De novo drug design: Integration of structure-based and ligand-based methods

Current Opinion in Drug Discovery and Development

Volumn 7, Issue 3, 2004, Pages 347-353

  Dean, Philip M ^a   Lloyd, David G ^b   Todorov, Nikolay P ^a  

^a De Novo Pharmaceuticals Ltd   (United Kingdom)

^b TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

De novo drug design; Integration; Ligand based design; Methods; Molecular design; Pharmacophore; Protein ligand interactions; Structure based design

Indexed keywords

LIGAND;

ACCURACY; DRUG DESIGN; KNOWLEDGE; LIGAND BINDING; PHARMACOPHORE; PROTEIN STRUCTURE; REVIEW; SCORING SYSTEM; SIMULATION; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS; CHEMICAL STRUCTURE; COMBINATORIAL CHEMISTRY; METHODOLOGY; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

COMBINATORIAL CHEMISTRY TECHNIQUES; DRUG DESIGN; LIGANDS; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 4043062916     PISSN: 13676733     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (80)

1. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD et al. The sequence of the human genome. Science (2001) 291(5507):1304-1351.
2. Brown D, Superti-Furga G: Rediscovering the sweet spot in drug discovery. Drug Disc Today (2003) 8(23):1067-1077.
3. Lloyd DG, Buenemann CL, Todorov NP, Manallack DT, Dean PM: Scaffold hopping in de novo design - ligand generation in the absence of receptor information. J Med Chem (2004) 47(3):493-496. De novo design in the absence of site coordinates.
4. Majeux N, Scarsi M, Caflisch A: Efficient electrostatic solvation model for protein-fragment docking. Proteins (2001) 42(2):256-268.
5. Boehm HJ, Boehringer M, Bur D, Gmuender H, Huber W, Klaus W, Kostrewa D, Kuehne H, Luebbers T, Meunier-Keller N, Mueller F: Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods and 3D guided optimization. A promising alternative to random screening. J Med Chem (2000) 43(14):2664-2674.
6. Congreve MS, Davis DJ, Devine L, Granata C, O'Reilly M, Wyatt PG, Jhoti H: Detection of ligands from a dynamic combinatorial library by X-ray crystallography. Angew Chem Int Ed (2003) 42(37):4479-4482.
7. Sharff A, Jhoti H: High-throughput crystallography to enhance drug discovery. Curr Opin Chem Biol (2003) 7(3):340-345.
8. Pellecchia M, Sem DS, Wüthrich K: NMR in drug discovery. Nat Rev Drug Disc (2002) 1(3):211-219.
9. Hann MM, Leach AR, Harper G: Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci (2001) 41(3):856-864.
10. Vinkers HM, de Jonge MR, Daeyaert FF, Heeres J, Koymans LM, van Lenthe JH, Lewi PJ, Timmerman H, Van Aken KV, Janssen PA: SYNOPSIS: SYNthesize and Optimize System In Silico. J Med Chem (2003) 46(13):2765-2773. De novo design targeted at ligands with increased synthetic accessibility.
11. Cross KP, Myatt G, Yang C, Fligner MA, Verducci JS, Blower PE Jr: Finding discriminating structural features by reassembling common building blocks. J Med Chem (2003) 46(22):4770-4775.
12. Sun Y, Ewing TJ, Skillman AG, Kuntz ID: CombiDOCK: Structure-based combinatorial docking and library design. J Comput Aided Mol Des (1998) 12(6):597-604.
13. Makino S, Ewing TJ, Kuntz ID: DREAM++: Flexible docking program for virtual combinatorial libraries. J Comput Aided Mol Des (1999) 13(5):513-532.
14. Pegg SC, Haresco JJ, Kuntz ID: A genetic algorithm for structure-based de novo design. J Comput Aided Mol Des (2001) 15(10):911-933.
15. Budin N, Ahmed S, Majeux N, Caflisch A: An evolutionary approach for structure-based design of natural and non-natural peptidic ligands. Comb Chem High Throughput Screen (2001) 4(8):661-673. A method for designing synthetically accessible ligands, taking into account solvation effects. Examples of successfully reproduced known peptide binders are given.
16. Burke MD, Berger EM, Schreiber SL: Generating diverse skeletons of small molecules combinatorially. Science (2003) 302(5645):613-618.
