Abstract
γ-Secretase (gamma secretase) is a complex unusual aspartyl protease, responsible for the production of amyloid-β peptides (Aβ) involved in Alzheimer’s disease (AD). Inhibition of gamma secretase (GS) is an attractive therapeutic strategy to slow down the pathological progression of AD. For a long time, GS-targeted structure-based drug designing remained unrealistic without the 3D structural knowledge of GS. Hence, to meet the prevailing urgent need for AD drugs, several groups individually tried to develop GS inhibitors, with the aid of computational drug designing methods. This chapter mainly provides with a detailed discussion on a QSAR-guided fragment-based virtual screening method for GS inhibitor design and identification. In this study, we took advantage of the wealth of available known small molecular GS inhibitors and applied in this drug designing program. Here, the non-transition state small molecular GS inhibitors with corresponding affinity values were used to develop 2D- and 3D-QSAR models investigating alternative site-binding GS inhibitors. HipHop pharmacophore-based alignment-dependent (CoMFA and CoMSIA) and GRIND-based alignment-independent 3D-QSAR models were developed to elucidate the potential 3D features involved in GS inhibition. Consensus of QSAR results from this study underscores the reliability and accuracy of the results and provides a rationale for the design of novel potent GS inhibitors that can have AD therapeutic application.
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References
Alzheimer (1987) About a peculiar diseases of the cerebral cortex. By Alois Alzheimer, 1907 (Translated by L. Jarvik and H. Greenson). Alzheimer Dis Assoc Disord 1:3–8
Selkoe DJ (2001) Alzheimer’s disease: genes, proteins, and therapy. Physiol Rev 81:741–766
Cummings JL (2004) Alzheimer’s disease. N Engl J Med 351:56–67
Glenner GG, Wong CW (1984) Alzheimer’s disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res Commun 120:885–890
Brookmeyer R, Johnson E, Ziegler-Graham K et al (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement 3:186–191
Prince M, Bryce R, Albanese E et al (2013) The global prevalence of dementia: a systematic review and meta-analysis. Alzheimers Dement 9:63–75
Hardy JA, Higgins GA (1992) Alzheimer’s disease: the amyloid cascade hypothesis. Science 256:184–185
Sisodia SS, St. George-Hyslop PH (2002) γ -secretase, notch, aβand Alzheimer’s disease: where do the presenilins fit in? Nat Rev Neurosci 3:281–289
John V, Beck JP, Bienkowski MJ et al (2003) Human beta-secretase (bace) and bace inhibitors. J Med Chem 46:4625–4630
Lahiri DK, Ghosh C, Ge YW (2003) A proximal gene promoter region for the β-amyloid precursor protein provides a link between development, apoptosis and Alzheimer’s disease. Ann N Y Acad Sci 1010:643–647
Walsh DM, Selkoe DJ (2004) Deciphering the molecular basis of memory failure in Alzheimer’s disease. Neuron 44:181–193
Tolia A, De Strooper B (2009) Structure and function of γ-secretase. Semin Cell Dev Biol 20:211–218
Page RM, Gutsmiedl A, Fukumori A et al (2010) β-amyloid precursor protein mutants respond to γ-secretase modulators. J Biol Chem 285:17798–17810
Iwatsubo T, Odaka A, Suzuki N et al (1994) Visualization of a beta 42(43) and a beta 40 in senile plaques with end-specific a beta monoclonals: evidence that an initially deposited species is a beta 42(43). Neuron 13:45–53
Beel A, Sanders C (2008) Substrate specificity of γ-secretase and other intramembrane proteases. Cell Mol Life Sci 65:1311–1334
Gordon WR, Arnett KL, Blacklow SC (2008) The molecular logic of notch signaling–a structural and biochemical perspective. J Cell Sci 121:3109–3119
