Login Register Cart 0
Generic placeholder image

Infectious Disorders - Drug Targets

Editor-in-Chief

ISSN (Print): 1871-5265
ISSN (Online): 2212-3989

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Mining the Proteome of Streptococcus mutans for Putative Drug Targets

Author(s): Shakti Chandra Vadhana Marimuthu, Haribalaganesh Ravinarayanan, Joseph Christina Rosy and Krishnan Sundar*

Volume 21, Issue 3, 2021

Published on: 22 June, 2020

Page: [429 - 438] Pages: 10

DOI: 10.2174/1871526520666200622143316

Price: $65

Abstract

Background: Dental caries is the most common and one of the prevalent diseases in the world. Streptococcus mutans is one of the major oral pathogens that cause dental caries by forming a biofilm on dental tissues, degrading dental enamel and consequent cavitation in the tissue. In vitro selection of drug targets is a laborious and expensive process and therefore, computational methods are preferable for target identification at the initial stage.

Objective: The present research aims to find new drug targets in S. mutans by using subtractive proteomics analysis, which implements various bioinformatics tools and databases.

Methods: The proteome of S. mutans UA159 was mined for novel drug targets using computational tools and databases such as: CD-HIT, BLASTP, DEG, KAAS and CELL2GO.

Results: Out of 1953 proteins of S. mutans UA159, proteins that are redundant, homologous to human and non-essential to the pathogen were eliminated. Around 178 proteins already available in drug target repositories were also eliminated. Possible functions and subcellular localization of 32 uncharacterized proteins were predicted. Substantially, 13 proteins were identified as novel drug targets in S. mutans UA159 that can be targeted by various drugs against dental caries.

Conclusion: This study will effectuate the development of novel therapeutic agents against dental caries and other Streptococcal infections.

Keywords: Streptococcus mutans, dental caries, bioinformatics, subtractive proteomics, database of essential genes, CELLO2- GO, drug targets.

