Journal of Environment and Sociobiology

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Predicting Drug Targets from Hypothetical Proteins of Pseudomonas Sp. Released from Permafrost Thawing under Impact of Climate Change


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1 Post-Graduate Department of Biotechnology, St. Xavier’s College (Autonomous), Kolkata, West Bengal, India
     

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One of the major consequences of the increase in global temperature is the thawing of permafrost, which is predicted to cause huge imbalances in natural ecosystems. The results of permafrost thawing is the resurface of quiescent psychrotolerant microbes which have been shown to be storehouses of antibiotic resistance genes (ARGs). Such superbugs, even if non-pathogenic, can transfer the ARGs to active pathogens, aggravating the existing public health crisis of antibiotic resistance. It is thus imperative to explore novel drug targets. Like most other organisms, bacteria possess coding sequences in the genome whose cellular and biochemical functions remain to be predicted. Functional annotation of such hypothetical proteins and their subsequent exploration as drug targets can thus be attempted as a novel computer-aided drug discovery approach. In this paper, we propose an in-silico pipeline for characterisation and functional annotation of hypothetical proteins using Pseudomonas aeruginosa, a multidrug-resistant WHO-listed critical priority pathogen. We then explore their potential as drug targets with small molecules of plant origin. Our results show considerable interactions between the proteins and the small molecules, including successful molecular docking, establishing a successful pipeline which may be useful in small molecule-based drug discovery in the near future.

Keywords

Permafrost thawing, Antibiotic resistance, Pseudomonas aeruginosa, Hypothetical protein
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  • Armenteros, J. J. A., Tsirigos, K. D., Sønderby, C. K., Petersen, T. N., Winther, O., Brunak, S., Heijne, G. von and Nielsen. H. 2019. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol., 37(4): 420-423.

  • Apweiler, R., Attwood, T. K., Bairoch, A., Bateman, A., Birney, E., Biswas, M., Bucher, P., Cerutti, L., Corpet, F., Corning, M. D. R., Durbin, R., Falquet, L., Fleischmann, W., Gouzy, J., Hermjakob, H., Hulo, N., Jonassen, I., Kahn, D., Kanapin, A., Karavidopoulou, Y., Lopez, R., Marx, B., Mulder, N. J., Oinn, T. M., Pagni, M., Servant, F., Sigrist, C. J. A. and Zdobnov, E. M. 2000. InterPro-an integrated documentation resource for protein families, domains and functional sites. Bioinformatics, 16(12): 1145-1150.

  • Bamford, V. A. and Naismith, J. H. 2010. Molecular insights into clinically isolated OmpC mutants and their role in multi-drug resistance (OmpC26). Worldwide Protein Data Bank.

  • Bienert, S., Waterhouse, A., de Beer, T. A. P. Tauriello, G., Studer, G., Bordoli, L. and Schwede, T. 2017. The SWISS-MODEL Repository—new features and functionality. Nucleic Acids Res. 45(D1): D313-D319.

  • Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johansson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M. and Lantuit, H. 2019. Permafrost is warming at a global scale. Nat. Commun., 10(1): 264. DOI: 10.1038/s41467- 018-08240-4.

  • Blum, M., Chang, H-Y., Chuguransky, S., Grego, T., Kandasaamy, S., Mitchell, A., Nuka, G., Paysan-Lafosse, T., Qureshi, M., Raj, S., Richardson, L., Salazar, G. A., Williams, L., Bork, P., Bridge, A., Gough, J., Haft, D. H., Letunic, I., Marchler-Bauer, A., Mi, H., Natale, D. A., Necci, M., Orengo, C. A., Pandurangan, A. P., Rivoire, C., Sigrist, C. J. A., Stillitoe, I., Thanki, N., Thomas, P. D., Tosatto, S. C. E., Wu, C. H., Bateman, A. and Finn, R. D. 2021. The InterPro protein families and domains database: 20 years on. Nucleic Acids Res., 49(D1): D344-D354.

