Journal of Computer-Aided Molecular Design

, Volume 24, Issue 10, pp 843–864 | Cite as

Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies

  • Prabu Manoharan
  • R. S. K. Vijayan
  • Nanda GhoshalEmail author


The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables (X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.





The authors thank Council of Scientific and Industrial Research (CSIR), New Delhi, India, for providing a financial grant under the Mission Mode Program CMM 0017. M.P thanks CSIR for a Project Fellowship and R.S.K.V thanks CSIR for a Senior Research Fellowship.

Supplementary material

10822_2010_9378_MOESM1_ESM.doc (440 kb)
Supplementary material 1 (DOC 440 kb)


  1. 1.
    Hansch C, Fujita T (1964) ρ-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626CrossRefGoogle Scholar
  2. 2.
    Zartler ER, Shapiro MJ (2005) Fragonomics: Fragment-based drug discovery. Curr Opin Chem Biol 9:366–370CrossRefGoogle Scholar
  3. 3.
    Verdonk ML, Hartshorn MJ (2004) Structure-guided fragment screening for lead discovery. Curr Opin Drug Discov Dev 7:404–410Google Scholar
  4. 4.
    Miranker A, Karplus M (1991) Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11:29–34CrossRefGoogle Scholar
  5. 5.
    Caflisch A, Miranker A, Karplus M (1993) Multiple copy simultaneous search and construction of ligands in binding sites. J Med Chem 36:2142–2167CrossRefGoogle Scholar
  6. 6.
    Evensen E, Joseph-McCarthy D, Karplus M (1997) MCSSV2. Harvard University, CambridgeGoogle Scholar
  7. 7.
    Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857CrossRefGoogle Scholar
  8. 8.
    Jencks WP (1981) On the attribution and additivity of binding energies. Proc Natl Acad Sci USA 78:4046–4050CrossRefGoogle Scholar
  9. 9.
    Free SM, Wilson JW (1964) A mathematical contribution to structure- activity studies. J Med Chem 7:395–399CrossRefGoogle Scholar
  10. 10.
    Fujita T, Ban T (1971) Structure-activity study of phenethylamines as substrates of biosynthetic enzymes of sympathetic transmitters. J Med Chem 14:148–152CrossRefGoogle Scholar
  11. 11.
    Vassar R, Bennett BD, Babu-Khan S, Kahn S, Mendiaz EA, Denis P, Teplow DB, Ross S, Amarante P, Loeloff R, Luo Y, Fisher S, Fuller J, Edenson S, Lile J, Jarosinski MA, Biere AL, Curran E, Burgess T, Louis JC, Collins F, Treanor J, Rogers G, Citron M (1999) β-Secretase cleavage of Alzheimer’s amyloid precursor protein by the transmembrane aspartic protease BACE. Science 286:735–741CrossRefGoogle Scholar
  12. 12.
    Yan R, Bienkowski MJ, Shuck ME, Miao H, Tory MC, Pauley AM, Brashier JR, Stratman NC, Mathews WR, Buhl AE, Carter DB, Tomasselli AG, Parodi LA, Heinrikson RL, Gurney ME (1999) Membrane-anchored aspartyl protease with Alzheimer’s disease β-secretase activity. Nature 402:533–537CrossRefGoogle Scholar
  13. 13.
    Selkoe DJ (1999) Translating cell biology into therapeutic advances in Alzheimer’s disease. Nature 399:A23–A31CrossRefGoogle Scholar
  14. 14.
    Baldwin ET, Bhat TN, Gulnik S, Hosur MV, Sowder RC 2nd, Cachau RE, Collins J, Silva AM, Erickson JW (1993) Crystal structures of native and inhibited forms of human cathepsin D: implications for lysosomal targeting and drug design. Proc Natl Acad Sci USA 90:6796–6800CrossRefGoogle Scholar
  15. 15.