Graphical Abstract

[1]
Xu, X.; Zhou, X.D.; Wu, C.D. The tea catechin epigallocatechin gallate suppresses cariogenic virulence factors of Streptococcus mutans. Antimicrob. Agents Chemother., 2011, 55(3), 1229-1236.
[http://dx.doi.org/10.1128/AAC.01016-10] [PMID: 21149622]
[2]
da Silva, A.C.B.; da Silva, D.R.; de Macêdo Ferreira, S.A.; Agripino, G.G.; Albuquerque, A.R.; do Rêgo, T.G. In silico approach for the identification of potential targets and specific antimicrobials for Streptococcus mutans. Adv. Biosci. Biotechnol., 2014, 5, 373-385.
[http://dx.doi.org/10.4236/abb.2014.54045]
[3]
Smith, D.J.; Taubman, M.A. Experimental immunization of rats with a Streptococcus mutans 59-kilodalton glucan-binding protein protects against dental caries. Infect. Immun., 1996, 64(8), 3069-3073.
[http://dx.doi.org/10.1128/IAI.64.8.3069-3073.1996] [PMID: 8757835]
[4]
Marsh, P.D. Microbial ecology of dental plaque and its significance in health and disease. Adv. Dent. Res., 1994, 8(2), 263-271.
[http://dx.doi.org/10.1177/08959374940080022001] [PMID: 7865085]
[5]
Zhang, Z.; Nadezhina, E.; Wilkinson, K.J. Quantifying diffusion in a biofilm of Streptococcus mutans. Antimicrob. Agents Chemother., 2011, 55(3), 1075-1081.
[http://dx.doi.org/10.1128/AAC.01329-10] [PMID: 21189346]
[6]
Koo, H.; Xiao, J.; Klein, M.I.; Jeon, J.G. Exopolysaccharides produced by Streptococcus mutans glucosyltransferases modulate the establishment of microcolonies within multispecies biofilms. J. Bacteriol., 2010, 192(12), 3024-3032.
[http://dx.doi.org/10.1128/JB.01649-09] [PMID: 20233920]
[7]
Amer, F.A.; El-Behedy, E.M.; Mohtady, H.A. New targets for antibacterial agents. Biotechnol. Mol. Biol. Rev., 2008, 3, 46-57.
[8]
Cvitkovitch, D.G.; Li, Y.H.; Ellen, R.P. Quorum sensing and biofilm formation in Streptococcal infections. J. Clin. Invest., 2003, 112(11), 1626-1632.
[http://dx.doi.org/10.1172/JCI200320430] [PMID: 14660736]
[9]
Wade, W.G. New aspects and new concepts of maintaining “microbiological” health. J. Dent., 2010, 38(Suppl. 1), S21-S25.
[http://dx.doi.org/10.1016/S0300-5712(10)70007-5] [PMID: 20621241]
[10]
Hopkins, A.L.; Groom, C.R. The druggable genome. Nat. Rev. Drug Discov., 2002, 1(9), 727-730.
[http://dx.doi.org/10.1038/nrd892] [PMID: 12209152]
[11]
Russ, A.P.; Lampel, S. The druggable genome: an update. Drug Discov. Today, 2005, 10(23-24), 1607-1610.
[http://dx.doi.org/10.1016/S1359-6446(05)03666-4] [PMID: 16376820]
[12]
An, J.; Totrov, M.; Abagyan, R. Comprehensive identification of “druggable” protein ligand binding sites. Genome Inform, 2004, 15(2), 31-41.
[PMID: 15706489]
[13]
Hajduk, P.J.; Huth, J.R.; Tse, C. Predicting protein druggability. Drug Discov. Today, 2005, 10(23-24), 1675-1682.
[http://dx.doi.org/10.1016/S1359-6446(05)03624-X] [PMID: 16376828]
[14]
Abi-Said, D.; Anaissie, E.; Uzun, O.; Raad, I.; Pinzcowski, H.; Vartivarian, S. The epidemiology of hematogenous candidiasis caused by different Candida species. Clin. Infect. Dis., 1997, 24(6), 1122-1128.
[http://dx.doi.org/10.1086/513663] [PMID: 9195068]
[15]
Hossain, M.; Mosnaz, A.T.M.J.; Sajib, A.M.; Roy, P.K.; Shakil, S.K.; Ullah, S.M.S.; Prodhan, S.H. Identification of putative drug targets of Listeria monocytogenes F2365 by subtractive genomics approach. J. Biosci. Biotechnol., 2013, 2, 63-71.
[16]
Dutta, A.; Singh, S.K.; Ghosh, P.; Mukherjee, R.; Mitter, S.; Bandyopadhyay, D. In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori. In Silico Biol. (Gedrukt), 2006, 6(1-2), 43-47.
[PMID: 16789912]
[17]