  • Botelho, J., Grosso, F. and Peixe, L. 2019. Antibiotic resistance in Pseudomonas aeruginosa - mechanisms, epidemiology and evolution. Drug Resist. Updat., 44: 100640. DOI: 10.1016/j.drup.2019.07.002

  • Chandran, V., Fronzes, R., Duquerroy, S., Cronin, N., Navaza, J. and Waksman, G. 2009. Structure of the outer membrane complex of a type IV secretion system. Nature, 462(7276): 1011-1015.

  • Chen, L., Yang, J., Yu, J., Yao, Z., Sun, L., Shen, Y. and Jin, Q. 2005. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res., 33 (Database issue): D325-D328.

  • da Costa, W. L. O., de Aragão Araújo, C. L, Dias, L. M., de Sousa Pereira, L. C., Alves, J. T. C., Araújo, F. A., Folador, E. L., Henriques, I., Silva, A. and Folador, A. R. C. 2018. Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum Strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance. PloS One, 13(6): e0198965. DOI: 10.1371/journal.pone.0198965

  • D’Amico, S., Collins, T., Marx, J-C., Feller, G. and Gerday, C. 2006. Psychrophilic microorganisms: challenges for life. EMBO Rep., 7(4): 385-389.

  • D’Costa, V. M., King, C. E., Kalan, L., Morar, M., Sung, W. W. L., Schwarz, C., Froese, D., Zazula, G., Calmels, F., Debruyne, R., Golding, G. B., Poinar, H. N. and Wright, G. D. 2011. Antibiotic resistance is ancient. Nature, 477(7365): 457-461.

  • Dey, P., Kundu, A., Chakraborty, H. J., Kar, B., Choi, W. S., Lee, B. M., Bhakta, T., Atanasov, A. G. and Kim, H. S. 2019. Therapeutic value of steroidal alkaloids in cancer: current trends and future perspectives. Int. J. Cancer, 145(7): 1731- 1744.

  • Doerks, T., von Mering, C. and Bork, P. 2004. Functional clues for hypothetical proteins based on genomic context analysis in prokaryotes. Nucleic Acids Res., 32(21): 6321-6326.

  • Duhovny, D., Nussinov, R. and Wolfson, H. J. 2002. Efficient unbound docking of rigid molecules. In: R. Guigó and D. Gusfield (eds.), Algorithms in Bioinformatics. WABI 2002. Lecture Notes in Computer Science, 2452: 185-200. Springer, Berlin, Heidelberg.

  • Goldstein, E. J. C., Citron, D. M. , Peraino, V. A. and Cross, S. A. 2003. Desulfovibrio desulfuricans bacteremia and review of human Desulfovibrio infections. J. Clin. Microbiol., 41(6): 2752-2754.

  • Guha, S., Das, S. and Ganguli, S. 2020. A comparative genomics pipeline for in silico characterization and functional annotation of short hypothetical proteins. J. Trop. Life Sci, 10(2): 141-148.

  • Haan, T. J. and Drown D. M. 2021. Unearthing antibiotic resistance associated with disturbance-induced permafrost thaw in interior Alaska. Microorganisms, 9(1): 116. DOI: 10.3390/microorganisms9010116

  • Hancock, R. E. W. and Speert, D. P. 2000. Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and impact on treatment. Drug Resist. Updat., 3(4): 247-255.

  • Hawkins, T. and Kihara, D. 2007. Function prediction of uncharacterized proteins. J. Bioinform. Comput. Biol., 5(1): 1-30.

  • Hultman, J., Waldrop, M. P., Mackelprang, R., David, M. M., McFarland, J., Blazewicz, S. J., Harden, J., Turetsky, M. R., McGuire, A. D., Shah, M. B., VerBerkmoes, N. C., Lee, L. H., Mavrommatis, K. and Jansson, J. K. 2015. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature, 521(7551): 208-212.

  • Huu, N. B., Denner, E. B., Ha, D. T., Wanner, G. and Stan-Lotter, H. 1999. Marinobacter aquaeolei sp. nov., a halophilic bacterium isolated from a Vietnamese oil-producing well. Int. J. Syst. Bacteriol., 49(2): 367-375.

