Small Molecule Docking from Theoretical Structural Models
Abstract
Structural approaches to rational drug design rely on the basic assumption that pharmacological activity requires, as necessary but not sufficient condition, the binding of a drug to one or several cellular targets, proteins in most cases. The traditional paradigm assumes that drugs that interact only with a single cellular target are specific and accordingly have little secondary effects, while promiscuous molecules are more likely to generate undesirable side effects. However, current examples indicate that often efficient drugs are able to interact with several biological targets [1] and in fact some dirty drugs, such as chlorpromazine, dextromethorphan, and ibogaine exhibit desired pharmacological properties [2]. These considerations highlight the tremendous difficulty of designing small molecules that both have satisfactory ADME properties and the ability of interacting with a limited set of target proteins with a high affinity, avoiding at the same time undesirable interactions with other proteins. In this complex and challenging scenario, computer simulations emerge as the basic tool to guide medicinal chemists during the drug discovery process.
Keywords
Binding Mode Root Mean Square Deviation Homology Model Virtual Screening Docking ProgramReferences
- 1.Campbell, S.J., Gold, N.D., Jackson, R.M., Westhead, D.R.: Ligand binding: functional site location, similarity and docking. Curr. Opin. Struct. Biol. 13(3), 389–395 (2003)CrossRefGoogle Scholar
- 2.Keiser, M.J., et al.: Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25(2), 197–206 (2007)CrossRefGoogle Scholar
- 3.Fisher, E.: Einfluss der Konfiguration auf die Wirkung der Enzyme. Berichte der Deutschen Chemischen Gesellschaft. 27, 2985–2993 (1894)CrossRefGoogle Scholar
- 4.Kuntz, I.D., Blaney, J.M., Oatley, S.J., Langridge, R., Ferrin, T.E.: A geometric approach to macromolecule–ligand interactions. J. Mol. Biol. 161(2), 269–288 (1982)CrossRefGoogle Scholar
- 5.Butler, K.T., Luque, F.J., Barril, X.: Toward accurate relative energy predictions of the bioactive conformation of drugs. J. Comput. Chem. 30(4), 601–610 (2009)CrossRefGoogle Scholar
- 6.Perola, E., Charifson, P.S.: Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J. Med. Chem. 47(10), 2499–2510 (2004)CrossRefGoogle Scholar
- 7.Berman, H.M., et al.: The protein data bank. Nucleic. Acids. Res. 28(1), 235–242 (2000)ADSCrossRefGoogle Scholar
- 8.Cozzini, P., et al.: Target flexibility: an emerging consideration in drug discovery and design. J. Med. Chem. 51(20), 6237–6255 (2008)CrossRefGoogle Scholar
- 9.Merz, K.M.: Limits of free energy computation for protein–ligand interactions. J Chem. Theory. Comput. 6(4), 1018–1027 (2010)CrossRefGoogle Scholar
- 10.Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J.: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug. Discov. 3(11), 935–949 (2004)CrossRefGoogle Scholar
- 11.Pruitt, K.D., Tatusova, T., Maglott, D.R.: NCBI reference sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic. Acids. Res. 33(Database issue), D501–504 (2005)Google Scholar
- 12.Novoa, E.M., de Pouplana, L.R., Barril, X., Orozco, M.: Ensemble docking from homology models. J. Chem. Theory. Comput. 6(8), 2547–2557 (2010)CrossRefGoogle Scholar
- 13.Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins. 47(4), 409–443 (2002)CrossRefGoogle Scholar
- 14.Leach, A.R., Shoichet, B.K., Peishoff, C.E.: Prediction of protein–ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem. 49(20), 5851–5855 (2006)CrossRefGoogle Scholar
- 15.Shoichet, B.K., McGovern, S.L., Wei, B., Irwin, J.J.: Lead discovery using molecular docking. Curr. Opin. Chem. Biol. 6(4), 439–446 (2002)CrossRefGoogle Scholar
- 16.Sousa, S.F., Fernandes, P.A., Ramos, M.J.: Protein-ligand docking: current status and future challenges. Proteins. 65(1), 15–26 (2006)CrossRefGoogle Scholar
