Searching hit potential antimicrobials in natural compounds space against biofilm formation
Biofilms 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, respective...
- Autores:
-
Pestana-Nobles, Roberto
Leyva-Rojas, Jorge A.
Yosa, Juvenal
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/6838
- Palabra clave:
- Biofilms
Virtual screening
Molecular dynamics
Natural products
Binding energy
Trans-aconitic acid
hit-to-lead
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| title |
Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| spellingShingle |
Searching hit potential antimicrobials in natural compounds space against biofilm formation Biofilms Virtual screening Molecular dynamics Natural products Binding energy Trans-aconitic acid hit-to-lead |
| title_short |
Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| title_full |
Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| title_fullStr |
Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| title_full_unstemmed |
Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| title_sort |
Searching hit potential antimicrobials in natural compounds space against biofilm formation |
| dc.creator.fl_str_mv |
Pestana-Nobles, Roberto Leyva-Rojas, Jorge A. Yosa, Juvenal |
| dc.contributor.author.none.fl_str_mv |
Pestana-Nobles, Roberto Leyva-Rojas, Jorge A. Yosa, Juvenal |
| dc.subject.eng.fl_str_mv |
Biofilms Virtual screening Molecular dynamics Natural products Binding energy Trans-aconitic acid hit-to-lead |
| topic |
Biofilms Virtual screening Molecular dynamics Natural products Binding energy Trans-aconitic acid hit-to-lead |
| description |
Biofilms 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. |
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2020 |
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2020-12-03T16:36:19Z |
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2020-12-03T16:36:19Z |
| dc.date.issued.none.fl_str_mv |
2020 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo científico |
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14203049 |
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https://hdl.handle.net/20.500.12442/6838 |
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doi:10.3390/molecules25225334 https://www.mdpi.com/1420-3049/25/22/5334 |
| identifier_str_mv |
14203049 doi:10.3390/molecules25225334 |
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https://hdl.handle.net/20.500.12442/6838 https://www.mdpi.com/1420-3049/25/22/5334 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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pdf |
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MDPI |
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Revista: Molecules |
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Vol. 25, No. 5334, (2020) |
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Universidad Simón Bolívar |
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Pestana-Nobles, Roberto476a6194-e883-45a0-bfd9-926829c41cd5Leyva-Rojas, Jorge A.6c62ccf2-5642-4dc8-b32a-1a042526f75eYosa, Juvenala88e205c-2dff-4cc9-9530-cb1d52adb9dd2020-12-03T16:36:19Z2020-12-03T16:36:19Z202014203049https://hdl.handle.net/20.500.12442/6838doi:10.3390/molecules25225334https://www.mdpi.com/1420-3049/25/22/5334Biofilms 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.pdfengMDPIAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Revista: MoleculesVol. 25, No. 5334, (2020)BiofilmsVirtual screeningMolecular dynamicsNatural productsBinding energyTrans-aconitic acidhit-to-leadSearching hit potential antimicrobials in natural compounds space against biofilm formationinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Flemming, 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.Yin, W.; Wang, Y.; Liu, L.; He, J. Biofilms: The Microbial “Protective Clothing” in Extreme Environments. Int. J. Mol. Sci. 2019, 20, 3423.Tolker-Nielsen, T. Biofilm Development. Microbiol. Spectr. 2015, 3.Del Pozo, J.L. Biofilm-related disease. Expert Rev. Anti-Infect. Ther. 2018, 16, 51–65.Fleming, D.; Rumbaugh, K.P. Approaches to Dispersing Medical Biofilms. Microorganisms 2017, 5, 15.Jamal, 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.Fernicola, 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.Cai, 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.Seshasayee, 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.Galperin, M.Y. A census of membrane-bound and intracellular signal transduction proteins in bacteria: Bacterial IQ, extroverts and introverts. BMC Microbiol. 2005, 5, 35.Römling, 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.Feirer, 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.Alviz-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.Jenal, U.; Malone, J. Mechanisms of cyclic-di-GMP signaling in bacteria. Annu. Rev. Genet. 2006, 40, 385–407.Paul, 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.Skerker, J.M.; Laub, M.T. Cell-cycle progression and the generation of asymmetry in Caulobacter crescentus. Nat. Rev. Microbiol. 