Searching hit potential antimicrobials in natural compounds space against biofilm formation

dc.contributor.authorPestana-Nobles, Roberto
dc.contributor.authorLeyva-Rojas, Jorge A.
dc.contributor.authorYosa, Juvenal
dc.description.abstractBiofilms are communities of microorganisms that can colonize biotic and abiotic surfaces and thus play a significant role in the persistence of bacterial infection and resistance to antimicrobial. About 65% and 80% of microbial and chronic infections are associated with biofilm formation, respectively. The increase in infections by multi-resistant bacteria instigates the need for the discovery of novel natural-based drugs that act as inhibitory molecules. The inhibition of diguanylate cyclases (DGCs), the enzyme implicated in the synthesis of the second messenger, cyclic diguanylate (c-di-GMP), involved in the biofilm formation, represents a potential approach for preventing the biofilm development. It has been extensively studied using PleD protein as a model of DGC for in silico studies as virtual screening and as a model for in vitro studies in biofilms formation. This study aimed to search for natural products capable of inhibiting the Caulobacter crescentus enzyme PleD. For this purpose, 224,205 molecules from the natural products ZINC15 database, have been evaluated through molecular docking and molecular dynamic simulation. Our results suggest trans-Aconitic acid (TAA) as a possible starting point for hit-to-lead methodologies to obtain new inhibitors of the PleD protein and hence blocking the biofilm formation.eng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.sourceRevista: Moleculeseng
dc.sourceVol. 25, No. 5334, (2020)
dc.subjectVirtual screeningeng
dc.subjectMolecular dynamicseng
dc.subjectNatural productseng
dc.subjectBinding energyeng
dc.subjectTrans-aconitic acideng
dc.titleSearching hit potential antimicrobials in natural compounds space against biofilm formationeng
dc.type.spaArtículo científicospa
dcterms.referencesFlemming, H.C.; Wingender, J.; Szewzyk, U.; Steinberg, P.; Rice, S.A.; Kjelleberg, S. Biofilms: An emergent form of bacterial life. Nat. Rev. Microbiol. 2016, 14, 563–575.eng
dcterms.referencesYin, W.; Wang, Y.; Liu, L.; He, J. Biofilms: The Microbial “Protective Clothing” in Extreme Environments. Int. J. Mol. Sci. 2019, 20, 3423.eng
dcterms.referencesTolker-Nielsen, T. Biofilm Development. Microbiol. Spectr. 2015, 3.eng
dcterms.referencesDel Pozo, J.L. Biofilm-related disease. Expert Rev. Anti-Infect. Ther. 2018, 16, 51–65.eng
dcterms.referencesFleming, D.; Rumbaugh, K.P. Approaches to Dispersing Medical Biofilms. Microorganisms 2017, 5, 15.eng
dcterms.referencesJamal, M.; Ahmad, W.; Andleeb, S.; Jalil, F.; Imran, M.; Nawaz, M.A.; Hussain, T.; Ali, M.; Rafiq, M.; Kamil, M.A. Bacterial biofilm and associated infections. J. Chin. Med Assoc. 2018, 81, 7–11.eng
dcterms.referencesFernicola, S.; Paiardini, A.; Giardina, G.; Rampioni, G.; Leoni, L.; Cutruzzolà, F.; Rinaldo, S. In Silico Discovery and In Vitro Validation of Catechol-Containing Sulfonohydrazide Compounds as Potent Inhibitors of the Diguanylate Cyclase PleD. J. Bacteriol. 2016, 198, 147–156.eng