- 17.Shoichet, B.K., Bodian, D.L., Kuntz, I.D.: Molecular docking using shape descriptors. J. Comput. Chem. 13, 380–397 (1992)CrossRefGoogle Scholar
- 18.Gardiner, E.J., Willett, P., Artymiuk, P.J.: Graph-theoretic techniques for macromolecular docking. J. Chem. Inf. Comput. Sci. 40(2), 273–279 (2000)CrossRefGoogle Scholar
- 19.Ponder, J.W., Case, D.A.: Force fields for protein simulations. Adv. Protein. Chem. 66, 27–85 (2003)CrossRefGoogle Scholar
- 20.Morris, G.M. et al.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function J. Comput. Chem. 19, 1639–1662 (1998)CrossRefGoogle Scholar
- 21.Jones, G., Willett, P., Glen, R.C.: Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 245(1), 43–53 (1995)CrossRefGoogle Scholar
- 22.Verdonk, M.L., Cole, J.C., Hartshorn, M.J., Murray, C.W., Taylor, R.D.: Improved protein-ligand docking using GOLD. Proteins. 52(4), 609–623 (2003)Google Scholar
- 23.Rarey, M., Kramer, B., Lengauer, T., Klebe, G.: A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261(3), 470–489 (1996)CrossRefGoogle Scholar
- 24.Jorgensen, W.L., Tirado-Rives, J.: The OPLS force field for proteins. Energy minimizations for crystals of cyclic peptides and Crambin. J. Am. Chem. Soc. 110, 1657–1666 (1988)Google Scholar
- 25.Abagyan, 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. 15, 488–506 (1994)CrossRefGoogle Scholar
- 26.Friesner, R.A., et al.: Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47(7), 1739–1749 (2004)Google Scholar
- 27.Ding, F., Yin, S., Dokholyan, N.V.: Rapid flexible docking using a stochastic rotamer library of ligands. J. Chem. Inf. Model. 50(9), 1623–1632 (2010)CrossRefGoogle Scholar
- 28.Gohlke, H., Klebe, G.: Statistical potentials and scoring functions applied to protein-ligand binding. Curr. Opin. Struct. Biol. 11(2), 231–235 (2001)CrossRefGoogle Scholar
- 29.Dunbrack, R.L., Jr. Karplus, M.: Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230(2), 543–574 (1993)Google Scholar
- 30.Holm, L., Sander, C.: Fast and simple Monte Carlo algorithm for side chain optimization in proteins: application to model building by homology. Proteins. 14(2), 213–223 (1992)CrossRefGoogle Scholar
- 31.Brunger, A.T., Kuriyan, J., Karplus, M.: Crystallographic R factor refinement by molecular dynamics. Science. 235(4787), 458–460 (1987)ADSCrossRefGoogle Scholar
- 32.Armen, R.S., Chen, J., Brooks, C.L.: An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J. Chem. Theory. Comput. 5(10), 2909–2923 (2009)CrossRefGoogle Scholar
- 33.Paulsen, J.L., Anderson, A.C.: Scoring ensembles of docked protein:ligand interactions for virtual lead optimization. J. Chem. Inf. Model. 49(12), 2813–2819 (2009)CrossRefGoogle Scholar
- 34.Craig, I.R., Essex, J.W., Spiegel, K.: Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J. Chem. Inf. Model. 50(4), 511–524 (2010)CrossRefGoogle Scholar
- 35.Huang, S.Y., Zou, X.: Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins. 66(2), 399–421 (2007a)MathSciNetCrossRefGoogle Scholar
- 36.Rao, S., et al.: Improving database enrichment through ensemble docking. J. Comput. Aided. Mol. Des. 22(9), 621–627 (2008)ADSCrossRefGoogle Scholar
- 37.Rueda, M., Bottegoni, G., Abagyan, R.: Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 50(1), 186–193 (2010)CrossRefGoogle Scholar
- 38.Damm, K.L., Carlson, H.A.: Exploring experimental sources of multiple protein conformations in structure-based drug design. J. Am. Chem Soc. 129(26), 8225–8235 (2007)CrossRefGoogle Scholar
- 39.Huang, S.Y., Zou, X.: Efficient molecular docking of NMR structures: application to HIV-1 protease. Protein. Sci. 16(1), 43–51 (2007b)CrossRefGoogle Scholar
- 40.Hawkins, P.C., Warren, G.L., Skillman, A.G., Nicholls, A.: How to do an evaluation: pitfalls and traps. J. Comput. Aided. Mol. Des. 22(3–4), 179–190 (2008)ADSCrossRefGoogle Scholar
