Artículo

Defelipe, L.A.; Arcon, J.P.; Modenutti, C.P.; Marti, M.A.; Turjanski, A.G.; Barril, X. "Solvents to fragments to drugs: MD applications in drug design" (2018) Molecules. 23(12)
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Abstract:

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates. © 2018 by the authors.

Registro:

Documento: Artículo
Título:Solvents to fragments to drugs: MD applications in drug design
Autor:Defelipe, L.A.; Arcon, J.P.; Modenutti, C.P.; Marti, M.A.; Turjanski, A.G.; Barril, X.
Filiación:Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, 1428, Argentina
IQUIBICEN/UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, 1428, Argentina
Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain
Faculty of Pharmacy, Institute of Biomedicine (IBUB), University of Barcelona, Avgda. Diagonal 643, Barcelona, 08028, Spain
Palabras clave:Cosolvent molecular dynamics; Docking; Drug design; Fragment screening; Molecular dynamics; ligand; protein; solvent; chemistry; drug design; drug development; metabolism; molecular dynamics; Drug Design; Drug Discovery; Ligands; Molecular Dynamics Simulation; Proteins; Solvents
Año:2018
Volumen:23
Número:12
DOI: http://dx.doi.org/10.3390/molecules23123269
Título revista:Molecules
Título revista abreviado:Molecules
ISSN:14203049
CODEN:MOLEF
CAS:protein, 67254-75-5; Ligands; Proteins; Solvents
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_14203049_v23_n12_p_Defelipe

Referencias:

  • Jorgensen, W.L., The many roles of computation in drug discovery (2004) Science, 303, pp. 1813-1818
  • Sliwoski, G., Kothiwale, S., Meiler, J., Lowe, E.W., Jr., Computational methods in drug discovery (2014) Pharmacol. Rev., 66, pp. 334-395
  • Brown, D.G., Boström, J., Where do recent small molecule clinical development candidates come from? (2018) J. Med. Chem., 61, pp. 9442-9468
  • Bottaro, S., Lindorff-Larsen, K., Biophysical experiments and biomolecular simulations: A perfect match? (2018) Science, 361, pp. 355-360
  • Lazaridis, T., Inhomogeneous fluid approach to solvation thermodynamics. 1. Theory (1998) J. Phys. Chem. B, 102, pp. 3531-3541
  • Pan, A.C., Xu, H., Palpant, T., Shaw, D.E., Quantitative characterization of the binding and unbinding of millimolar drug fragments with molecular dynamics simulations (2017) J. Chem. Theory Comput., 13, pp. 3372-3377
  • Lee, E.H., Hsin, J., Sotomayor, M., Comellas, G., Schulten, K., Discovery through the computational microscope (2009) Structure, 17, pp. 1295-1306
  • Goodford, P.J., A computational procedure for determining energetically favorable binding sites on biologically important macromolecules (1985) J. Med. Chem., 28, pp. 849-857
  • Miranker, A., Karplus, M., Functionality maps of binding sites: A multiple copy simultaneous search method (1991) Proteins, 11, pp. 29-34
  • Brenke, R., Kozakov, D., Chuang, G.-Y., Beglov, D., Hall, D., Landon, M.R., Mattos, C., Vajda, S., Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques (2009) Bioinformatics, 25, pp. 621-627
  • Baum, B., Muley, L., Smolinski, M., Heine, A., Hangauer, D., Klebe, G., Non-additivity of functional group contributions in protein-ligand binding: A comprehensive study by crystallography and isothermal titration calorimetry (2010) J. Mol. Biol., 397, pp. 1042-1054
  • Biela, A., Betz, M., Heine, A., Klebe, G., Water makes the difference: Rearrangement of Water Solvation Layer Triggers Non-additivity of Functional Group Contributions in Protein–Ligand Binding (2012) ChemMedChem, 7, pp. 1423-1434
  • Eastman, P., Swails, J., Chodera, J.D., McGibbon, R.T., Zhao, Y., Beauchamp, K.A., Wang, L.-P., Stern, C.D., OpenMM 7: Rapid development of high performance algorithms for molecular dynamics (2017) PLoS Comput. Biol., 13
  • Kutzner, C., Páll, S., Fechner, M., Esztermann, A., de Groot, B.L., Grubmüller, H., Best bang for your buck: GPU nodes for GROMACS biomolecular simulations (2015) J. Comput. Chem., 36, pp. 1990-2008
  • Lee, T.-S., Cerutti, D.S., Mermelstein, D., Lin, C., LeGrand, S., Giese, T.J., Roitberg, A., York, D.M., GPU-accelerated molecular dynamics and free energy methods in AMBER18: Performance enhancements and new features (2018) J. Chem. Inf. Model.