Barten DM, Guss VL, Corsa JA et al (2005) Dynamics of β-amyloid reductions in brain, cerebrospinal fluid, and plasma of β-amyloid precursor protein transgenic mice treated with a γ-secretase inhibitor. J Pharmacol Exp Ther 312:635–643
Comery TA, Martone RL, Aschmies S et al (2005) Acute γ-secretase inhibition improves contextual fear conditioning in the Tg2576 mouse model of Alzheimer’s disease. J Neurosci 25:8898–8902
Wolfe MS, Kopan R (2004) Intramembrane proteolysis: theme and variations. Science 305:1119–1123
Edbauer D, Winkler E, Regula JT et al (2003) Reconstitution of gamma-secretase activity. Nat Cell Biol 5:486–488
De Strooper B, Saftig P, Craessaerts K et al (1998) Deficiency of presenilin-1 inhibits the normal cleavage of amyloid precursor protein. Nature 391:387–390
Laudon H, Hansson EM, Melen K et al (2005) A nine-transmembrane domain topology for presenilin 1. J Biol Chem 280:35352–35360
Li H, Wolfe MS, Selkoe DJ (2009) Toward structural elucidation of the γ-secretase complex. Structure 17:326–334
Fagan R, Swindells M, Overington J et al (2001) Nicastrin, a presenilin-interacting protein, contains an amino-peptidase/transferring receptor superfamily domain. Trends Biochem Sci 26:213–214
Fortnam PC, Crystal AS, Morais VA et al (2004) Membrane topology and nicastrin-enhanced endoproteolysis of APH-1, a component of the γ-secretase complex. J Biol Chem 279:3685–3693
Serneels L, Dejaegere T, Craessaerts K et al (2005) Differential contribution of the three aph1 genes to γ-secretase activity in vivo. Proc Natl Acad Sci U S A 102:1719–1724
Crystal AS, Morais VA, Pierson TC et al (2003) Membrane topology γ-secretase component pen-2. J Biol Chem 278:20117–20123
Kimberly WT, LaVoie MJ, Ostaszewski BL et al (2003) Gamma-secretase is a membrane protein complex comprised of presenilin, nicastrin, aph-1, and pen-2. Proc. Natl Acad Sci U S A 100:6382–6387
Wolfe MS, Xia W, Ostaszewski BL et al (1999) Two transmembrane aspartates in presenilin-1 required for presenilin endoproteolysis and gamma-secretase activity. Nature 398:513–517
Kornilova AY, Bihel F, Das C et al (2005) The initial substrate binding site of γ-secretase is located on presenilin near the active site. Proc Natl Acad Sci U S A 102:3230–3235
Shah S, Lee SF, Tabuchi K et al (2005) Nicastrin functions as a gamma-secretase-substrate receptor. Cell 122:435–447
Sobhanifar S, Schneider B, Löhr F et al (2010) Structural investigation of the c-terminal catalytic fragment of presenilin 1. Proc Natl Acad Sci 107:9644–9649
De Stooper B, Iwatsubo T, Wolfe MS (2012) Presenilins and γ-secretase: structure, function, and role in Alzheimer disease. Cold Spring Harb Perspect Med 2:a006304
Bai XC, Yan C, Yang G et al (2015) An atomic structure of human γ-secretase. Nature 525:212–217
Sun L, Zhao L, Yang G et al (2015) Structural basis of human γ-secretase assembly. Proc Natl Acad Sci U S A 112:6003–6008
Klafki H, Abramowski D, Swoboda R et al (1996) The carboxyl termini of β-amyloid peptides 1-40 and 1-42 are generated by distinct γ-secretase activities. J Biol Chem 271:28655–28659
Bateman RJA (2009) γ-secretase inhibitor decreases amyloid-β production in the central nervous system. Ann Neurol 66:48–54
Nguyen J-T, Hamada Y, Kimura T et al (2008) Design of potent aspartic protease inhibitors to treat various diseases. Arch Pharm 341:523–535
Shearman MS, Beher D, Clarke EE et al (2000) L-685,458, an aspartyl protease transition state mimic, is a potent inhibitor of amyloid β-protein precursor γ-secretase activity. Biochemistry 39:8698–8704
Wolfe MS, Xia W, Moore CL et al (1999) Peptidomimetic probes and molecular modeling suggest that Alzheimer’s γ-secretase is an intramembrane-cleaving aspartyl protease. Biochemistry 38:4720–4727
