Abstract
Drug-resistant infections have become a major concern to human health worldwide, and the number of resistant bacteria is increasing each day. The conventional drug designing approaches are time-consuming and involve huge investment in addition to frequent failures at the clinical trial phase due to unwanted side effects. Because of these reasons, pharmaceutical companies are losing interest to invest in antibiotic research. Modern computational approaches have made the early process of drug target identification and lead compound optimization a lot easier. The anti-virulence strategy of target identification has proved to be safer as compared to the bactericidal or bacteriostatic drugs, since the chance of resistance development would be less due to non-interference with normal bacterial growth and survival. Identification of druggable targets and the use of chemical compound databases and computational tools made it possible to screen millions of molecules within a reasonably short time, taking care of individual ADMET properties. The early detection of potential drug targets and lead compounds is highly desirous in antibiotic research as it demands less time and cost. Therefore, a healthy collaboration between computational and experimental researchers is the future of novel antibiotic discovery.
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References
Anitha P, Anbarasu A, Ramaiah S (2014) Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii. Comput Biol Med 48:17–27. doi:10.1016/j.compbiomed.2014.02.009
Armstrong GD, Rowe PC, Goodyer P, Orrbine E, Klassen TP, Wells G, MacKenzie A, Lior H, Blanchard C, Auclair F, Thompson B, Rafter DJ, McLaine PN (1995) A phase I study of chemically synthesized verotoxin (Shiga-like toxin) Pk-trisaccharide receptors attached to chromosorb for preventing hemolytic-uremic syndrome. J Infect Dis 171:1042–1045. doi:10.1093/infdis/171.4.1042
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2. doi:10.1186/1471-2105-4-2
Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204–3218. doi:10.1039/B409813G
Bohm HJ (1992a) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78
Bohm HJ (1992b) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6:593–606
Boucher HW, Talbot GH, Benjamin DK Jr, Bradley J, Guidos RJ, Jones RN, Murray BE, Bonomo RA, Gilbert D (2013) 10 x ‘20 Progress--development of new drugs active against gram-negative bacilli: an update from the Infectious Diseases Society of America. Clin Infect Dis 56:1685–1694. doi:10.1093/cid/cit152
Brazas MD, Hancock RE (2005) Using microarray gene signatures to elucidate mechanisms of antibiotic action and resistance. Drug Discov Today 10:1245–1252. doi:10.1016/S1359-6446(05)03566-X
Brooks BR, Brooks CL 3rd, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614. doi:10.1002/jcc.21287
Chan PF, Macarron R, Payne DJ, Zalacain M, Holmes DJ (2002) Novel antibacterials: a genomics approach to drug discovery. Curr Drug Targets Infect Dis 2:291–308
Chanumolu SK, Rout C, Chauhan RS (2012) UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria. PloS One 7:e32833. doi:10.1371/journal.pone.0032833
Chen X, Ung CY, Chen Y (2003) Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients? Natl Prod Rep 20:432–444. doi:10.1039/B303745B
Chen X, Zhou H, Liu YB, Wang JF, Li H, Ung CY, Han LY, Cao ZW, Chen YZ (2006) Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br J Pharmacol 149:1092–1103. doi:10.1038/sj.bjp.0706945
Chung BK, Dick T, Lee DY (2013) In silico analyses for the discovery of tuberculosis drug targets. J Antimicrob Chemother 68:2701–2709. doi:10.1093/jac/dkt273
Davies SC, Fowler T, Watson J, Livermore DM, Walker D (2013) Annual report of the Chief Medical Officer: infection and the rise of antimicrobial resistance. Lancet 381:1606–1609. doi:10.1016/S0140-6736(13)60604-2
Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4:P3. doi:10.1186/gb-2003-4-5-p3
Domagk G (1935) Ein Beitrag zur Chemotherapie der bakteriellen Infektionen. Dtsch Med Wochenschr 61:250. doi:10.1055/s-0028-1129486
Durrant JD, Amaro RE, McCammon JA (2009) AutoGrow: a novel algorithm for protein inhibitor design. Chem Biol Drug Des 73:168–178. doi:10.1111/j.1747-0285.2008.00761.x
Durrant JD, Lindert S, McCammon JA (2013) AutoGrow 3.0: an improved algorithm for chemically tractable, semi-automated protein inhibitor design. J Mol Graph Model 44:104–112. doi:10.1016/j.jmgm.2013.05.006
Ehrlich P, Hata S (1910) Die Experimentelle Chemotherapie der Spirilosen. Julius Springer, Berlin
Felise HB, Nguyen HV, Pfuetzner RA, Barry KC, Jackson SR, Blanc MP, Bronstein PA, Kline T, Miller SI (2008) An inhibitor of gram-negative bacterial virulence protein secretion. Cell Host Microbe 4:325–336. doi:10.1016/j.chom.2008.08.001
Field D, Feil EJ, Wilson GA (2005) Databases and software for the comparison of prokaryotic genomes. Microbiology 151:2125–2132. doi:10.1099/mic.0.28006-0
Fischer HP (2001) The impact of expression profiling technologies on antimicrobial target identification and validation. Drug Discov Today 6:1149–1150. doi:10.1016/S1359-6446(01)02047-5
Fleming A (2001) On the antibacterial action of cultures of a penicillium, with special reference to their use in the isolation of B. influenzae. 1929. Bull World Health Org 79:780–790. doi:10.1590/S0042-96862001000800017
Frecer V, Ho B, Ding JL (2004) De novo design of potent antimicrobial peptides. Antimicrob Agents Chemother 48:3349–3357. doi:10.1128/AAC.48.9.3349-3357.2004
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. doi:10.1021/jm0306430
Fritz B, Raczniak GA (2002) Bacterial genomics: potential for antimicrobial drug discovery. BioDrugs 16:331–337
Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics 9:104. doi:10.1186/1471-2105-9-104
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107. doi:10.1093/nar/gkr777
Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2015) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. doi:10.1093/nar/gkv1072
Guex N, Peitsch MC, Schwede T (2009) Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis 30(Suppl 1):S162–S173. doi:10.1002/elps.200900140
Hampton T (2013) Report reveals scope of US antibiotic resistance threat. JAMA 310(16):1661–1663. doi:10.1001/jama.2013.280695
Han LY, Zheng CJ, Xie B, Jia J, Ma XH, Zhu F, Lin HH, Chen X, Chen YZ (2007) Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discov Today 12:304–313. doi:10.1016/j.drudis.2007.02.015
Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447. doi:10.1021/ct700301q
Hilpert K, Elliott MR, Volkmer-Engert R, Henklein P, Donini O, Zhou Q, Winkler DF, Hancock RE (2006) Sequence requirements and an optimization strategy for short antimicrobial peptides. Chem Biol 13:1101–1107. doi:10.1016/j.chembiol.2006.08.014
Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8:R183. doi:10.1186/gb-2007-8-9-r183
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Prot 4:44–57. doi:10.1038/nprot.2008.211
Hung DT, Shakhnovich EA, Pierson E, Mekalanos JJ (2005) Small-molecule inhibitor of Vibrio cholerae virulence and intestinal colonization. Science 310:670–674. doi:10.1126/science.1116739
Jabes D (2011) The antibiotic R&D pipeline: an update. Curr Opin Microbiol 14:564–569. doi:10.1016/j.mib.2011.08.002
Jensen NH, Roth BL (2008) Massively parallel screening of the receptorome. Comb Chem High Throughput Screen 11:420–426. doi:10.2174/138620708784911483
Ji ZL, Wang Y, Yu L, Han LY, Zheng CJ, Chen YZ (2006) In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction. Toxicol Lett 164:104–112. doi:10.1016/j.toxlet.2005.11.017