    Saunders AJ, Kim TW, Tanzi RE (1999) BACE maps to chromosome 11 and a BACE homolog, BACE2, reside in the obligate down syndrome region of chromosome 21. Science 286:1255aCrossRefGoogle Scholar
  16. 16.
    Charrier N, Clarke B, Cutler L, Demont E, Dingwall C, Dunsdon R, East P, Hawkins J, Howes C, Hussain I, Jeffrey P, Maile G, Matico R, Mosley J, Naylor A, O’Brien A, Redshaw S, Rowland P, Soleil V, Smith JK, Sweitzer S, Theobald P, Vesey D, Walter SD, Wayne G (2008) Second generation of hydroxyethylamine BACE-1 inhibitors: optimizing potency and oral bioavailability. J Med Chem 51:3313–3317CrossRefGoogle Scholar
  17. 17.
    Clarke B, Demont E, Dingwall C, Dunsdon R, Faller A, Hawkins J, Hussain I, MacPherson D, Maile G, Matico R, Milner P, Mosley J, Naylor AA, O’Brien A, Redshaw S, Riddell D, Rowland P, Soleil V, Smith JK, Stanway S, Stemp G, Sweitzer S, Theobald P, Vesey D, Walter DS, Ward J, Wayne G (2008) BACE-1 inhibitors part 1: identification of novel hydroxy ethylamines (HEAs). Bioorg Med Chem Lett 18:1011–1016CrossRefGoogle Scholar
  18. 18.
    Clarke B, Demont E, Dingwall C, Dunsdon R, Faller A, Hawkins J, Hussain I, MacPherson D, Maile G, Matico R, Milner P, Mosley J, Naylor AA, O’Brien A, Redshaw S, Riddell D, Rowland P, Soleil V, Smith JK, Stanway S, Stemp G, Sweitzer S, Theobald P, Vesey D, Walter DS, Ward J, Wayne G (2008) BACE-1 inhibitors part 2: Identification of hydroxyl ethylamines (HEAs) with reduced peptidic character. Bioorg Med Chem Lett 18:1017–1021CrossRefGoogle Scholar
  19. 19.
    Beswick P, Charrier N, Clarke B, Demont E, Dingwall C, Dunsdon R, Faller A, Gleave R, Hawkins J, Hussain I, Johnson CN, MacPherson D, Maile G, Milner P, Naylor A, O’Brien A, Redshaw S, Riddell D, Rowland P, Skidmore J, Soleil V, Smith K, Stanway S, Stemp G, Stuart A, Theobald P, Vesey D, Walter DS, Ward J, Wayne G (2008) BACE-1 inhibitors. Part 3: identification of hydroxyl ethylamines (HEAs) with nanomolar potency in cells. Bioorg Med Chem Lett 18:1022–1026CrossRefGoogle Scholar
  20. 20.
    Congreve M, Aharony D, Albert J, Callaghan O, Campbell J, Carr RA, Chessari G, Cowan S, Edwards PD, Frederickson M, McMenamin R, Murray CW, Patel S, Wallis N (2007) Application of fragment screening by X-ray crystallography to the discovery of aminopyridines as inhibitors of beta-secretase. J Med Chem 50:1124–1132CrossRefGoogle Scholar
  21. 21.
    Murray CW, Callaghan O, Chessari G, Cleasby A, Congreve M, Frederickson M, Hartshorn JM, McMenamin R, Patel S, Wallis N (2007) Application of fragment screening by X-ray crystallography to the discovery of aminopyridines as inhibitors of beta-secretase. J Med Chem 50:1116–1123CrossRefGoogle Scholar
  22. 22.
    Nicolaou AC, Brown N, Pattichis SC (2007) Molecular optimization using computational multi-objective methods. Curr Opin Drug Discov Devel 3:316–324Google Scholar
  23. 23.
    Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Quality Technol 12:214–219Google Scholar
  24. 24.
    Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41:856–864Google Scholar
  25. 25.
    HyperChem 7.52 Hypercube Inc., Gainesville Fl 32608 USAGoogle Scholar
  26. 26.
    Molecular Operating EnVironment (MOE) (2009) Chemical computing group, MontrealGoogle Scholar
  27. 27.
    SPSS Version 15.0 (2009) SPSS Inc., ChicagoGoogle Scholar
  28. 28.