  • Li, Z., Lazaridis, T., The effect of water displacement on binding thermodynamics: Concanavalin A (2005) J. Phys. Chem. B, 109, pp. 662-670
  • Englert, L., Biela, A., Zayed, M., Heine, A., Hangauer, D., Klebe, G., Displacement of disordered water molecules from hydrophobic pocket creates enthalpic signature: Binding of phosphonamidate to the S1 0 -pocket of thermolysin (2010) BBA—Gen. Subj., 1800, pp. 1192-1202
  • Michel, J., Tirado-Rives, J., Jorgensen, W.L., Energetics of displacing water molecules from protein binding sites: Consequences for ligand optimization (2009) J. Am. Chem. Soc., 131, pp. 15403-15411
  • García-Sosa, A.T., Mancera, R.L., Dean, P.M., Waterscore: A novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes (2003) J. Mol. Model., 9, pp. 172-182
  • Crawford, T.D., Tsui, V., Flynn, E.M., Wang, S., Taylor, A.M., Côté, A., Audia, J.E., Cummings, R., Diving into the water: Inducible binding conformations for BRD4, TAF1(2), BRD9, and CECR2 bromodomains (2016) J. Med. Chem., 59, pp. 5391-5402
  • Ladbury, J.E., Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design (1996) Chem. Biol., 3, pp. 973-980
  • Poornima, C.S., Dean, P.M., Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions (1995) J. Comput. Aided Mol. Des., 9, pp. 500-512
  • Levinson, N.M., Boxer, S.G., A conserved water-mediated hydrogen bond network defines bosutinib’s kinase selectivity (2014) Nat. Chem. Biol., 10, pp. 127-132
  • García-Sosa, A.T., Hydration properties of ligands and drugs in protein binding sites: Tightly-bound, bridging water molecules and their effects and consequences on molecular design strategies (2013) J. Chem. Inf. Model., 53, pp. 1388-1405
  • Sridhar, A., Ross, G.A., Biggin, P.C., WaterDock 2.0: Water placement prediction for Holo-structures with a pymol plugin (2017) PLoS ONE, 12
  • Bissantz, C., Kuhn, B., Stahl, M., A medicinal chemist’s guide to molecular interactions (2010) J. Med. Chem., 53, pp. 5061-5084
  • Wiesner, S., Kurian, E., Prendergast, F.G., Halle, B., Water molecules in the binding cavity of intestinal fatty acid binding protein: Dynamic characterization by water 17O and 2H magnetic relaxation dispersion (1999) J. Mol. Biol., 286, pp. 233-246
  • Gauto, D.F., Di Lella, S., Guardia, C.M.A., Estrin, D.A., Martí, M.A., Carbohydrate-binding proteins: Dissecting ligand structures through solvent environment occupancy (2009) J. Phys. Chem. B, 113, pp. 8717-8724
  • López, E.D., Arcon, J.P., Gauto, D.F., Petruk, A.A., Modenutti, C.P., Dumas, V.G., Marti, M.A., Turjanski, A.G., Watclust: A tool for improving the design of drugs based on protein-water interactions (2015) Bioinformatics, 31, pp. 3697-3699
  • Klibanov, A.M., Improving enzymes by using them in organic solvents (2001) Nature, 409, pp. 241-246
  • Halling, P.J., What can we learn by studying enzymes in non-aqueous media? (2004) Philos. Trans. R. Soc. Lond. B Biol. Sci., 359, pp. 1287-1296
  • Allen, K.N., Bellamacina, C.R., Ding, X., Jeffery, C.J., Mattos, C., Petsko, G.A., Ringe, D., An experimental approach to mapping the binding surfaces of crystalline proteins (1996) J. Phys. Chem., 100, pp. 2605-2611