Kauppi AM, Nordfelth R, Uvell H, Wolf-Watz H, Elofsson M (2003) Targeting bacterial virulence: inhibitors of type III secretion in Yersinia. Chem Biol 10:241–249. doi:10.1016/S1074-5521(03)00046-2
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Prot 10:845–858. doi:10.1038/nprot.2015.053
Kline T, Felise HB, Barry KC, Jackson SR, Nguyen HV, Miller SI (2008) Substituted 2-imino-5-arylidenethiazolidin-4-one inhibitors of bacterial type III secretion. J Med Chem 51:7065–7074. doi:10.1021/jm8004515
Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A (2011) From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 74:2554–2574. doi:10.1016/j.jprot.2011.05.011
Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acid Res 42:D1091–D1097. doi:10.1093/nar/gkt1068
Lesic B, Lepine F, Deziel E, Zhang J, Zhang Q, Padfield K, Castonguay MH, Milot S, Stachel S, Tzika AA, Tompkins RG, Rahme LG (2007) Inhibitors of pathogen intercellular signals as selective anti-infective compounds. PLoS Pathog 3:1229–1239. doi:10.1371/journal.ppat.0030126
Li Q, Lai L (2007) Prediction of potential drug targets based on simple sequence properties. BMC Bioinformatics 8:353. doi:10.1186/1471-2105-8-353
Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acid Res 34(Web Server issue):W219–W224. doi:10.1093/nar/gkl114
Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H (2010) PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acid Res 38(Web Server issue):W609–W614. doi:10.1093/nar/gkq300
Mandal S, Moudgil M, Mandal SK (2009) Rational drug design. Eur J Pharmacol 625:90–100. doi:10.1016/j.ejphar.2009.06.065
Moellering RC Jr (2011) Discovering new antimicrobial agents. Int J Antimicrob Agents 37:2–9. doi:10.1016/j.ijantimicag.2010.08.018
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 3:2785–2791. doi:10.1002/jcc.21256
Muschiol S, Bailey L, Gylfe A, Sundin C, Hultenby K, Bergstrom S, Elofsson M, Wolf-Watz H, Normark S, Henriques-Normark B (2006) A small-molecule inhibitor of type III secretion inhibits different stages of the infectious cycle of Chlamydia trachomatis. Proc Natl Acad Sci USA 103:14566–14571. doi:10.1073/pnas.0606412103
Nielsen M, Lundegaard C, Lund O, Petersen TN (2010) CPHmodels-3.0--remote homology modeling using structure-guided sequence profiles. Nucleic Acid Res 38(Web Server issue):W576–W581. doi:10.1093/nar/gkq535
Palumbi SR (2001) Humans as the world’s greatest evolutionary force. Science 293:1786–1790. doi:10.1126/science.293.5536.1786
Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL (2007) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Discov 6:29–40. doi:10.1038/nrd2201
Pieper U, Webb BM, Dong GQ, Schneidman-Duhovny D, Fan H, Kim SJ, Khuri N, Spill YG, Weinkam P, Hammel M, Tainer JA, Nilges M, Sali A (2014) ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acid Res 42(Database issue):D336–D346. doi:10.1093/nar/gkh095
Power E (2006) Impact of antibiotic restrictions: the pharmaceutical perspective. Clin Microbiol Infect 12(Suppl 5):25–34. doi:10.1111/j.1469-0691.2006.01528.x
Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2010) Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18:2225–2231. doi:10.1016/j.bmc.2010.01.068
Projan SJ (2003) Why is big Pharma getting out of antibacterial drug discovery? Curr Opin Microbiol 6:427–430. doi:10.1016/j.mib.2003.08.003
Rasko DA, Sperandio V (2010) Anti-virulence strategies to combat bacteria-mediated disease. Nat Rev Drug Discov 9:117–128. doi:10.1038/nrd3013
Rasko DA, Moreira CG, de Li R, Reading NC, Ritchie JM, Waldor MK, Williams N, Taussig R, Wei S, Roth M, Hughes DT, Huntley JF, Fina MW, Falck JR, Sperandio V (2008) Targeting QseC signaling and virulence for antibiotic development. Science 321:1078–1080. doi:10.1126/science.1160354