Perumal, D.; Lim, C.S.; Sakharkar, K.R.; Sakharkar, M.K. Differential genome analyses of metabolic enzymes in Pseudomonas aeruginosa for drug target identification. In Silico Biol. (Gedrukt), 2007, 7(4-5), 453-465.
[PMID: 18391237]
[18]
Ravinarayanan, H.; Coico, R.; Sundar, K. Identification of putative therapeutic targets in Candida tropicalis: an in silico approach. Trends in Bioinfo., 2015, 8, 52-62.
[http://dx.doi.org/10.3923/tb.2015.52.62]
[19]
Li, Y.H.; Xu, J.Y.; Tao, L.; Li, X.F.; Li, S.; Zeng, X.; Chen, S.Y.; Zhang, P.; Qin, C.; Zhang, C.; Chen, Z.; Zhu, F.; Chen, Y.Z. SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS One, 2016, 11(8), e0155290.
[http://dx.doi.org/10.1371/journal.pone.0155290] [PMID: 27525735]
[20]
Cai, C.Z.; Han, L.Y.; Ji, Z.L.; Chen, X.; Chen, Y.Z. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res., 2003, 31(13), 3692-3697.
[http://dx.doi.org/10.1093/nar/gkg600] [PMID: 12824396]
[21]
Krogh, A.; Larsson, B.; von Heijne, G.; Sonnhammer, E.L.L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol., 2001, 305(3), 567-580.
[http://dx.doi.org/10.1006/jmbi.2000.4315] [PMID: 11152613]
[22]
Yu, C.S.; Cheng, C.W.; Su, W.C.; Chang, K.C.; Huang, S.W.; Hwang, J.K.; Lu, C.H. CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PLoS One, 2014, 9(6), e99368.
[http://dx.doi.org/10.1371/journal.pone.0099368] [PMID: 24911789]
[23]
Yu, N.Y.; Wagner, J.R.; Laird, M.R.; Melli, G.; Rey, S.; Lo, R.; Dao, P.; Sahinalp, S.C.; Ester, M.; Foster, L.J.; Brinkman, F.S.L. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics, 2010, 26(13), 1608-1615.
[http://dx.doi.org/10.1093/bioinformatics/btq249] [PMID: 20472543]
[24]
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]
[25]
Sarangi, A.N.; Aggarwal, R.; Rahman, Q.; Trivedi, N. Subtractive genomics approach for in silico identification and characterization of novel drug targets in Neisseria meningitides Serogroup B. J. Comput. Sci. Syst. Biol., 2009, 2, 255-258.
[http://dx.doi.org/10.4172/jcsb.1000038]
[26]
Zhang, R.; Ou, H.Y.; Zhang, C.T. DEG: a database of essential genes. Nucleic Acids Res., 2004, 32(Database issue), D271-D272.
[http://dx.doi.org/10.1093/nar/gkh024] [PMID: 14681410]
[27]
Rathi, B.; Sarangi, A.N.; Trivedi, N. Genome subtraction for novel target definition in Salmonella typhi. Bioinformation, 2009, 4(4), 143-150.
[http://dx.doi.org/10.6026/97320630004143] [PMID: 20198190]
[28]
Moriya, Y.; Itoh, M.; Okuda, S.; Yoshizawa, A.C.; Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res., 2007, 35(Web Server issue), W182-5.
[http://dx.doi.org/10.1093/nar/gkm321] [PMID: 17526522]
[29]
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 issue), D1035-D1041.
[http://dx.doi.org/10.1093/nar/gkq1126] [PMID: 21059682]
[30]
Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: Therapeutic target database. Nucleic Acids Res., 2002, 30(1), 412-415.
[http://dx.doi.org/10.1093/nar/30.1.412] [PMID: 11752352]
[31]
Featherstone, J.D.B. The science and practice of caries prevention. J. Am. Dent. Assoc., 2000, 131(7), 887-899.
[http://dx.doi.org/10.14219/jada.archive.2000.0307] [PMID: 10916327]
[32]
Kubo, I.; Muroi, H.; Himejima, M. Antimicrobial activity against Streptococcus mutans of mate tea flavor components. J. Agric. Food Chem., 1993, 41, 107-111.
[http://dx.doi.org/10.1021/jf00025a023]
[33]
Tredwin, C.J.; Scully, C.; Bagan-Sebastian, J.V. Drug-induced disorders of teeth. J. Dent. Res., 2005, 84(7), 596-602.
[http://dx.doi.org/10.1177/154405910508400703] [PMID: 15972585]
[34]