  • Liepinsh, E., Otting, G., Organic solvents identify specific ligand binding sites on protein surfaces (1997) Nat. Biotechnol., 15, pp. 264-268
  • English, A.C., Done, S.H., Caves, L.S., Groom, C.R., Hubbard, R.E., Locating interaction sites on proteins: The crystal structure of thermolysin soaked in 2% to 100% isopropanol (1999) Proteins, 37, pp. 628-640
  • English, A.C., Groom, C.R., Hubbard, R.E., Experimental and computational mapping of the binding surface of a crystalline protein (2001) Protein Eng, 14, pp. 47-59
  • Clackson, T., Wells, J.A., A hot spot of binding energy in a hormone-receptor interface (1995) Science, 267, pp. 383-386
  • Caflisch, A., Computational combinatorial ligand design: Application to human alpha-thrombin (1996) J. Comput. Aided Mol. Des., 10, pp. 372-396
  • Seco, J., Luque, F.J., Barril, X., Binding site detection and druggability index from first principles (2009) J. Med. Chem., 52, pp. 2363-2371
  • Vukovic, S., Huggins, D.J., Quantitative metrics for drug-target ligandability (2018) Drug Discov. Today, 23, pp. 1258-1266
  • Barril, X., Druggability predictions: Methods, limitations, and applications (2013) Wiley Interdiscip. Rev. Comput. Mol. Sci., 3, pp. 327-338
  • Guvench, O., MacKerell, A.D., Jr., Computational fragment-based binding site identification by ligand competitive saturation (2009) PLoS Comput. Biol., 5
  • Bakan, A., Nevins, N., Lakdawala, A.S., Bahar, I., Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules (2012) J. Chem. Theory Comput., 8, pp. 2435-2447
  • Ghanakota, P., Carlson, H.A., Moving beyond active-site detection: MixMD applied to allosteric systems (2016) J. Phys. Chem. B, 120, pp. 8685-8695
  • Sayyed-Ahmad, A., Gorfe, A.A., Mixed-probe simulation and probe-derived surface topography map analysis for ligand binding site identification (2017) J. Chem. Theory Comput., 13, pp. 1851-1861
  • Yu, W., Lakkaraju, S.K., Raman, E.P., Fang, L., MacKerell, A.D., Jr., Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules (2015) J. Chem. Inf. Model., 55, pp. 407-420
  • Ghanakota, P., van Vlijmen, H., Sherman, W., Beuming, T., Large-scale validation of mixed-solvent simulations to assess hotspots at protein-protein interaction interfaces (2018) J. Chem. Inf. Model., 58, pp. 784-793
  • Alvarez-Garcia, D., Barril, X., Molecular simulations with solvent competition quantify water displaceability and provide accurate interaction maps of protein binding sites (2014) J. Med. Chem., 57, pp. 8530-8539
  • Graham, S.E., Smith, R.D., Carlson, H.A., Predicting displaceable water sites using mixed-solvent molecular dynamics (2018) J. Chem. Inf. Model., 58, pp. 305-314
  • Uehara, S., Tanaka, S., Cosolvent-based molecular dynamics for ensemble docking: Practical method for generating druggable protein conformations (2017) J. Chem. Inf. Model., 57, pp. 742-756
  • Oleinikovas, V., Saladino, G., Cossins, B.P., Gervasio, F.L., Understanding cryptic pocket formation in protein targets by enhanced sampling simulations (2016) J. Am. Chem. Soc., 138, pp. 14257-14263