Rice LB (2008) The Maxwell Finland Lecture: for the duration-rational antibiotic administration in an era of antimicrobial resistance and clostridium difficile. Clin Infect Dis 46:491–496. doi:10.1086/526535
Russ AP, Lampel S (2005) The druggable genome: an update. Drug Discov Today 10:1607–1610. doi:10.1016/S1359-6446(05)03666-4
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. doi:10.1101/gr.1239303
Silver LL (2011) Challenges of antibacterial discovery. Clin Microbiol Rev 24:71–109. doi:10.1128/CMR.00030-10
Sipahi OR (2008) Effects of antibiotic resistance on industrial antibiotic R&D. Expert Rev Anti Infect Ther 6:523–539
Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395. doi:10.1124/pr.112.007336
Speck-Planche A, Cordeiro MN (2015) A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol Biol 1260:45–64. doi:10.1007/978-1-4939-2239-0_4
Speck-Planche A, Kleandrova VV, Cordeiro MN (2013) Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg Med Chem 21:2727–2732. doi:10.1016/j.bmc.2013.03.015
Spellberg B, Guidos R, Gilbert D, Bradley J, Boucher HW, Scheld WM, Bartlett JG, Edwards J Jr, Infectious Diseases Society of America (2008) The epidemic of antibiotic-resistant infections: a call to action for the medical community from the Infectious Diseases Society of America. Clin Infect Dis 46(2):155–164. doi:10.1086/524891
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acid Res 43:D447–D452. doi:10.1093/nar/gku1003
Tang CM, Moxon ER (2001) The impact of microbial genomics on antimicrobial drug development. Annu Rev Genomics Hum Genet 2:259–269. doi:10.1146/annurev.genom.2.1.259
Tenorio-Borroto E, Penuelas Rivas CG, Vasquez Chagoyan JC, Castanedo N, Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2012) ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg Med Chem 20:6181–6194. doi:10.1016/j.bmc.2012.07.020
Trachtman H, Cnaan A, Christen E, Gibbs K, Zhao S, Acheson DW, Weiss R, Kaskel FJ, Spitzer A, Hirschman GH, Investigators of the HUSSPMCT (2003) Effect of an oral Shiga toxin-binding agent on diarrhea-associated hemolytic uremic syndrome in children: a randomized controlled trial. JAMA 290:1337–1344. doi:10.1001/jama.290
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. doi:10.1002/jcc.21334
Veenendaal AK, Sundin C, Blocker AJ (2009) Small-molecule type III secretion system inhibitors block assembly of the Shigella type III secreton. J Bacteriol 191:563–570. doi:10.1128/JB.01004-08
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623. doi:10.1002/prot.10465
Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH (2012) PubChem’s BioAssay Database. Nucleic Acid Res 40(Database issue):D400–D412. doi:10.1093/nar/gkr1132
Webb B, Sali A (2014) Comparative protein structure modeling using MODELLER. Curr Prot Bioinfo 47:5.6.1–5.6.32. doi:10.1002/0471250953.bi0506s47
Wu S, Zhang Y (2007) LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acid Res 35:3375–3382. doi:10.1093/nar/gkm251
Yuan Y, Pei J, Lai L (2011) LigBuilder 2: a practical de novo drug design approach. J Chem Info Model 51:1083–1091. doi:10.1021/ci100350u
Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40. doi:10.1186/1471-2105-9-40
Zsoldos Z, Reid D, Simon A, Sadjad SB, Johnson AP (2007) eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 26:198–212. doi:10.1016/j.jmgm.2006.06.002
Acknowledgments
The authors wish to thank the Indian Council of Medical Research (ICMR) and the government of India for providing necessary funds and facilities. RSM thanks ICMR for funding support (IRIS ID: 2013–1551G).
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Mandal, R.S., Das, S. (2017). In Silico Approaches Toward Combating Antibiotic Resistance. In: Arora, G., Sajid, A., Kalia, V. (eds) Drug Resistance in Bacteria, Fungi, Malaria, and Cancer. Springer, Cham. https://doi.org/10.1007/978-3-319-48683-3_25
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