- 41.Warren, G.L., et al.: A critical assessment of docking programs and scoring functions. J. Med. Chem. 49(20), 5912–5931 (2006)CrossRefGoogle Scholar
- 42.Yang, L., et al.: Identifying unexpected therapeutic targets via chemical-protein interactome. PLoS ONE. 5(3), e9568 (2010)CrossRefGoogle Scholar
- 43.Petrelli, A., Giordano, S.: From single- to multi-target drugs in cancer theraphy: when aspecificity becomes an advantage. Curr. Med. Chem. 15, 422–432 (2008)CrossRefGoogle Scholar
- 44.Wermuth, C.G.: Multitarget drugs: the end of the ‘one-target-on-disease’ phylosophy? Drug. Discov. Today. 9, 826–827 (2004)CrossRefGoogle Scholar
- 45.Kirchmair, J., Markt, P., Distinto, S., Wolber, G., Langer, T.: Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—what can we learn from earlier mistakes? J. Comput. Aided. Mol. Des. 22(3–4), 213–228 (2008)ADSCrossRefGoogle Scholar
- 46.Langer T., Hoffmann RD.: Pharmacophores and Pharmacophore Searches. Wiley-VCH, Weinheim, Germany, pp. 338–343 (2006)Google Scholar
- 47.Truchon, J.F., Bayly, C.I.: Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model. 47(2), 488–508 (2007)CrossRefGoogle Scholar
- 48.Jain, A.N., Nicholls, A.: Recommendations for evaluation of computational methods. J. Comput. Aided. Mol. Des. 22(3–4), 133–139 (2008)ADSCrossRefGoogle Scholar
- 49.Nicholls, A.: What do we know and when do we know it? J. Comput. Aided. Mol. Des. 22(3–4), 239–255 (2008)ADSCrossRefGoogle Scholar
- 50.Witten, I.H., Frank, E.: Credibility: Evaluating what’s been learned. In: Data minings: Practical machine learning tools and techniques, 2nd ed; Morgan Kaufmann: San Francisco, CA, pp. 161–176 (2005)Google Scholar
- 51.Koehl, P., Levitt, M.: A brighter future for protein structure prediction. Nat. Struct. Biol. 6(2), 108–111 (1999)CrossRefGoogle Scholar
- 52.Marti-Renom, M.A., et al.: Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29, 291–325 (2000)CrossRefGoogle Scholar
- 53.Arnold, K., Bordoli, L., Kopp, J., Schwede, T.: The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics. 22(2), 195–201 (2006)CrossRefGoogle Scholar
- 54.Bates, P.A., Kelley, L.A., MacCallum, R.M., Sternberg, M.J.: Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM. Proteins. Suppl. 5, 39–46 (2001)CrossRefGoogle Scholar
- 55.Eswar, N., et al.: Tools for comparative protein structure modeling and analysis. Nucleic. Acids. Res. 31(13), 3375–3380 (2003)CrossRefGoogle Scholar
- 56.Sali, A., Blundell, T.L.: Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234(3), 779–815 (1993)CrossRefGoogle Scholar
- 57.Altschul, S.F., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic. Acids. Res. 25(17), 3389–3402 (1997)CrossRefGoogle Scholar
- 58.Wistrand, M., Sonnhammer, E.L.: Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER. BMC Bioinformatics. 6, 99 (2005)CrossRefGoogle Scholar
- 59.McGovern, S.L., Shoichet, B.K.: Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J. Med. Chem. 46(14), 2895–2907 (2003)CrossRefGoogle Scholar
- 60.Rockey, W.M., Elcock, A.H.: Structure selection for protein kinase docking and virtual screening: homology models or crystal structures? Curr. Protein. Pept. Sci. 7(5), 437–457 (2006)CrossRefGoogle Scholar
- 61.Tuccinardi, T., Botta, M., Giordano, A., Martinelli, A.: Protein kinases: docking and homology modeling reliability. J. Chem. Inf. Model. 50(8), 1432–1441 (2010)CrossRefGoogle Scholar
- 62.Blake, J.D., Cohen, F.E.: Pairwise sequence alignment below the twilight zone. J. Mol. Biol. 307(2), 721–735 (2001)CrossRefGoogle Scholar
- 63.Jennings, A.J., Edge, C.M., Sternberg, M.J.: An approach to improving multiple alignments of protein sequences using predicted secondary structure. Protein. Eng. 14(4), 227–231 (2001)CrossRefGoogle Scholar
- 64.Sanchez, R., Sali, A.: Advances in comparative protein-structure modelling. Curr. Opin. Struct. Biol. 7(2), 206–214 (1997)CrossRefGoogle Scholar