dcterms.referencesCai, Y.M.; Hutchin, A.; Craddock, J.; Walsh, M.A.; Webb, J.S.; Tews, I. Differential impact on motility and biofilm dispersal of closely related phosphodiesterases in Pseudomonas aeruginosa. Sci. Rep. 2020, 10, 6232.eng
dcterms.referencesSeshasayee, A.S.; Fraser, G.M.; Luscombe, N.M. Comparative genomics of cyclic-di-GMP signalling in bacteria: post-translational regulation and catalytic activity. Nucleic Acids Res. 2010, 38, 5970–5981.eng
dcterms.referencesGalperin, M.Y. A census of membrane-bound and intracellular signal transduction proteins in bacteria: Bacterial IQ, extroverts and introverts. BMC Microbiol. 2005, 5, 35.eng
dcterms.referencesRömling, U.; Galperin, M.Y.; Gomelsky, M. Cyclic di-GMP: The First 25 Years of a Universal Bacterial Second Messenger. Microbiol. Mol. Biol. Rev. 2013, 77, 1–52.eng
dcterms.referencesFeirer, N.; Kim, D.; Xu, J.; Fernandez, N.; Waters, C.M.; Fuqua, C. The Agrobacterium tumefaciens CheY-like protein ClaR regulates biofilm formation. Microbiology 2017, 163, 1680–1691.eng
dcterms.referencesAlviz-Gazitua, P.; Fuentes-Alburquenque, S.; Rojas, L.A.; Turner, R.J.; Guiliani, N.; Seeger, M. The Response of Cupriavidus metallidurans CH34 to Cadmium Involves Inhibition of the Initiation of Biofilm Formation, Decrease in Intracellular c-di-GMP Levels, and a Novel Metal Regulated Phosphodiesterase. Front. Microbiol. 2019, 10, 1499.eng
dcterms.referencesJenal, U.; Malone, J. Mechanisms of cyclic-di-GMP signaling in bacteria. Annu. Rev. Genet. 2006, 40, 385–407.eng
dcterms.referencesPaul, R.; Weiser, S.; Amiot, N.C.; Chan, C.; Schirmer, T.; Giese, B.; Jenal, U. Cell cycle-dependent dynamic localization of a bacterial response regulator with a novel di-guanylate cyclase output domain. Genes Dev. 2004, 18, 715–727.eng
dcterms.referencesSkerker, J.M.; Laub, M.T. Cell-cycle progression and the generation of asymmetry in Caulobacter crescentus. Nat. Rev. Microbiol. 2004, 2, 325–337.eng
dcterms.referencesEntcheva-Dimitrov, P.; Spormann, A.M. Dynamics and Control of Biofilms of the Oligotrophic Bacterium Caulobacter crescentus. J. Bacteriol. 2004, 186, 8254–8266.eng
dcterms.referencesValentini, M.; Filloux, A. Biofilms and Cyclic di-GMP (c-di-GMP) Signaling: Lessons from Pseudomonas aeruginosa and Other Bacteria. J. Biol. Chem. 2016, 291, 12547–12555.eng
dcterms.referencesLage, O.M.; Ramos, M.C.; Calisto, R.; Almeida, E.; Vasconcelos, V.; Vicente, F. Current screening methodologies in drug discovery for selected human diseases. Mar. Drugs 2018, 16, 279.eng
dcterms.referencesRossiter, S.E.; Fletcher, M.H.; Wuest, W.M. Natural Products as Platforms to Overcome Antibiotic Resistance. Chem. Rev. 2017, 117, 12415–12474.eng
dcterms.referencesHerrmann, J.; Fayad, A.A.; Müller, R. Natural products from myxobacteria: Novel metabolites and bioactivities. Nat. Prod. Rep. 2017, 34, 135–160.eng
dcterms.referencesRodrigues, T.; Reker, D.; Schneider, P.; Schneider, G. Counting on natural products for drug design. Nat. Chem. 2016, 8, 531–541.eng
dcterms.referencesNofiani, R.; Weisberg, A.J.; Tsunoda, T.; Panjaitan, R.G.P.; Brilliantoro, R.; Chang, J.H.; Philmus, B.; Mahmud, T. Antibacterial Potential of Secondary Metabolites from Indonesian Marine Bacterial Symbionts. Int. J. Microbiol. 2020, 2020, 8898631.eng