  • Comitani, F., Gervasio, F.L., Exploring cryptic pockets formation in targets of pharmaceutical interest with swiSH (2018) J. Chem. Theory Comput., 14, pp. 3321-3331
  • Kimura, S.R., Hu, H.P., Ruvinsky, A.M., Sherman, W., Favia, A.D., Deciphering cryptic binding sites on proteins by mixed-solvent molecular dynamics (2017) J. Chem. Inf. Model., 57, pp. 1388-1401
  • Raman, E.P., Yu, W., Guvench, O., Mackerell, A.D., Reproducing crystal binding modes of ligand functional groups using Site-Identification by Ligand Competitive Saturation (SILCS) simulations (2011) J. Chem. Inf. Model., 51, pp. 877-896
  • Arcon, J.P., Defelipe, L.A., Modenutti, C.P., López, E.D., Alvarez-Garcia, D., Barril, X., Turjanski, A.G., Martí, M.A., Molecular dynamics in mixed solvents reveals protein-ligand interactions, improves docking, and allows accurate binding free energy predictions (2017) J. Chem. Inf. Model., 57, pp. 846-863
  • Ghanakota, P., Carlson, H.A., Driving structure-based drug discovery through cosolvent molecular dynamics (2016) J. Med. Chem., 59, pp. 10383-10399
  • Pan, A.C., Borhani, D.W., Dror, R.O., Shaw, D.E., Molecular determinants of drug-receptor binding kinetics (2013) Drug Discov. Today, 18, pp. 667-673
  • Kuntz, I.D., Chen, K., Sharp, K.A., Kollman, P.A., The maximal affinity of ligands (1999) Proc. Natl. Acad. Sci. USA, 96, pp. 9997-10002
  • Huang, D., Caflisch, A., The free energy landscape of small molecule unbinding (2011) PLoS Comput. Biol., 7
  • Lexa, K.W., Goh, G.B., Carlson, H.A., Parameter choice matters: Validating probe parameters for use in mixed-solvent simulations (2014) J. Chem. Inf. Model., 54, pp. 2190-2199
  • Foster, T.J., MacKerell, A.D., Jr., Guvench, O., Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery (2012) J. Comput. Chem., 33, pp. 1880-1891
  • Alvarez-Garcia, D., Barril, X., Relationship between protein flexibility and binding: Lessons for structure-based drug design (2014) J. Chem. Theory Comput., 10, pp. 2608-2614
  • Erlanson, D.A., Fesik, S.W., Hubbard, R.E., Jahnke, W., Jhoti, H., Twenty years on: The impact of fragments on drug discovery (2016) Nat. Rev. Drug Discov., 15, pp. 605-619
  • Chen, Y., Shoichet, B.K., Molecular docking and ligand specificity in fragment-based inhibitor discovery (2009) Nat. Chem. Biol., 5, pp. 358-364
  • Giannetti, A.M., Shoichet, B.K., Docking for fragment inhibitors of AmpC β-lactamase (2009) Proc. Natl. Acad. Sci. USA, 106, pp. 7455-7460
  • Zhao, H., Gartenmann, L., Dong, J., Spiliotopoulos, D., Caflisch, A., Discovery of BRD4 bromodomain inhibitors by fragment-based high-throughput docking (2014) Bioorg. Med. Chem. Lett., 24, pp. 2493-2496
  • Spiliotopoulos, D., Zhu, J., Wamhoff, E.-C., Deerain, N., Marchand, J.-R., Aretz, J., Rademacher, C., Caflisch, A., Virtual screen to NMR (VS2NMR): Discovery of fragment hits for the CBP bromodomain (2017) Bioorg. Med. Chem. Lett., 27, pp. 2472-2478
  • Vass, M., Agai-Csongor, E., Horti, F., Keseru, G.M., Multiple fragment docking and linking in primary and secondary pockets of dopamine receptors (2014) ACS Med. Chem. Lett., 5, pp. 1010-1014