  • Jansson, J. K. and Taş, N. 2014. The microbial ecology of permafrost. Nat. Rev. Microbiol., 12(6): 414-425.

  • Jeannot, K., Hagart, K., Dortet, L., Kostrzewa, M., Filloux, A., Plesiat, P. and Larrouy- Maumus, G. 2021. Detection of colistin resistance in Pseudomonas aeruginosa using the MALDIxin test on the routine MALDI Biotyper Sirius Mass Spectrometer. Front. Mcrobiol., 12: 725383. DOI: 10.3389/fmicb.2021.725383

  • Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E. and Kolinski, A. 2016. Coarse-grained protein models and their applications. Chem. Rev., 116 (14): 7898-7936.

  • Kuriata, A., Gierut, A. M., Oleniecki, T., Ciemny, M. P., Kolinski, A., Kurcinski, M. and Kmiecik. S. 2018. CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Res., 46(W1): W338-W343.

  • Laldingliani, T. B. C., Thangjam, N. M., Zomuanawma, R., Bawitlung, L., Pal, A. and Kumar, A. 2022. Ethnomedicinal study of medicinal plants used by Mizo tribes in Champhai district of Mizoram, India. J. Ethnobiol. Ethnomed., 18(1): 22. DOI: 10.1186/s13002-022-00520-0

  • Lambert, P. A. 2002. Mechanisms of antibiotic resistance in Pseudomonas aeruginosa. J. R. Soc. Med., 95(Suppl. 41): 22-26.

  • Landrum, L. and Holland, M. M. 2020. Extremes become routine in an emerging new Arctic. Nat. Clim. Change, 10(12): 1108-1115.

  • Laskowski, R. A., Hutchinson, E. G., Michie, A. D., Wallace, A. C., Jones, M. L. and Thornton, J. M. 1997. PDBsum: a web-based database of summaries and analyses of all PDB structures. Trends Biochem. Sci., 22(12): 488-490.

  • Li, Z., Sun, A., Liu, X., Chen, Q-L., Bi, L., Ren, P-X., Shen, J-P., Jin, S., He, J-Z., Hu, H-W. and Yang, Y. 2022. Climate warming increases the proportions of specific antibiotic resistance genes in natural soil cosystems. J. Hazard. Mater., 430: /DOI: 10.1016/j.jhazmat.2022.128442

  • Liu, B., Zheng, D., Jin, Q., Chen, L. and Yang, J. 2019. VFDB 2019: A comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res., 47(D1): D687-D692.

  • Liu, X., Khara, P., Baker, M. L., Christie, P. J. and Hu, B. 2022. Structure of a type IV secretion system core complex encoded by multi-drug resistance F plasmids. Nat. Commun., 13(1): 379. DOI: 10.1038/s41467-022-30584-1

  • Loubinoux, J., Bronowicki, J-P., Pereira, I. A. C., Mougenel, J-L. and Faou, A. E. 2002. Sulfate-reducing bacteria in human feces and their association with inflammatory bowel diseases. FEMS Microbiol. Ecol., 40(2): 107-112.

  • MacFadden, D. R., McGough, S. F., Fisman, D., Santillana, M. and Brownstein, J. S. 2018. Antibiotic resistance increases with local temperature. Nat. Clim. Change, 8(6): 510-514.

  • Mackelprang, R., Saleska, S. R., Jacobsen, C. S., Jansson, J. K. and Taş, N. 2016. Permafrost meta-omics and climate change. Annu. Rev. Earth Planet. Sci., 44(1): 439-462.

  • Margesin, R., and Collins, T. 2019. Microbial ecology of the cryosphere (glacial and permafrost habitats): current knowledge. Appl. Microbiol. Biotechnol. 103(6): 2537-2549.