dcterms.referencesEmiru, Y.K.; Siraj, E.A.; Teklehaimanot, T.T.; Amare, G.G. Antibacterial Potential of Aloe weloensis (Aloeacea) Leaf Latex against Gram-Positive and Gram-Negative Bacteria Strains. Int. J. Microbiol. 2019, 2019, 5328238.eng
dcterms.referencesPettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605–1612.eng
dcterms.referencesBurton, G.J.; Hecht, G.B.; Newton, A. Roles of the histidine protein kinase pleC in Caulobacter crescentus motility and chemotaxis. J. Bacteriol. 1997, 179, 5849–5853.eng
dcterms.referencesAldridge, P.; Paul, R.; Goymer, P.; Rainey, P.; Jenal, U. Role of the GGDEF regulator PleD in polar development of Caulobacter crescentus. Mol. Microbiol. 2003, 47, 1695–1708.eng
dcterms.referencesAldridge, P.; Jenal, U. Cell cycle-dependent degradation of a flagellar motor component requires a novel-type response regulator. Mol. Microbiol. 1999, 32, 379–391.eng
dcterms.referencesJenal, U. Cyclic di-guanosine-monophosphate comes of age: A novel secondary messenger involved in modulating cell surface structures in bacteria? Curr. Opin. Microbiol. 2004, 7, 185–191.eng
dcterms.referencesTamayo, R.; Pratt, J.T.; Camilli, A. Roles of cyclic diguanylate in the regulation of bacterial pathogenesis. Annu. Rev. Microbiol. 2007, 61, 131–148.eng
dcterms.referencesYosa Reyes, J.; Nagy, T.; Meuwly, M. Competitive reaction pathways in vibrationally induced photodissociation of H2SO4 . Phys. Chem. Chem. Phys. 2014, 16, 18533–18544.eng
dcterms.referencesWassmann, P.; Chan, C.; Paul, R.; Beck, A.; Heerklotz, H.; Jenal, U.; Schirmer, T. Structure of BeF3 −-Modified Response Regulator PleD: Implications for Diguanylate Cyclase Activation, Catalysis, and Feedback Inhibition. Structure 2007, 15, 915–927.eng
dcterms.referencesNeves, M.A.C.; Totrov, M.; Abagyan, R. Docking and scoring with ICM: The benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des. 2012, 26, 675–686.eng
dcterms.referencesKhatoon, U.T.; Nageswara Rao, G.V.S.; Mohan, K.M.; Ramanaviciene, A.; Ramanavicius, A. Antibacterial and antifungal activity of silver nanospheres synthesized by tri-sodium citrate assisted chemical approach. Vacuum 2017, 146, 259–265.eng
dcterms.referencesChoudhury, R.; Majumdar, M.; Biswas, P.; Khan, S.; Misra, T.K. Kinetic study of functionalization of citrate stabilized silver nanoparticles with catechol and its anti-biofilm activity. Nano-Struct. Nano-Objects 2019, 19, 100326.eng
dcterms.referencesDu, C.; Cao, S.; Shi, X.; Nie, X.; Zheng, J.; Deng, Y.; Ruan, L.; Peng, D.; Sun, M. Genetic and Biochemical Characterization of a Gene Operon for trans-Aconitic Acid, a Novel Nematicide from Bacillus thuringiensis. J. Biol. Chem. 2017, 292, 3517–3530.eng
dcterms.referencesKumari, R.; Kumar, R.; Lynn, A. g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations. J. Chem. Inf. Model. 2014, 54, 1951–1962.eng
dcterms.referencesBaker, N.A.; Sept, D.; Holst, M.J.; McCammon, J.A. The adaptive multilevel finite element solution of the Poisson-Boltzmann equation on massively parallel computers. IBM J. Res. Dev. 2001, 45, 427–438.eng