  • Marchand, J.-R., Dalle Vedove, A., Lolli, G., Caflisch, A., Discovery of inhibitors of four bromodomains by fragment-anchored ligand docking (2017) J. Chem. Inf. Model., 57, pp. 2584-2597
  • Jubb, H., Blundell, T.L., Ascher, D.B., Flexibility and small pockets at protein-protein interfaces: New insights into druggability (2015) Prog. Biophys. Mol. Biol., 119, pp. 2-9
  • Chipot, C., Pohorille, A., (2007) Free Energy Calculations: Theory and Applications in Chemistry and Biology, , Springer Science & Business Media: Berlin, Germany, ISBN
  • Chen, D., Ranganathan, A., IJzerman, A.P., Siegal, G., Carlsson, J., Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor (2013) J. Chem. Inf. Model., 53, pp. 2701-2714
  • Steinbrecher, T.B., Dahlgren, M., Cappel, D., Lin, T., Wang, L., Krilov, G., Abel, R., Sherman, W., Accurate binding free energy predictions in fragment optimization (2015) J. Chem. Inf. Model., 55, pp. 2411-2420
  • Jiang, W., Roux, B., Free energy perturbation Hamiltonian replica-exchange molecular dynamics (FEP/H-REMD) for absolute ligand binding free energy calculations (2010) J. Chem. Theory Comput., 6, pp. 2559-2565
  • Aldeghi, M., Heifetz, A., Bodkin, M.J., Knapp, S., Biggin, P.C., Accurate calculation of the absolute free energy of binding for drug molecules (2016) Chem. Sci., 7, pp. 207-218
  • Lin, Y.-L., Meng, Y., Jiang, W., Roux, B., Explaining why Gleevec is a specific and potent inhibitor of Abl kinase (2013) Proc. Natl. Acad. Sci. USA, 110, pp. 1664-1669
  • Dror, R.O., Pan, A.C., Arlow, D.H., Borhani, D.W., Maragakis, P., Shan, Y., Xu, H., Shaw, D.E., Pathway and mechanism of drug binding to G-protein-coupled receptors (2011) Proc. Natl. Acad. Sci. USA, 108, pp. 13118-13123
  • Mondal, J., Friesner, R.A., Berne, B.J., Role of desolvation in thermodynamics and kinetics of ligand binding to a kinase (2014) J. Chem. Theory Comput., 10, pp. 5696-5705
  • Tiwary, P., Mondal, J., Berne, B.J., How and when does an anticancer drug leave its binding site? (2017) Sci. Adv., 3, p. e1700014
  • Lotz, S.D., Dickson, A., Unbiased molecular dynamics of 11 min timescale drug unbinding reveals transition state stabilizing interactions (2018) J. Am. Chem. Soc., 140, pp. 618-628
  • Plattner, N., Noé, F., Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models (2015) Nat. Commun., 6, p. 7653
  • Bisignano, P., Doerr, S., Harvey, M.J., Favia, A.D., Cavalli, A., De Fabritiis, G., Kinetic characterization of fragment binding in AmpC β-lactamase by high-throughput molecular simulations (2014) J. Chem. Inf. Model., 54, pp. 362-366
  • Ferruz, N., Harvey, M.J., Mestres, J., De Fabritiis, G., Insights from fragment hit binding assays by molecular simulations (2015) J. Chem. Inf. Model., 55, pp. 2200-2205
  • Buch, I., Giorgino, T., De Fabritiis, G., Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations (2011) Proc. Natl. Acad. Sci. USA, 108, pp. 10184-10189
  • Rathi, P.C., Ludlow, R.F., Hall, R.J., Murray, C.W., Mortenson, P.N., Verdonk, M.L., Predicting “hot” and “warm” spots for fragment binding (2017) J. Med. Chem., 60, pp. 4036-4046