  • Mishra, U., Hugelius, G., Shelef, E., Yang, Y., Strauss, J., Lupachev, A., Harden, J. W., Jastrow, J. D., Ping, C-L., Riley, W. J., Schuur, E. A. G., Matamala, R., Siewert, M., Nave, L. E., Koven, C. D., Fuchs, M., Palmtag, J., Kuhry, P., Treat, C. C., Zubrzycki, S., Hoffman, F. M., Elberling, B., Camill, P., Veremeeva, A. and Orr, A. 2021. Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks. Sci. Adv., 7(9): eaaz5236. DOI: 10.1126/sciadv/aaz5236.

  • Mohanty, S., Baliyarsingh, B. and Nayak, S. K. 2020. Antimicrobial resistance in Pseudomonas aeruginosa: a concise review. In: M. Mareş, S. H. E. Lim, K-S. Lai and R-T. Cristina (eds.), Antimicrobial Resistance: A One Health Perspective. IntechOpen, pp. 1-21.

  • Moser, D., Klaiber, I., Vogler, B. and Kraus, W. 1999. Molluscicidal and antibacterial compounds from Petunia hybrida. Pestic. Sci., 55(3): 336-339.

  • Mukhopadhyay, S., Ganguli, S. and Chakrabarti, S. 2022. Exploring the functions and interactions of undeciphered proteins from Shigella flexneri. Int. J. Comput. Biol. Drug Des., 15(1): 60-75.

  • Murros, K. E., Huynh, V. A., Takala, T. M. and Saris, P. E. J. 2021. Desulfovibrio bacteria are associated with Parkinson’s disease. Fron. Cell. Infect. Microbiol., 11: 652617. DOI: 10.3389/fcimb.2021.652617

  • NCBI Resource Coordinators. 2016. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res., 44(D1): D7-D19.

  • Nickerson, N. N., Jao, C. C., Xu, Y., Quinn, J., Skippington, E., Alexander, M. K., Miu, A, Skelton, N., Hankins, J. V., Lopez, M. S., Koth, C. M., Rutherford, S. and Nishiyama, M. 2018. A novel inhibitor of the LolCDE ABC transporter essential for lipoprotein trafficking in gram-negative bacteria. Antimicrob. Agents and Chemother., 62(4): e02151-17. DOI: 10.1128/AAC.02151-17

  • Nielsen, H., Tsirigos, K. D., Brunak, S. and Heijne, G. von. 2019. A brief history of protein sorting prediction. Protein J., 38(3): 200-216.

  • Nimrod, G., Schushan, M., Steinberg, D. M. and Ben-Tal, N. 2008. Detection of functionally important regions in ‘Hypothetical Proteins’ of known structure. Structure, 16(12): 1755-1763.

  • Pang, Z., Raudonis, R., Glick, B. R., Lin, T-J. and Cheng, Z. 2019. Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and alternative therapeutic strategies. Biotechnol. Adv., 37(1): 177-192.

  • Park, H-G., Sathiyanarayanan, G., Hwang, C-H., Ann, D-H., Kim, J-H., Bang, G., Jang, K-S., Ryu, H. W., Lee, Y. K., Yang, Y-H. and Kim, Y. G. 2017. Chemical structure of the lipid A component of Pseudomonas sp. strain PAMC 28618 from thawing permafrost in relation to pathogenicity. Sci. Rep., 7(1): 2168. DOI: 10.1038/s41598-017-02145-w

  • Peitsch, M. C. 1996. Promod and Swiss-model: internet-based tools for automated comparative protein modelling. Biochem. Soc. Trans., 24(1): 274-279.

  • Perron, G. G., Whyte, L., Turnbaugh, P. J., Goordial, J., Hanage, W. P., Dantas, G. and Desai, M. M. 2015. Functional characterization of bacteria isolated from ancient arctic soil exposes diverse resistance mechanisms to modern antibiotics. PloS One, 10(3): e0069533. DOI: 10.1371/journal.pone.0069533

  • Poole, K. 2011. Pseudomonas aeruginosa: resistance to the max. Front. Microbiol., 2: 65. DOI: 10.3389/fmicb.2011.00065 Ramachandran, G. N., Ramakrishnan, C. and Sasisekharan, V. 1963. Stereochemistry of polypeptide chain configurations. J. Mol. Biol., 7: 95-99.