17. Stahl M, Rarey M: Detailed analysis of scoring functions for virtual screening. J Med Chem (2001) 44(7):1035-1042.
18. Wang R, Lu Y, Wang S: Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem (2003) 46(12):2287-2303.
19. Charifson PS, Corkery JJ, Murcko MA, Walters WP: Consensus scoring: A method for obtaining improved hit rates from docking databases of three dimensional structures into proteins. J Med Chem (1999) 42(25):5100-5109.
20. Jacobsson M, Liden P, Stjemschantz E, Boström H, Norinder U: Improving structure-based virtual screening by multivariate analysis of scoring date. J Med Chem (2003) 46(26):5781-5789.
21. Pearlman DA: Free energy grids: A practical qualitative application of free energy perturbation to ligand design using the OWFEG method. J Med Chem (1999) 42(21):4313-4324.
22. Ferrara P, Apostolakis J, Caflisch A: Evaluation of a fast implicit solvent model for molecular dynamics simulations. Proteins (2002) 46(1):24-33.
23. Lazaridis T, Karplus M: Effective energy function for proteins in solution. Proteins (1999) 35(2):133-152.
24. Luo R, David L, Gilson MK: Accelerated Polsson-Boltzmann calculations for static and dynamic systems. J Comput Chem (2002) 23(13):1244-1253.
25. Bashford D, Case D: Generalized Bom models of macromolecular solvation effects. Annu Rev Phys Chem (2000) 51:129-152.
26. Hummer G: Hydrophobic force field as a molecular alternative to surface-area models. J Am Chem Soc (1999) 121(26):6299-6305.
27. Siebert X, Hummer G: Hydrophobicity maps of the N-peptide coiled coil of HIV-1 gp41. Biochemistry (2002) 41(9):2956-2961.
28. Gallicchio E, Levy RM: AGBNP: An analytic implicit solvent model suitable for molecular dynamics simulations and high-resolution modelling. J Comput Chem (2004) 25(4):479-499.
29. Totrov M: Accurate and efficient Generalized Born model based on solvent accessibility. Derivation and application for logP octanol/water prediction and flexible peptide docking. J Comput Chem (2004) 25(4):609-619.
30. Lum K, Chandler D, Weeks JD: Hydrophobicity at small and large length scales. J Phys Chem B (1999) 103(22):4570-4577.
31. Verkhivker GM, Bouzida D, Gehlaar DK, Rejto PA, Freer ST, Rose PW: Complexity and simplicity of ligand-macromolecule interactions: The energy landscape perspective. Curr Opin Struct Biol (2002) 12(2):197-203.
32. Rejto PA, Verkhivker GM: Unraveling principles of lead discovery: From unfrustrated energy landscapes to novel molecular anchors. Proc Natl Acad Sci USA (1996) 93(17):8945-8950.
33. Wang J, Verkhivker GM: Energy landscape theory, funnels, specificity and optimal criterion of biomolecular binding. Phys Rev Lett (2003) 90(18):188101. Ideas from energy landscape theory are applied to ligand docking and a criterion is derived to discriminate optimal ligands, which could have significant implications for ligand scoring.
34. Teague SJ: Implications of protein flexibility for drug discovery. Nat Rev Drug Disc (2003) 2(7):527-541. A thorough discussion of the difficulties of incorporating protein flexibility in de novo ligand design and its exploration for drug discovery in general.
35. Rauh D, Klebe G, Stubbs MT: Understanding protein-ligand interactions: The price of protein flexibility. J Mol Biol (2004) 335(5):1325-1341.
36. Billas IM, Iwema T, Garnier JM, Mitschler A, Rochel N, Moras D: Structural adaptability in the ligand-binding pocket of the ecdysone hormone receptor. Nature (2003) 426(6962):91-96.
37. Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D: Design of a novel globular protein fold with atomic-level accuracy. Science (2003) 302(5649):1364-1368.
38. Bouzida D, Rejto PA, Arthurs S, Colson AB, Freer ST, Gehlhaar DK, Larson V, Luty BA, Rose PW, Verkhivker GM: Computer simulations of ligand-protein binding with ensembles of protein conformations: A Monte Carlo study of HIV-1 protease binding energy landscapes. Int J Quantum Chem (1999) 72(1):73-84.