Singh, J.; Kumar, A.; Budhiaraja, S.; Hooda, A. Ethnomedicine: use in Dental Caries. Braz. J. Oral Sci., 2007, 6, 1308-1312.
[35]
Adonizio, A.; Kong, K.F.; Mathee, K. Inhibition of quorum sensing-controlled virulence factor production in Pseudomonas aeruginosa by South Florida plant extracts. Antimicrob. Agents Chemother., 2008, 52(1), 198-203.
[http://dx.doi.org/10.1128/AAC.00612-07] [PMID: 17938186]
[36]
Hosseini, F.; Adlgostar, A.; Sharifnia, F. Antibacterial activity of Pistacia atlantica extracts on Streptococcus mutans Biofilm. Int. Res. J. Biol. Sci., 2013, 2, 1-7.
[37]
Bhattacharya, S.; Ghosh, P.; Banerjee, D.; Banerjee, A.; Ray, S. In silico drug target discovery through proteome mining from M. tuberculosis: An insight into antivirulent therapy. Comb. Chem. High Throughput Screen., 2020, 23(3), 253-268.
[http://dx.doi.org/10.2174/1386207323666200219120903] [PMID: 32072892]
[38]
Munir, A.; Malik, S.I.; Malik, K.A. Proteome mining for the identification of putative drug targets for human pathogen clostridium tetani. Curr Bioinform, 2019, 14, 532-40.
[http://dx.doi.org/10.2174/1574893613666181114095736]
[39]
Iftikhar, R.; Rizwan, M.; Khan, S.; Mehmood, A.; Munir, A. Subtractive proteome mining approach towards unique putative drug targets identification for salmonella typhimurium. Infect. Disord. Drug Targets, 2020, 20(6), 884-892.
[http://dx.doi.org/10.2174/1871526519666191211142758] [PMID: 31823708]
[40]
Azam, S.S.; Shamim, A. An insight into the exploration of druggable genome of Streptococcus gordonii for the identification of novel therapeutic candidates. Genomics, 2014, 104(3), 203-214.
[http://dx.doi.org/10.1016/j.ygeno.2014.07.007] [PMID: 25068724]
[41]
Baseer, S.; Ahmad, S.; Ranaghan, K.E.; Azam, S.S. Towards a peptide-based vaccine against Shigella sonnei: A subtractive reverse vaccinology based approach. Biologicals, 2017, 50, 87-99.
[http://dx.doi.org/10.1016/j.biologicals.2017.08.004] [PMID: 28826780]
[42]
Sanober, G.; Ahmad, S.; Azam, S.S. Identification of plausible drug targets by investigating the druggable genome of MDR Staphylococcus epidermidis. Gene Rep., 2017, 7, 147-153.
[http://dx.doi.org/10.1016/j.genrep.2017.04.008]
[43]
Ain, Q.U.; Ahmad, S.; Azam, S.S. Subtractive proteomics and immunoinformatics revealed novel B-cell derived T-cell epitopes against Yersinia enterocolitica: An etiological agent of Yersiniosis. Microb. Pathog., 2018, 125, 336-348.
[http://dx.doi.org/10.1016/j.micpath.2018.09.042] [PMID: 30273644]
[44]
Khalid, Z.; Ahmad, S.; Raza, S.; Azam, S.S. Subtractive proteomics revealed plausible drug candidates in the proteome of multidrug resistant Corynebacterium diphtheriae. Meta Gene, 2018, 17, 34-42.
[http://dx.doi.org/10.1016/j.mgene.2018.04.008]
[45]
Uddin, R.; Siddiqui, Q.N.; Azam, S.S.; Saima, B.; Wadood, A. Identification and characterization of potential druggable targets among hypothetical proteins of extensively drug resistant Mycobacterium tuberculosis (XDR KZN 605) through subtractive genomics approach. Eur. J. Pharm. Sci., 2018, 114, 13-23.
[http://dx.doi.org/10.1016/j.ejps.2017.11.014] [PMID: 29174549]
[46]
Ahmad, S.; Raza, S.; Ain, Q.U.; Uddin, R.; Rungrotmongkol, T.; Azam, S.S. From phylogeny to protein dynamics: A computational hierarchical quest for potent drug identification against an emerging enteropathogen Yersinia enterocolitica. Mol. Liq., 2018, 265, 372-389.
[http://dx.doi.org/10.1016/j.molliq.2018.06.013]

Rights & Permissions Print Cite
Article Metrics
18
1
© 2024 Bentham Science Publishers | Privacy Policy