  • Rehman, S., Khalid, A., Saqib , Q. N., Ahmad, F., Rehman, S., Zaman, N., Mehmood, A. and Samad, A. 2019. In-vitro antimicrobial analysis of aqueous methanolic extracts and crude saponins isolated from leaves and roots of Sarcococca saligna. Pak. J. Agric. Res., 32(2): 268-274.

  • Rey, F. E., Gonzalez, M. D., Cheng, J., Wu, M., Ahern, P. P. and Gordon, J. I. 2013. Metabolic niche of a prominent sulfate-reducing human gut bacterium. Proc. Natil. Acad. Sci. U. S. A, 110(33): 13582-13587.

  • Rice, L. B. 2008. Federal funding for the study of antimicrobial resistance in nosocomial pathogens: no ESKAPE. J. Infect. Dis., 197(8): 1079-1081.

  • Rodríguez-Verdugo, A., Gaut, B. S. and Tenaillon, O. 2013. Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress. BMC Evol. Biol., 13(1): 50. DOI: 10.1186/1471-2148-13-50.

  • Rodríguez-Verdugo, A., Lozano-Huntelman, N., Cruz-Loya, M., Savage, V. and Yeh, P. 2020. Compounding effects of climate warming and antibiotic resistance. iScience, 23(4): 101024. DOI: 10.1016/j.isci.2020.101024

  • Sebastian, S., Schreiber, S., Haupt, V. J., Adasme, M. F. and Schroeder, M. 2015. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res., 43(W1): W443-W447.

  • Santajit, S. and Indrawattana, N. 2016. Mechanisms of antimicrobial resistance in ESKAPE pathogens. Biomed. Res. Int., 2016: 2475067. DOI: 10.1155/ 2016/2475067

  • Sayle, R. A. and Milner-White, E. J. 1995. RASMOL: biomolecular graphics for all. Trends Biochem. Sci., 20(9): 374. DOI: 10.1016/s0968.-0004(00)89080-5.

  • Schneidman-Duhovny, D., Inbar, Y., Nussinov, R. and Wolfson, H. J. 2005. Patchdock and Symmdock: servers for rigid and symmetric docking. Nucleic Acids Res., 33 (Web Server Issue): W363-W367.

  • Sivashankari, S. and Shanmughavel, P. 2006. Functional annotation of hypothetical proteins - a review. Bioinformation, 1(8): 335-338.

  • Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. and Romanovsky, V. E. 2022. The changing thermal state of permafrost. Nat. Rev. Earth Environ., 3(1): 10-23.

  • Spröer, C., Lang, E., Hobeck, P., Burghardt, J., Stackebrandt, E. and Tindall, B. J. 1998. Note: transfer of Pseudomonas nautica to Marinobacter hydrocarbonoclasticus. Int. J. Syst. Bacteriol., 48(4): 1445-1448.

  • Tacconelli, E., Carrara, E., Savoldi, A., Harbarth, S., Mendelson, M., Monnet, D. L., Pulcini, C., Kahlmeter, G., Kluytmans, J., Carmeli, Y., Ouellette, M., Outterson, J. D., Patel, J., Cavaleri, M., Cox, E. M., Houchens, C. R., Grayson, M. L., Hansen, P., Singh, N. and Theuretzbacher, U. 2018. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect. Dis, 18(3): 318-327.

  • Tang, X., Chang, S., Zhang, K., Luo, Q., Zhang, Z., Wang, T., Qiao, W., Wang, C., Shen, C., Zhang, Z., Zhu, X., Wei, X., Dong, C. and Zhang, X. and Dong, H. 2021. Structural basis for bacterial lipoprotein relocation by the transporter LolCDE. Nat. Struct. Mol. Biol., 28(4): 347-355.

  • Volkamer, A., Griewel, A., Grombacher, T. and Rarey, M. 2010. Analyzing the topology of active sites: on the prediction of pockets and subpockets. J. Chem. Inf. Model., 50(11): 2041-2052.

  • Volkamer, A., Kuhn, D., Rippmann, F. and Rarey, M. 2012. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics, 28(15): 2074-2075.