dcterms.referencesWeiser, J.; Shenkin, P.S.; Still, W.C. Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J. Comput. Chem. 1999, 20, 217–230.eng
dcterms.referencesKonecny, R.B.; McCammon, N.A.; Andrew, J. iAPBS: A programming interface to the adaptive Poisson-Boltzmann solver. Comput. Sci. Discov. 2012, 5.eng
dcterms.referencesSargsyan, K.; Grauffel, C.; Lim, C. How Molecular Size Impacts RMSD Applications in Molecular Dynamics Simulations. J. Chem. Theory Comput. 2017, 13, 1518–1524.eng
dcterms.referencesRoe, D.R.; Cheatham, T.E. PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. J. Chem. Theory Comput. 2013, 9, 3084–3095.eng
dcterms.referencesNittinger, E.; Inhester, T.; Bietz, S.; Meyder, A.; Schomburg, K.T.; Lange, G.; Klein, R.; Rarey, M. Large-Scale Analysis of Hydrogen Bond Interaction Patterns in Protein–Ligand Interfaces. J. Med. Chem. 2017, 60, 4245–4257.eng
dcterms.referencesLobanov, M.Y.; Bogatyreva, N.S.; Galzitskaya, O.V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol. 2008, 42, 623–628.eng
dcterms.referencesYuhara, K.; Yonehara, H.; Hattori, T.; Kobayashi, K.; Kirimura, K. Enzymatic characterization and gene identification of aconitate isomerase, an enzyme involved in assimilation of trans-aconitic acid, from Pseudomonas sp. WU-0701. FEBS J. 2015, 282, 4257–4267.eng
dcterms.referencesBortolo, T.d.S.C.; Marchiosi, R.; Viganó, J.; Siqueira-Soares, R.d.C.; Ferro, A.P.; Barreto, G.E.; Bido, G.d.S.; Abrahão, J.; dos Santos, W.D.; Ferrarese-Filho, O. Trans-aconitic acid inhibits the growth and photosynthesis of Glycine max. Plant Physiol. Biochem. 2018, 132, 490–496.eng
dcterms.referencesSchnitzler, M.; Petereit, F.; Nahrstedt, A. Trans-Aconitic acid, glucosylflavones and hydroxycinnamoyltartaric acids from the leaves of Echinodorus grandiflorus ssp. aureus, a Brazilian medicinal plant. Rev. Bras. Farmacogn. 2007, 17, 149–154.eng
dcterms.referencesKanitkar, A.; Aita, G.; Madsen, L. The recovery of polymerization grade aconitic acid from sugarcane molasses. J. Chem. Technol. Biotechnol. 2013, 88, 2188–2192.eng
dcterms.referencesDe Souza Neto, L.R.; Moreira-Filho, J.T.; Neves, B.J.; Maidana, R.L.B.R.; Guimarães, A.C.R.; Furnham, N.; Andrade, C.H.; Silva, F.P., Jr. In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery. Front. Chem. 2020, 8, 93.eng
dcterms.referencesKitaura, K.; Ikeo, E.; Asada, T.; Nakano, T.; Uebayasi, M. Fragment molecular orbital method: An approximate computational method for large molecules. Chem. Phys. Lett. 1999, 313, 701–706.eng
dcterms.referencesHevener, K.E.; Pesavento, R.; Ren, J.; Lee, H.; Ratia, K.; Johnson, M.E. Chapter Twelve—Hit-to-Lead: Hit Validation and Assessment. In Modern Approaches in Drug Discovery; Methods in Enzymology; Lesburg, C.A., Ed.; Academic Press: New York, NY, USA, 2018; Volume 610, pp. 265–309.eng
dcterms.referencesSterling, T.; Irwin, J.J. ZINC 15—Ligand Discovery for Everyone. J. Chem. Inf. Model. 2015, 55, 2324–2337eng
dcterms.referencesAbagyan, R.; Totrov, M.; Kuznetsov, D. ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 1994, 15, 488–506.eng
dcterms.referencesAbagyan, R.; Totrov, M. Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins. J. Mol. Biol. 1994, 235, 983–1002spa