    Vijayan RS, Ghoshal N (2008) Structural basis for ligand recognition at the benzodiazepine binding site of GABAA alpha 3 receptor, and pharmacophore-based virtual screening approach. J Mol Graph Model 27:286–298CrossRefGoogle Scholar
  29. 29.
    TSAR Version 3.3 (2007) Accelrys Inc., San DiegoGoogle Scholar
  30. 30.
    Hajduk PJ (2006) Puzzling through fragment-based drug design. Nat Chem Biol 12:658–659CrossRefGoogle Scholar
  31. 31.
    Cruz-Monteagudo M, Borges F, Cordeiro MN (2008) Desirability-based multiobjective optimization for global QSAR studies: application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles. J Comput Chem 29:2445–2459CrossRefGoogle Scholar
  32. 32.
    STATISTICA, 6.0 (2001) Statsoft_IncGoogle Scholar
  33. 33.
    Laskowski RA, Thornton JM, Humblet C, Singh J (1996) X-SITE: use of empirically derived atomic packing preferences to identify favourable interaction regions in the binding sites of proteins. J Mol Biol 259:175–201CrossRefGoogle Scholar
  34. 34.
    Nissink JWM, Verdonk ML, Klebe G (2000) Simple knowledge-based descriptors to predict protein-ligand interactions. methodology and validation. J Comput Aided Mol Des 14:787–803CrossRefGoogle Scholar
  35. 35.
    Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist’s guide to molecular interactions. J Med Chem 53:5061–5084CrossRefGoogle Scholar
  36. 36.
    Panigrahi SK, Desiraju GR (2007) Strong and weak hydrogen bonds in the protein-ligand interface. Proteins 67:128–141CrossRefGoogle Scholar
  37. 37.
    Desiraju GR, Sarkel S (2004) N–H …O, O–H …O, and C–H …O hydrogen bonds in protein-ligand complexes: strong and weak interactions in molecular recognition. Proteins 54:247–259Google Scholar
  38. 38.
    Barratt E, Bronowska A, Vondrasek J, Cerny J, Bingham R, Phillips S, Homans SW (2006) Thermodynamic penalty arising from burial of a ligand polar group within a hydrophobic pocket of a protein receptor. J Mol Biol 362:994–1003CrossRefGoogle Scholar
  39. 39.
    Verloop A, Hoogenstraaten W, Tipker J (1976) Development and application of new steric substituent parameters in drug design. In Drug Design Academic Press, New YorkGoogle Scholar
  40. 40.
    Hall LH, Kier LB (1992) The molecular connectivity chi indexes and kappa shape indexes in structure-property modeling, Reviews in Computational Chemistry ed, WileyGoogle Scholar
  41. 41.
    Hansch C, Leo AJ (1979) Substituent constants for correlation analysis in chemistry and biology. Wiley, New YorkGoogle Scholar
  42. 42.
    Golbraikh A, Tropsha A (2002) Beware of q2. J Mol Graph Model 20:269–276CrossRefGoogle Scholar
  43. 43.
  44. 44.
    Cronin MTD, Schultz TW (2003) Pitfalls in QSARs. THEOCHEM 622:39–52CrossRefGoogle Scholar
  45. 45.
    Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression based QSARs. Environ Health Perspect 111:1361–1375CrossRefGoogle Scholar
  46. 46.
    Pavlidis P, Noble WS (2003) Matrix2png: a utility for visualizing matrix data. Bioinformatics 19:295–296CrossRefGoogle Scholar
  47. 47.
    OpenEye (2006) OpenEye Scientific Software Santa Fe NMGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Prabu Manoharan
    • 1
  • R. S. K. Vijayan
    • 1
  • Nanda Ghoshal
    • 1
    Email author
  1. 1.Structural Biology and Bioinformatics DivisionIndian Institute of Chemical Biology, (A unit of CSIR)Jadavpur, KolkataIndia

Personalised recommendations