2004, 2, 325–337.Entcheva-Dimitrov, P.; Spormann, A.M. Dynamics and Control of Biofilms of the Oligotrophic Bacterium Caulobacter crescentus. J. Bacteriol. 2004, 186, 8254–8266.Valentini, 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.Lage, 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.Rossiter, S.E.; Fletcher, M.H.; Wuest, W.M. Natural Products as Platforms to Overcome Antibiotic Resistance. Chem. Rev. 2017, 117, 12415–12474.Herrmann, J.; Fayad, A.A.; Müller, R. Natural products from myxobacteria: Novel metabolites and bioactivities. Nat. Prod. Rep. 2017, 34, 135–160.Rodrigues, T.; Reker, D.; Schneider, P.; Schneider, G. Counting on natural products for drug design. Nat. Chem. 2016, 8, 531–541.Nofiani, 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.Emiru, 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.Pettersen, 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.Burton, 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.Aldridge, 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.Aldridge, P.; Jenal, U. Cell cycle-dependent degradation of a flagellar motor component requires a novel-type response regulator. Mol. Microbiol. 1999, 32, 379–391.Jenal, 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.Tamayo, R.; Pratt, J.T.; Camilli, A. Roles of cyclic diguanylate in the regulation of bacterial pathogenesis. Annu. Rev. Microbiol. 2007, 61, 131–148.Yosa Reyes, J.; Nagy, T.; Meuwly, M. Competitive reaction pathways in vibrationally induced photodissociation of H2SO4 . Phys. Chem. Chem. Phys. 2014, 16, 18533–18544.Wassmann, 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.Neves, 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.Khatoon, 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.Choudhury, 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.Du, 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.Kumari, R.; Kumar, R.; Lynn, A. g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations. J. Chem. Inf. Model. 2014, 54, 1951–1962.Baker, 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.Weiser, J.; Shenkin, P.S.; Still, W.C. Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J. Comput. Chem. 1999, 20, 217–230.Konecny, R.B.; McCammon, N.A.; Andrew, J. iAPBS: A programming interface to the adaptive Poisson-Boltzmann solver. Comput. Sci. Discov. 2012, 5.Sargsyan, K.; Grauffel, C.; Lim, C. How Molecular Size Impacts RMSD Applications in Molecular Dynamics Simulations. J. Chem. Theory Comput. 2017, 13, 1518–1524.Roe, 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.Nittinger, 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.Lobanov, M.Y.; Bogatyreva, N.S.; Galzitskaya, O.V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol. 2008, 42, 623–628.Yuhara, 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.Bortolo, 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.Schnitzler, 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.Kanitkar, A.; Aita, G.; Madsen, L. The recovery of polymerization grade aconitic acid from sugarcane molasses. J. Chem. Technol. Biotechnol. 2013, 88, 2188–2192.De 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.Kitaura, 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.Hevener, 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.Sterling, T.; Irwin, J.J. ZINC 15—Ligand Discovery for Everyone. J. Chem. Inf. Model. 2015, 55, 2324–2337Abagyan, 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.Totrov, M.; Abagyan, R. Rapid boundary element solvation electrostatics calculations in folding simulations: Successful folding of a 23-residue peptide. Pept. Sci. 2001, 60, 124–133An, J.; Totrov, M.; Abagyan, R. Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes. Mol. Cell. Proteom. 2005, 4, 752–761.Fernandez-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.Fernandez-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.Méndez, 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.Méndez, 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, 51–67Frisch, 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.Case, 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.Su, 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.Maier, 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.Wang, 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.Onufriev, A.V.; Izadi, S. Water models for biomolecular simulations. WIREs Comput. Mol. Sci. 2018, 8, e1347.Jorgensen, 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.Miller, B.R.; McGee, T.D.; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E. MMPBSA.py: An efficient program for end-state free energy calculations. J. Chem. Theory Comput. 2012, 8, 3314–3321.Ben-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.Genheden, 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.Hou, 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.Abagyan, R.; Totrov, M. Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins. J. Mol. 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