dcterms.referencesTotrov, M.; Abagyan, R. Rapid boundary element solvation electrostatics calculations in folding simulations: Successful folding of a 23-residue peptide. Pept. Sci. 2001, 60, 124–133eng
dcterms.referencesAn, J.; Totrov, M.; Abagyan, R. Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes. Mol. Cell. Proteom. 2005, 4, 752–761.eng
dcterms.referencesFernandez-Recio, J.; Totrov, M.; Skorodumov, C.; Abagyan, R. Optimal docking area: A new method for predicting protein–protein interaction sites. PROTEINS Struct. Funct. Bioinform. 2005, 58, 134–143.eng
dcterms.referencesFernandez-Recio, J.; Totrov, M.; Abagyan, R. ICM-DISCO docking by global energy optimization with fully flexible side-chains. PROTEINS Struct. Funct. Bioinform. 2003, 52, 113–117.eng
dcterms.referencesMéndez, R.; Leplae, R.; Lensink, M.F.; Wodak, S.J. Assessment of CAPRI predictions in rounds 3—5 shows progress in docking procedures. PROTEINS Struct. Funct. Bioinform. 2005, 60, 150–169.eng
dcterms.referencesMéndez, R.; Leplae, R.; De Maria, L.; Wodak, S.J. Assessment of blind predictions of protein—protein interactions: Current status of docking methods. PROTEINS Struct. Funct. Bioinform. 2003, 52, 51–67eng
dcterms.referencesFrisch, M.; Trucks, G.; Schlegel, H.; Scuseria, G.; Robb, M.; Cheeseman, J.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G.; et al. Gaussian 09; Gaussian, Inc.: Wallingford, CT, USA, 2009.eng
dcterms.referencesCase, D.; Ben-Shalom, I.; Brozell, S.; Cerutti, D.; Cheatham, T., III; Cruzeiro, V.; Darden, T.; Duke, R.; Ghoreishi, D.; Gilson, M.; et al. AMBER 2018; University of California: San Francisco, CA, USA, 2018.eng
dcterms.referencesSu, P.C.; Tsai, C.C.; Mehboob, S.; Hevener, K.E.; Johnson, M.E. Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F. tularensis enoyl-ACP reductase (FabI). J. Comput. Chem. 2015, 36, 1859–1873.eng
dcterms.referencesMaier, J.A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K.E.; Simmerling, C. ff 14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff 99SB. J. Chem. Theory Comput. 2015, 18, 3696–3713.eng
dcterms.referencesWang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general Amber force field. J. Comput. Chem. 2004, 25, 1157–1174.eng
dcterms.referencesOnufriev, A.V.; Izadi, S. Water models for biomolecular simulations. WIREs Comput. Mol. Sci. 2018, 8, e1347.eng
dcterms.referencesJorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926–935.eng
dcterms.referencesMiller, B.R.; McGee, T.D.; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E. An efficient program for end-state free energy calculations. J. Chem. Theory Comput. 2012, 8, 3314–3321.eng
dcterms.referencesBen-Shalom, I.Y.; Pfeiffer-Marek, S.; Baringhaus, K.H.; Gohlke, H. Efficient Approximation of Ligand Rotational and Translational Entropy Changes upon Binding for Use in MM-PBSA Calculations. J. Chem. Inf. Model. 2017, 57, 170–189.eng
dcterms.referencesGenheden, S.; Ryde, U. Comparison of the Efficiency of the LIE and MM/GBSA Methods to Calculate Ligand-Binding Energies. J. Chem. Theory Comput. 2011, 7, 3768–3778.eng
dcterms.referencesHou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model. 2011, 51, 69–82.eng


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