39. Stultz C, Karplus M: MCSS functionality maps for a flexible protein. Proteins (1999) 37(4):512-529. One of the first attempts for de novo ligand design in a flexible binding site.
40. Claussen H, Buning C, Rarey M, Lengauer T: FlexE: Efficient molecular docking considering protein structure variations. J Mol Biol (2001) 308(2):377-395.
41. Fernandez-Recio J, Totrov M, Abagyan R: ICM-DISCO by global energy optimization with fully flexible side chains. Proteins (2003) 52(1):113-117.
42. Coutsias EA, Seok C, Jacobson MP, Dill KA: A kinematic view of loop closure. J Comput Chem (2004) 25(4):510-528.
43. Zhu J, Fan H, Liu H, Shi Y: Structure-based ligand design for flexible proteins. J Comput Aided Mol Des (2001) 15(11):979-996.
44. Källblad P, Todorov NP, Willems H, Alberts IL: Reagent screening in highly flexible sites. J Med Chem (2004): in press.
45. Rarey M, Kramer B, Lengauer T: The particle concept: Placing discrete water molecules during protein-ligand docking predictions. Proteins (1999) 34(1):17-28.
46. Mancera RL: De novo ligand design with explicit water molecules: An application to bacterial neuraminidase. J Comput Aided Mol Des (2002) 16(7):479-499.
47. Lamb ML, Burdick KW, Toba S, Young MM, Skillman AG, Zou X, Arnold JR, Kuntz ID: Design, docking and evaluation of multiple libraries against multiple targets. Proteins (2001) 42(3):296-318.
48. Janin J: Quantifying biological specificity: The statistical mechanics of molecular recognition. Proteins (1996) 25(4):438-445.
49. Sims PA, Wong CF, McCammon JA: A computational model of binding thermodynamics: The design of cyclin-dependent kinase 2 inhibitors. J Med Chem (2003) 46(15):3314-3325.
50. Kastenholz MA, Pastor M, Cruciani G, Haaksma EE, Fox T: GRID/CPCA: A new computational tool to design selective ligands. J Med Chem (2000) 43(16):3033-3044. A novel method for site analysis of related proteins highlighting specificity features.
51. Ridderstrom M, Zamora I, Fjellstrom O, Andersson TB: Analysis of selective regions in the active sites of human cytochromes P450, 2C8, 2C9, 2C18 and 2C19 homology models using GRID/CPCA. J Med Chem (2001) 44(24):4072-4081.
52. Baskin II, Tikhonova IG, Palyulin VA, Zefirov NS: Selectivity fields: Comparative molecular field analysis (CoMFA) of the glycine/NMDA and AMPA receptors. J Med Chem (2003) 46(19):4063-4069.
53. Kangas E, Tidor B: Electrostatic specificity in molecular ligand design. J Chem Phys (2000) 112(20):9120-9131. Precise analysis of the electrostatic specificity of ligands including hydration/dehydration contributions, with exemplification that the strongest binders are not always the most specific ones.
54. Greenbaum DC, Arnold WD, Lu F, Hayrapetian L, Baruch A, Krumrine J, Toba S, Chehade K, Brömme D, Kuntz ID, Bogyo M: Small molecule affinity fingerprinting: A tool for enzyme family subclassification, target identification, and inhibitor design. Chem Biol (2002) 9(10):1085-1094.
55. Vigers GP, Rizzi J: Multiple active site corrections for docking and virtual screening. J Med Chem (2004) 47(1):80-89.
56. 3 receptor antagonists, using the newly developed molecular modeling program SLATE. J Med Chem (2001) 44(11):1666-1674.
57. Patel Y, Gillet VJ, Bravi G, Leach AR: A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J Comput Aided Mol Des (2002) 16(8-9):653-681.
58. Guner OF: History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem (2002) 2(12):1321-1332
59. Lemmen C, Lengauer T: Computational methods for the structural alignment of molecules. J Comput Aided Mol Des (2000) 14(3):215-232.
60. Mestres J, Rohrer DC, Maggiora GM: A molecular-field-based similarity study of non-nucleoside HIV-1 reverse transcriptase Inhibitors. 2. The relationship between alignment solutions obtained from conformationally rigid and flexible matching. J Comput Aided Mol Des (2000) 14(1):39-51.