- 65.Shi, J., Blundell, T.L., Mizuguchi, K.: FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J. Mol. Biol. 310(1), 243–257 (2001)CrossRefGoogle Scholar
- 66.Marti-Renom, M.A., Madhusudhan, M.S., Sali, A.: Alignment of protein sequences by their profiles. Protein. Sci. 13(4), 1071–1087 (2003)CrossRefGoogle Scholar
- 67.von Ohsen, N., Sommer, I., Zimmer, R.: Profile-profile alignment: a powerful tool for protein structure prediction. Pac. Symp. Biocomput. 252–263 (2003)Google Scholar
- 68.Jaroszewski, L., Rychlewski, L., Godzik, A.: Improving the quality of twilight-zone alignments. Protein Sci. 9(8), 1487–1496 (2000)CrossRefGoogle Scholar
- 69.Sauder, J.M., Arthur, J.W., Dunbrack, R.L., Jr: Large-scale comparison of protein sequence alignment algorithms with structure alignments. Proteins. 40(1), 6–22 (2000)CrossRefGoogle Scholar
- 70.Marti-Renom, M.A., Madhusudhan, M.S., Fiser, A., Rost, B., Sali, A.: Reliability of assessment of protein structure prediction methods. Structure. 10(3), 435–440 (2002)CrossRefGoogle Scholar
- 71.Eswar, N., Sali, A.: (2007) Comparative modeling of drug target proteins. In: Taylor J., Triggle D., Mason J.S., (eds.) Computer-Assisted Drug Design, Comprehensive Medicinal Chemistry II, vol. 4, pp. 215–236. Elsevier, Oxford, UKGoogle Scholar
- 72.Sanchez, R., et al.: Protein structure modeling for structural genomics. Nat. Struct. Biol. 7 Suppl. 986–990 (2000)Google Scholar
- 73.Eramian, D., Eswar, N., Shen, M.Y., Sali, A.: How well can the accuracy of comparative protein structure models be predicted? Protein. Sci. 17(11), 1881–1893 (2008)CrossRefGoogle Scholar
- 74.Baker, D., Sali, A.: Protein structure prediction and structural genomics. Science. 294(5540), 93–96 (2001)ADSCrossRefGoogle Scholar
- 75.Cavasotto, C.N., Phatak, S.S.: Homology modeling in drug discovery: current trends and applications. Drug. Discov. Today. 14(13–14), 676–683 (2009)CrossRefGoogle Scholar
- 76.Diller, D.J., Li, R.: Kinases, homology models, and high throughput docking. J. Med. Chem. 46(22), 4638–4647 (2003)CrossRefGoogle Scholar
- 77.Oshiro, C., et al.: Performance of 3D-database molecular docking studies into homology models. J. Med. Chem. 47(3), 764–767 (2004)CrossRefGoogle Scholar
- 78.Kairys, V., Fernandes, M.X., Gilson, M.K.: Screening drug-like compounds by docking to homology models: a systematic study. J. Chem. Inf. Model. 46(1), 365–379 (2006)CrossRefGoogle Scholar
- 79.Ferrara, P., Jacoby, E.: Evaluation of the utility of homology models in high throughput docking. J. Mol. Model. 13(8), 897–905 (2007)CrossRefGoogle Scholar
- 80.Fan, H., et al.: Molecular docking screens using comparative models of proteins. J. Chem. Inf. Model. 49(11), 2512–2527 (2009)CrossRefGoogle Scholar
- 81.Barril, X., Morley, S.D.: Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J. Med. Chem. 48(13), 4432–4443 (2005)CrossRefGoogle Scholar
- 82.Birch, L., Murray, C.W., Hartshorn, M.J., Tickle, I.J., Verdonk, M.L.: Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase. J. Comput. Aided. Mol. Des. 16(12), 855–869 (2002)CrossRefGoogle Scholar
- 83.Hillisch, A., Pineda, L.F., Hilgenfeld, R.: Utility of homology models in the drug discovery process. Drug. Discov. Today. 9(15), 659–669 (2004)CrossRefGoogle Scholar
- 84.Wishart, D.S., et al.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic. Acids. Res. 36(Database issue), D901–906 (2008)Google Scholar
- 85.Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. EMBO J. 5(4), 823–826 (1986)Google Scholar
- 86.O’Donovan, C., Apweiler, R., Bairoch, A.: The human proteomics initiative (HPI). Trends. Biotechnol. 19(5), 178–181 (2001)CrossRefGoogle Scholar
- 87.Park, S.J., Kufareva, I., Abagyan, R.: Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles. J. Comput. Aided. Mol. Des. 24(5), 459–471 (2010)ADSCrossRefGoogle Scholar