Application of fragment based virtual screening towards inhibition of bacterial N-acetyglucosaminidase^$

References

• J. Davies and D. Davies, Origins and evolution of antibiotic resistance, Microbiol. Mol. Biol. Rev. 74 (2010), pp. 417‐433.
   PubMed Web of Science ®Google Scholar
• W. Vollmer, D. Blanot, and M.A. de Pedro, Peptidoglycan structure and architecture, FEMS Microbiol. Rev. 32 (2008), pp. 149–167.
   Google Scholar
• A.L. Lovering, S.S. Safadi, and N.C.J. Strynadka, Structural perspective of peptidoglycan biosynthesis and assembly, Ann. Rev. Biochem. 81 (2012), pp. 451–478.
   PubMed Web of Science ®Google Scholar
• M.G. Pinho, M. Kjos, and J.V. Veening, How to get (a)round: Mechanisms controlling growth and division of coccoid bacteria, Nat. Rev. Microbiol. 9 (2013), pp. 601–614.
   Google Scholar
• A. Typas, M. Banzhaf, C.A. Gross, and W. Vollmer, From the regulation of peptidoglycan synthesis to bacterial growth and morphology, Nat. Rev. Microbiol. 10 (2011), pp. 123–136.
   Google Scholar
• W. Vollmer, B. Joris, P. Charlier, and S. Foster, Bacterial peptidoglycan(murein) hydrolases, FEMS Microbiol. Rev. 32 (2008), pp. 259–286.
   Google Scholar
• S. Zoll, B. Patzold, M. Schlag, F. Gotz, H. Kalbacher, and T. Stehle, Structural basis of cell wall cleavage by a staphylococcal autolysin, PLoS Pathog. 6 (2010). Available at http://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1000807.
   Google Scholar
• R. Biswas, L. Voggu, U.K. Simon, P. Hentschel, G. Thumm, and F. Gotz, Activity of the major staphylococcal autolysin Atl, FEMS Microbiol. Lett. 259 (2006), pp. 260–268.
   Google Scholar
• W. Hashimoto, A. Ochiai, K. Momma, T. Itoh, B. Mikami, Y. Maruyama, and K. Murata, Crystal structure of the glycosidase family 73 peptidoglycan hydrolase FlgJ, Biochem. Biophys. Res. Commun. 381 (2009), pp. 16–21.
   PubMed Web of Science ®Google Scholar
• M. Bublitz, L. Polle, C. Holland, D.W. Heinz, M. Nimtz, and W.D. Schubert, Structural basis for autoinhibition and activation of Auto, a virulence-associated peptidoglycan hydrolase of Listeria monocytogenes, Mol. Microbiol. 71 (2009), pp. 1509–1522.
   Web of Science ®Google Scholar
• X.H. Bai, H.J. Chen, Y.L. Jiang, Z. Wen, Y. Huang, W. Cheng, Q. Li, L. Qi, J.R. Zhang,Y. Chen, and C.Z. Zhou, Structure of pneumococcal peptidoglycan hydrolase LytB reveals insights into the bacterial cell wall remodeling and pathogenesis, J. Biol. Chem. 289 (2014), pp. 23403–23416.
   Web of Science ®Google Scholar
• A. Lipski, M. Herve, V. Lombard, D. Nurizzo, D. Mengin-Lecreulx, Y. Bourne, and F. Vincent, Structural and biochemical characterization of the beta-N-acetylglucosaminidase from Thermotoga maritima: Toward rationalization of mechanistic knowledge in the GH73 family, Glycobiology 25 (2015), pp. 319–330.
   Google Scholar
• P. Zaloba, B.A. Bailey-Elkin, M. Derksen, and B.L. Mark, Structural and biochemical insights into the peptidoglycan hydrolase domain of FlgJ from Salmonella typhimurium, Plos One 11 (2016). Available at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149204.
   Google Scholar
• E. Tamai, H. Sekiya, E. Goda, N. Makihata, J. Maki, H. Yoshida, and S. Kamitori, Structural and biochemical characterization of the Clostridium perfringens autolysin catalytic domain, FEBS Lett. 591 (2017), pp. 231–239.
   Google Scholar
• M. Mihelič, K. Vlahoviček-Kahlina, M. Renko, S. Mesnage, A. Doberšek, A. Taler-Verčič, and D. Turk, The mechanism behind the selection of two different cleavage sites in NAG-NAM polymers, IUCrJ. 4 (2017), pp. 185–198.
   Google Scholar
• S. Nayyab, M. O’Connor, J. Brewster, J. Gravier, M. Jamieson, E. Magno, R.D. Miller, D. Phelan, K. Roohani, P. Williard, A. Basu, and C.W. Reid, Diamide inhibitors of the Bacillus subtilis N‑acetylglucosaminidase LytG that exhibit antibacterial activity, ACS Infect. Dis. 3 (2017), pp. 421–427.
   Google Scholar
• F.M. Büttner, S. Zoll, M. Nega, F. Götz, and T. Stehle, Structure-function analysis of Staphylococcus aureus amidase reveals the determinants of peptidoglycan recognition and cleavage, J. Biol. Chem. 280 (2014), pp. 11083–11094.
   Web of Science ®Google Scholar
• P.Y. Chung and Y.S. Toh, Anti-biofilm agents: Recent breakthrough against multi-drug resistant Staphylococcus aureus, Pathog. Dis. 70 (2014), pp. 231–239.
   Google Scholar
• F.A. Herlihey, P.J. Moynihan, and A.J. Clarke, The essential protein for bacterial flagella formation FlgJ functions as a β-N-acetylglucosaminidase, J. Biol. Chem. 289 (2014), pp. 31029–31042.