  • Wallace, A. C., Laskowski, R. A. and Thornton, J. M. 1995. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng., 8(2): 127-134.

  • Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., Lepore, R. and Schwede, T. 2018. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 46(W1): W296-W303.

  • Williams, C. J., Headd, J. J., Moriarty, N. W., Prisant, M. G., Videau, L. L., Deis, L. N., Verma, V., Keedy, D. A., Hintze, B. J., Chen, V. B., Jain, S., Lewis, S. M., Arendall, W. B., Snoeyink, J., Adams, P. D., Lovell, S. C., Richardson, J. S. and Richardson, D. C. 2018. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci., 27(1): 293-315.

  • Wiltgen, M. 2019. Algorithms for structure comparison and analysis: homology modelling of proteins. In: Encyclopedia of Bioinformatics and Computational Biology. Vol. 1, pp. 38-61 Elsevier.

  • Wu, R., Trubl, G., Taş, N. and Jansson, J. K. 2022. Permafrost as a potential pathogen reservoir. One Earth, 5(4): 351-360.

  • Yayan, J., Ghebremedhin, B. and Rasche, K. 2015. Antibiotic resistance of Pseudomonas aeruginosa in pneumonia at a Single University Hospital Center in Germany over a 10-year period. PloS One, 10(10): e0139836. DOI: 10.1371 journal.pone.0139836.

  • Yu, C-S., Chen, Y-C., Lu, C-H. and Hwang, J-K. 2006. Prediction of protein subcellular localization. Proteins, 64(3): 643-651.

  • Yu, C-S., Lin, C-J. and Hwang, J-K. 2004. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci., 13(5): 1402-1406.

  • Zhang, C., Vasmatzis, G., Cornette, J. L. and DeLisi, C. 1997. Determination of atomic desolvation energies from the structures of crystallized proteins. J. Mol. Biol., 267(3): 707-726.

  • Zhang, S., Yang, G., Hou, S., Zhang, T., Li, Z. and Liang, F. 2018. Distribution of ARGs and MGEs among glacial soil, permafrost, and sediment using metagenomic analysis. Environ. Pollut., 234: 339-346.


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  • Predicting Drug Targets from Hypothetical Proteins of Pseudomonas Sp. Released from Permafrost Thawing under Impact of Climate Change

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Authors

Arunima Bhattacharya
Post-Graduate Department of Biotechnology, St. Xavier’s College (Autonomous), Kolkata, West Bengal, India
Sarmishta Mukhopadhyay
Post-Graduate Department of Biotechnology, St. Xavier’s College (Autonomous), Kolkata, West Bengal, India
Sayak Ganguli
Post-Graduate Department of Biotechnology, St. Xavier’s College (Autonomous), Kolkata, West Bengal, India

Abstract


One of the major consequences of the increase in global temperature is the thawing of permafrost, which is predicted to cause huge imbalances in natural ecosystems. The results of permafrost thawing is the resurface of quiescent psychrotolerant microbes which have been shown to be storehouses of antibiotic resistance genes (ARGs). Such superbugs, even if non-pathogenic, can transfer the ARGs to active pathogens, aggravating the existing public health crisis of antibiotic resistance. It is thus imperative to explore novel drug targets. Like most other organisms, bacteria possess coding sequences in the genome whose cellular and biochemical functions remain to be predicted. Functional annotation of such hypothetical proteins and their subsequent exploration as drug targets can thus be attempted as a novel computer-aided drug discovery approach. In this paper, we propose an in-silico pipeline for characterisation and functional annotation of hypothetical proteins using Pseudomonas aeruginosa, a multidrug-resistant WHO-listed critical priority pathogen. We then explore their potential as drug targets with small molecules of plant origin. Our results show considerable interactions between the proteins and the small molecules, including successful molecular docking, establishing a successful pipeline which may be useful in small molecule-based drug discovery in the near future.

Keywords


Permafrost thawing, Antibiotic resistance, Pseudomonas aeruginosa, Hypothetical protein

References