61. Good AC, Cheney DL: Analysis and optimization of structure-based virtual screening protocols (1): Exploration of ligand conformational sampling techniques. J Mol Graphics Model (2003) 22(1):23-30.
62. Jain AN: Ligand-based structural hypothesis for virtual screening. J Med Chem (2004) 47(4):947-961.
63. Bernard D, Coop A, MacKerell AD Jr: 2D Conformationally sampled pharmacophore: A ligand-based pharmacophore to differentiate δ opioid agonists from antagonists. J Am Chem Soc (2003) 125(10):3101-3107.
64. Putta S, Eksterowicz J, Lemmen C, Stanton R: A novel subshape molecular descriptor. J Chem Inf Comput Sci (2003) 43(5):1623-1635.
65. Melani F, Gratteri P, Adamo M, Bonacchini C: Field Interaction and geometrical overlap: A new simplex and experimental design based computational procedure for superposing small ligands. J Med Chem (2003) 46(8):1359-1371.
66. Perry NC, Lloyd DG, Todorov NP, Alberts IL: The use of ligand-based site models in virtual high-throughput screening. In: EuroQSAR 2002 Designing Drugs and Crop Protectants: Processes, Problems and Solutions. Ford M, Livingston D, Dearden J, van de Waterbeemd H (Eds), Blackwell Publishing, Oxford, UK (2003):68-72.
67. Kurogi Y, Guner OF: Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem (2001) 8(9):1035-1055.
68. Lloyd DG, Perry NC, Todorov NP, de Esch IJP, Alberts IL: Quasi2: Virtual site model derivation and application to lead identification. 224th ACS Meeting, Boston, MA, USA (2002):CINF0015
69. Naerum L, Norskov-Lauritsen L, Diesen PH: Scaffold hopping and optimization towards libraries of glycogen synthase kinase-3 inhibitors. Bioorg Med Chem Lett (2002) 12(11):1525-1528. A good illustration of scaffold hopping.
70. Sippl W: Development of biologically active compounds by combining 3D QSAR and structure-based design methods. J Comput Aided Mol Des (2002) 16(11):825-830.
71. Fradera X, Knegtel RM, Mestres J: Similarity-driven flexible ligand docking. Proteins (2000) 40(4):623-636.
72. Greenidge PA, Merette SA, Beck R, Dodson G, Goodwin CA, Scully MF, Spencer J, Weiser J, Deadman JJ: Generation of ligand conformations in continuum solvent consistent with protein active site topology: Application to thrombin. J Med Chem (2003) 46(8):1293-1305.
73. Murcia M, Ortitz AR: Virtual screening with flexible docking and COMBINE-based models. Application to a series of Factor Xa inhibitors. J Med Chem (2004) 47(4):805-820.
74. Kuo CL, Assefa H, Kamath S, Brzozowski Z, Slawinski J, Saczewski F, Buolamwini JK, Neamati N: Application of CoMFA and CoMSIA 3D-QSAR and docking studies in optimization of mercaptobenzensulfonamides as HIV-1 integrase inhibitors. J Med Chem (2004) 47(2):385-399.
75. Feher M, Deretey E, Roy S: BHB: A simple knowledge-based scoring function to improve the efficiency of database screening. J Chem Inf Comput Sci (2003) 43(4):1316-1327.
76. Hindle SA, Rarey M, Buning C, Lengauer T: Flexible docking under pharmacophore type constraints. J Comput Aided Mol Des (2002) 16(2):129-149.
77. Deng Z, Chuaqui C, Singh J: Structural Interaction Fingerprint (SIFt): A novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem (2004) 47(2):337-344.
78. Stahl M, Todorov NP, James T, Mauser H, Boehm HJ, Dean PM: A validation study of the practical use of automated de novo design. J Comput Aided Mol Des (2002) 16(7):459-478. In silico validation of de novo design methods across a broad spectrum of target sites.
79. Evers A, Gohlke H, Klebe G: Ligand-supported homology modeling of protein binding sites using knowledge-based potentials. J Mol Biol (2003) 334(2):327-345.
80. De Groot MJ, Alex AA, Jones BC: Development of a combined protein and pharmacophore model for cytochrome P450 2C9. J Med Chem (2002) 45(10):1983-1993.