activating enzyme (Gfa) from Paracoccus denitrificans detected and purified via two-dimensional proton exchange NMR spectroscopy. Journal of Biochemistry, 2009. 277, 3069-72
• 42. Martínez Cuesta, S., Rahman, S. A., Furnham, N., & Thornton, J. M. The Classification and Evolution of Enzyme Function. Biophysical journal, 2015. 109(6), 1082–1086.
• 43. Punekar AS, Liljeruhm J, Shepherd TR, Forster AC, Selmer M. Structural and functional insights into the molecular mechanism of rRNA m6A methyltransferase RlmJ. Nucleic Acids Research, 2013 41(20):9537-9548.
• 44. Favrot, L., Blanchard, J. S., & Vergnolle, O. Bacterial GCN5-Related N-Acetyltransferases: From Resistance to Regulation. Biochemistry, 2016. 55(7), 989–1002.
• 45. Wakil, S. J., Stoops, J. K., & Joshi, V. C. Fatty acid synthesis and its regulation. Annual review of biochemistry, 2003. 52: P. 537–579.
• 46. Gehring, A. M., Lees, W. J., Mindiola, D. J., Walsh, C. T., & Brown, E. D. Acetyltransfer precedes uridylyltransfer in the formation of UDP-N-acetylglucosamine in separable active sites of the bifunctional GlmU protein of Escherichia coli. Biochemistry, 2006. 35(2), 579–585.
• 47. Gehring, A. M., Lees, W. J., Mindiola, D. J.. Acetyltransferase Precedes Uridyltransfer in the Formation of UDP-N-Acetylglucosamine in Separable Sites of Bifunctional GlmU Protein of E. coli. Biochemistry, 1996. 35, 579–585.
• 48. Sharma, R., Lambu, M. R., Jamwal, U., Rani, C., Chib, R., Wazir, P., Mukherjee, D., Chaubey, A., & Khan, I. A. Escherichia coli N-Acetylglucosamine-1-Phosphate-Uridyltransferase/Glucosamine-1-Phosphate-Acetyltransferase (GlmU) Inhibitory Activity of Terreic Acid Isolated from Aspergillus terreus, Journal of bimolecular screening, 2016. 21(4), 342–353.
• 49. Pereira, M. P., Blanchard, J. E., Murphy, C., Roderick, S. L., & Brown, E. D. High-throughput screening identifies novel inhibitors of the acetyltransferase activity of Escherichia coli GlmU. Antimicrobial agents and chemotherapy, 2009. 53(6), 2306–2311.
• 50. Ames, G. F., Mimura, C. S., & Shyamala, V.Bacterial periplasmic permeases belong to a family of transport proteins operating from Escherichia coli to human: Traffic ATPases. FEMS microbiology reviews, 1990. 6(4), 429–446.
• 51. Raetz, C. R., & Whitfield, C. Lipopolysaccharide endotoxins. Annual review of biochemistry, 1983. 71, 635–700.
• 52. Shiomi D, Sakai M, Niki H. Determination of bacterial rod shape by a novel cytoskeletal membrane protein. The EMBO Journal, 2008. 27(23):3081-3091.
• 53. Linton, K. J., & Higgins, C. F. The Escherichia coli ATP-binding cassette (ABC) proteins. Molecular microbiology, 1998. 28(1), 5–13.
• 54. Messner, P., Schäffer, C., & Kosma, P. Bacterial cell-envelope glycoconjugates. Advances in carbohydrate chemistry and biochemistry, 2013. 69, 209–272.
• 55. Sperandeo P, Lau FK, Carpentieri A, De Castro C, Molinaro A, Deho G, Silhavy TJ, Polissi A. Functional analysis of the protein machinery required for transport of lipopolysaccharide to the outer membrane of Escherichia coli. Journal of Bacteriology, 2008. 190, 4460-9.
• 56. Gómez-Santos, N., Glatter, T., Koebnik, R. et al. A TonB-dependent transporter is required for secretion of protease PopC across the bacterial outer membrane. Nature Community, 2019. 10, 1360
• 57. Bröer, S., & Gether, U. The solute carrier 6 family of transporters. British journal of pharmacology, 2012. 167(2), 256–278.
• 58. Jack DL, Yang NM, Saier MH. The drug/metabolite transporter superfamily.2012 Europen Journal of Biochemistry, 268 (13): 3620–39.
• 59. Västermark, Å., Almén, M.S., Simmen, M.W. et al. Functional specialization in nucleotide sugar transporters occurred through differentiation of the gene cluster EamA (DUF6) before the radiation of Viridiplantae. BMC Evolutional Biology, 2011. 11, 123.
• 60. Sperandeo, P., Martorana, A. M., & Polissi, A. The lipopolysaccharide transport (Lpt) machinery: A nonconventional transporter for lipopolysaccharide assembly at the outer membrane of Gram-negative bacteria. The Journal of biological chemistry,2017. 292(44), 17981–17990.
• 61. Allen, R. C., Popat, R., Diggle, S. P., & Brown, S. P. Targeting virulence: can we make evolution-proof drugs?. Nature reviews. Microbiology.2014. 12(4), 300–308. 62. Chan D. I.; Vogel H. J. Current understanding of fatty acid biosynthesis and the acyl carrier protein. Journal of Biochemistry,2010. 430, 1–19.
• 63. Cukier, C. D., Hope, A. G., Elamin, A. A., Moynie, L., Schnell, R., Schach, S., Kneuper, H., Singh, M., Naismith, J. H., Lindqvist, Y., Gray, D. W., & Schneider, G. Discovery of an allosteric inhibitor binding site in 3-Oxo-acyl-ACP reductase from Pseudomonas aeruginosa. ACS chemical biology,2013. 8(11), 2518–2527.