   Web of Science ®Google Scholar
• K.J. Yokoi, K. Sugahara, A. Iguchi, G. Nishitani, M. Ikeda, T. Shimada, N. Inagaki, A. Yamakawa, A. Taketo, and K. Kodaira, Molecular properties of the putative autolysin Atl(WM) encoded by Staphylococcus warneri M: Mutational and biochemical analyses of the amidase and glucosaminidase domains, Gene 416 (2008), pp. 66–76.
   Google Scholar
• N. Inagaki, A. Iguchi, T. Yokoyama, K.J. Yokoi, Y. Ono, A. Yamakawa, A. Taketo, and K. Kodaira, Molecular properties of the glucosaminidase AcmA from Lactococcus lactis MG1363: Mutational and biochemical analyses, Gene 447 (2009), pp. 61–71.
   Google Scholar
• Y. Maruyama, A. Ochiai, T. Itoh, B. Mikami, W. Hashimoto, and K. Murata, Mutational studies of the peptidoglycan hydrolase FlgJ of Sphingomonas sp. strain A1, J. Basic Microbiol. 50 (2010), pp. 311–317.
   PubMed Web of Science ®Google Scholar
• J. Borišek, S. Pintar, M. Ogrizek, D. Turk, A. Perdih, and M. Novič, A water-assisted catalytic mechanism in glycoside hydrolases demonstrated on the Staphylococcus aureus autolysin E, ACS Catal. 8 (2018), pp. 4334–4345.
   Web of Science ®Google Scholar
• C.W. Murray and D.C Rees, The rise of fragment-based drug discovery, Nat. Chem. 3 (2009), pp.187–192.
   Google Scholar
• D.A. Erlanson, W. Jahnke, R. Mannhold, H. Kubinyi, and G. Folkers, Fragment-based Drug Discovery: Lessons and Outlook, John Wiley & Sons, Weinheim, 2016.
   Google Scholar
• A. Chavanieu and M. Pugnière, Developments in SPR fragment screening, Expert Opin. Drug Discov. 11 (2016), pp. 489–499.
   Google Scholar
• K. Retra, H. Irth and J.E. van Muijlwijk-Koezen, Surface plasmon resonance biosensor analysis as a useful tool in FBDD, Drug Discov. Today Technol. 7 (2010), pp. 181–187.
   Google Scholar
• J. Borišek, S. Pintar, M. Ogrizek, S. Golič Grdadolnik, V. Hodnik, D. Turk, and M. Novič. Discovery of benzamidopiperidinylureidophenyls as prospective inhibitors of bacterial autolysin E from Staphylococcus aureus, accepted. Available at: https://doi.org/10.1080/14756366.2018.1493474
   Google Scholar
• Discovery Studio Client 4.1.0. 14169, Dassault Systèmes BIOVIA, San Diego, 2014; software available at http://accelrys.com.
   Google Scholar
• B.R. Brooks, C.L. Brooks III, A.D. Mackerell, L. Nilsson, R.J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch A. Caflisch, L. Caves, Q. Cui, A.R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R.W. Pastor, C.B. Post, J.Z. Pu, M. Schaefer, B. Tidor, R.M. Venable, H.L. Woodcock, X. Wu, W. Yang, D.M. York, and M. Karplus, CHARMM: The biomolecular simulation program, J. Comp. Chem. 30 (2009), pp. 1545–1615.
   PubMed Web of Science ®Google Scholar
• T. Tibaut, J. Borišek, M. Novič, and D. Turk, Comparison of in silico tools for binding site prediction applied for structure-based design of autolysin inhibitors, SAR QSAR Environ. Res. 7 (2016), pp. 573–587.
   Google Scholar
• Make Receptor 3.2.0.2, OpenEye Scientific Software, Inc., Santa Fe, NM, USA, 2017; software available at www.eyesopen.com.
   Google Scholar
• P.C. Hawkins, A.G. Skillman, G.L. Warren, B.A. Ellingson, and M.T. Stahl, Conformer generation with OMEGA: Algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database, J. Chem. Inf. Model. 50 (2010), pp. 572–584.
   PubMed Web of Science ®Google Scholar
• P.C. Hawkins and A. Nicholis, Conformer generation with OMEGA: Learning from the data set and the analysis of failures, J. Chem. Inf. Model. 52 (2012), pp. 2919–2936.
   PubMed Web of Science ®Google Scholar
• OMEGA 2.5.1.4. OpenEye Scientific Software, Inc., Santa Fe, NM, 2017; software available at www.eyesopen.com.
   Google Scholar
• M. McGann, FRED and HYBRID docking performance on standardized datasets, J. Comput. Aided Mol. Des. 26 (2012), pp. 897–906.
   PubMed Web of Science ®Google Scholar
• G.B. McGaughey, R.P. Sheridan, C.I. Bayly, J.C. Culberson, C. Kreatsoulas, S. Lindsley, V. Maiorov, J.F. Truchon, and W.D. Cornell, Comparison of topological, shape, and docking methods in virtual screening, J. Chem. Inf. Model. 47 (2007), pp. 1504–1519.
   PubMed Web of Science ®Google Scholar
• M. McGann, FRED pose prediction and virtual screening accuracy, J. Chem. Inf. Model. 51 (2011), pp. 578–596.
   PubMed Web of Science ®Google Scholar
• M.R. McGann, H.R. Almond, A. Nicholls, J.A. Grant, and F.K. Brown, Gaussian docking functions, Biopolymers 68 (2003), pp. 76–90.
   PubMed Web of Science ®Google Scholar
• FRED 3.2.0.2. OpenEye Scientific Software, Inc., Santa Fe, NM, 2017; software available at www.eyesopen.com.
   Google Scholar
• VIDA 4.3.0.4. OpenEye Scientific Software, Inc., Santa Fe, NM, 2017; software available at www.eyesopen.com.
   Google Scholar