  • Martinez-Rosell, G., Harvey, M.J., De Fabritiis, G., Molecular-simulation-driven fragment screening for the discovery of new CXCL12 inhibitors (2018) J. Chem. Inf. Model., 58, pp. 683-691
  • Ludlow, R.F., Verdonk, M.L., Saini, H.K., Tickle, I.J., Jhoti, H., Detection of secondary binding sites in proteins using fragment screening (2015) Proc. Natl. Acad. Sci. USA, 112, pp. 15910-15915
  • Shan, Y., Kim, E.T., Eastwood, M.P., Dror, R.O., Seeliger, M.A., Shaw, D.E., How does a drug molecule find its target binding site? (2011) J. Am. Chem. Soc., 133, pp. 9181-9183
  • Colizzi, F., Perozzo, R., Scapozza, L., Recanatini, M., Cavalli, A., Single-molecule pulling simulations can discern active from inactive enzyme inhibitors (2010) J. Am. Chem. Soc., 132, pp. 7361-7371
  • Gioia, D., Bertazzo, M., Recanatini, M., Masetti, M., Cavalli, A., Dynamic docking: A paradigm shift in computational drug discovery (2017) Molecules, 22, p. 2029
  • Casasnovas, R., Limongelli, V., Tiwary, P., Carloni, P., Parrinello, M., Unbinding kinetics of a p38 MAP kinase type II inhibitor from metadynamics simulations (2017) J. Am. Chem. Soc., 139, pp. 4780-4788
  • Ruiz-Carmona, S., Schmidtke, P., Luque, F.J., Baker, L., Matassova, N., Davis, B., Roughley, S., Barril, X., Dynamic undocking and the quasi-bound state as tools for drug discovery (2017) Nat. Chem., 9, pp. 201-206
  • Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J., Autodock4 and AutoDockTools4: Automated docking with selective receptor flexibility (2009) J. Comput. Chem., 30, pp. 2785-2791
  • Forli, S., Huey, R., Pique, M.E., Sanner, M.F., Goodsell, D.S., Olson, A.J., Computational protein-ligand docking and virtual drug screening with the AutoDock suite (2016) Nat. Protoc., 11, pp. 905-919
  • Ruiz-Carmona, S., Alvarez-Garcia, D., Foloppe, N., Garmendia-Doval, A.B., Juhos, S., Schmidtke, P., Barril, X., Morley, S.D., RDock: A fast, versatile and open source program for docking ligands to proteins and nucleic acids (2014) PLoS Comput. Biol., 10
  • Sousa, S.F., Ribeiro, A.J.M., Coimbra, J.T.S., Neves, R.P.P., Martins, S.A., Moorthy, N.S., Fernandes, P.A., Ramos, M.J., Protein-ligand docking in the new millennium—a retrospective of 10 years in the field (2013) Curr. Med. Chem., 20, pp. 2296-2314
  • Cleves, A.E., Jain, A.N., Knowledge-guided docking: Accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock (2015) J. Comput. Aided Mol. Des., 29, pp. 485-509
  • Hu, B., Lill, M.A., PharmDock: A pharmacophore-based docking program (2014) J. Cheminform., 6, p. 14
  • Perryman, A.L., Santiago, D.N., Forli, S., Martins, D.S., Olson, A.J., Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: Participation in the SAMPL4 protein-ligand binding challenge (2014) J. Comput. Aided Mol. Des., 28, pp. 429-441
  • Friesner, R.A., Banks, J.L., Murphy, R.B., Halgren, T.A., Klicic, J.J., Mainz, D.T., Repasky, M.P., Perry, J.K., Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy (2004) J. Med. Chem., 47, pp. 1739-1749
  • Jones, G., Willett, P., Glen, R.C., Leach, A.R., Taylor, R., Development and validation of a genetic algorithm for flexible docking (1997) J. Mol. Biol., 267, pp. 727-748
  • Corbeil, C.R., Williams, C.I., Labute, P., Variability in docking success rates due to dataset preparation (2012) J. Comput. Aided Mol. Des., 26, pp. 775-786
  • Allen, W.J., Balius, T.E., Mukherjee, S., Brozell, S.R., Moustakas, D.T., Lang, P.T., Case, D.A., Rizzo, R.C., Dock 6: Impact of new features and current docking performance (2015) J. Comput. Chem., 36, pp. 1132-1156
  • Coleman, R.G., Carchia, M., Sterling, T., Irwin, J.J., Shoichet, B.K., Ligand pose and orientational sampling in molecular docking (2013) PLoS ONE, 8
  • Balius, T.E., Fischer, M., Stein, R.M., Adler, T.B., Nguyen, C.N., Cruz, A., Gilson, M.K., Shoichet, B.K., Testing inhomogeneous solvation theory in structure-based ligand discovery (2017) Proc. Natl. Acad. Sci. USA, 114, pp. E6839-E6846
  • Uehara, S., Tanaka, S., AutoDock-GisT: Incorporating thermodynamics of active-site water into scoring function for accurate protein-ligand docking (2016) Molecules, 21, p. 1604
  • Gohlke, H., Hendlich, M., Klebe, G., Knowledge-based scoring function to predict protein-ligand interactions (2000) J. Mol. Biol., 295, pp. 337-356
  • Muegge, I., Martin, Y.C., A general and fast scoring function for protein-ligand interactions: A simplified potential approach (1999) J. Med. Chem., 42, pp. 791-804
  • Zheng, Z., Merz, K.M., Development of the knowledge-based and empirical combined scoring algorithm (KECSA) to score protein–ligand interactions (2013) J. Chem. Inf. Model., 53, pp. 1073-1083
  • Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Greenwood, J., Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field (2015) J. Am. Chem. Soc., 137, pp. 2695-2703
  • García-Sosa, A.T., Mancera, R.L., Free energy calculations of mutations involving a tightly bound water molecule and ligand substitutions in a ligand-protein complex (2010) Mol. Inform., 29, pp. 589-600
  • Aldeghi, M., Ross, G.A., Bodkin, M.J., Essex, J.W., Knapp, S., Biggin, P.C., Large-scale analysis of water stability in bromodomain binding pockets with grand canonical Monte Carlo (2018) Commun. Chem., 1, p. 19

Citas:

---------- APA ----------
Defelipe, L.A., Arcon, J.P., Modenutti, C.P., Marti, M.A., Turjanski, A.G. & Barril, X. (2018) . Solvents to fragments to drugs: MD applications in drug design. Molecules, 23(12).
http://dx.doi.org/10.3390/molecules23123269
---------- CHICAGO ----------
Defelipe, L.A., Arcon, J.P., Modenutti, C.P., Marti, M.A., Turjanski, A.G., Barril, X. "Solvents to fragments to drugs: MD applications in drug design" . Molecules 23, no. 12 (2018).
http://dx.doi.org/10.3390/molecules23123269
---------- MLA ----------
Defelipe, L.A., Arcon, J.P., Modenutti, C.P., Marti, M.A., Turjanski, A.G., Barril, X. "Solvents to fragments to drugs: MD applications in drug design" . Molecules, vol. 23, no. 12, 2018.
http://dx.doi.org/10.3390/molecules23123269
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Defelipe, L.A., Arcon, J.P., Modenutti, C.P., Marti, M.A., Turjanski, A.G., Barril, X. Solvents to fragments to drugs: MD applications in drug design. Molecules. 2018;23(12).
http://dx.doi.org/10.3390/molecules23123269