Wallace OB, Smith DW, Deshpande MS et al (2003) Inhibitors of aβ production: solid-phase synthesis and sar of r-hydroxycarbonyl derivatives. Bioorg Med Chem Lett 13:1203–1206
Wolfe MS (2008) γ-Secretase inhibition and modulation for Alzheimer’s disease. Curr Alzheimer Res 5:158–164
Das C, Berezovska O, Diehl TS et al (2003) Designed helical peptides inhibit an intramembrane protease. J Am Chem Soc 125:11794–11795
Dovey HF et al (2001) Functional gamma-secretase inhibitors reduce beta-amyloid peptide levels in brain. J Neurochem 76:173–181
Kreft AF, Martone R, Porte A (2009) Recent advances in the identification of γ-secretase inhibitors to clinically test the aβ oligomer hypothesis of Alzheimer’s disease. J Med Chem 52:6169–6188
Hansch C, Fujita T (1964) ρ-σ-π Analysis: a method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626
Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594
Leach AR, Gillet VJ (2003) An introduction to chemoinformatics. Springer, Dordrecht
Reddy AS, Pati SP, Kumar PP et al (2007) Virtual screening in drug discovery – a computational perspective. Curr Protein Pept Sci 8:329–351
Ravi Keerti A, Ashok Kumar B, Parthasarathy T et al (2005) Qsar studies–potent benzodiazepine gamma-secretase inhibitors. Bioorg Med Chem 13:1873–1878
Sammi T, Silakari O, Ravikumar M (2009) Three-dimensional quantitative structure-activity relationship (3d-qsar) studies of various benzodiazepine analogues of gamma-secretase inhibitors. J Mol Model 15:343–348
Gundersen E, Fan K, Haas K et al (2005) Molecular-modeling based design, synthesis, and activity of substituted piperidines as gamma-secretase inhibitors. Bioorg Med Chem Lett 15:1891–1894
Ajmani S, Janardhan S, Viswanadhan VN (2013) Toward a general predictive qsar model for gamma-secretase inhibitors. Mol Divers 17:421–434
Zettl H, Ness J, Hähnke V et al (2012) Discovery of γ-secretase modulators with a novel activity profile by text-based virtual screening. ACS Chem Biol 7:1488–1495
Manoharan P, Ghoshal N (2012) Rationalizing lead optimization by consensus 2d- comfa comsia grind (3d) qsar guided fragment hopping in search of γ-secretase inhibitors. Mol Divers 16:563–577
Lewis SJ, Smith AL, Neduvelil JG et al (2005) A novel series of potent gamma-secretase inhibitors based on a benzobicyclo[4.2.1] nonane core. Bioorg Med Chem Lett 15:373–378
Sparey T, Beher D, Best J et al (2005) Cyclicsulfamide gamma-secretase inhibitors. Bioorg Med Chem Lett 15:4212–4216
Keown LE, Collins I, Cooper LC et al (2009) Novel orally bioavailable gamma-secretase inhibitors with excellent in vivo activity. J Med Chem 52:3441–3444
Molecular operating environment (MOE) (2009) Chemical Computing Group, Montreal
TSAR, Version 3.3 (2007) Accelrys Inc, San Diego
Cramer RD III, Bunce JD (1988) Comparative molecular field analysis (comfa) 1.Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967
Cramer RD, De Priest SA, Patterson DE et al (1993) In: Kubinyi H (ed) The developing practice of comparative molecular field analysis. in3dqsar in drug design: theory methods and applications. ESCOM, Leiden, pp 443–485
Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (comsia) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146
Klebe G (1998) Comparative molecular similarity indices analysis: comsia. Perspect Drug Discovery Des 12-14:87–104
Sybyl, version 7.3 (2009) Tripos International, St. Louis, 63144
Pastor M, Cruciani G, McLay I et al (2000) Grid-independent descriptors (grind): a novel class of alignment-independent three-dimensional molecular descriptors. JMed Chem 43:3233–3243
Cruciani G, Fontaine F, Pastor M (2004) Almond, 3.3.0. Molecular Discovery Ltd, Perugia
Hoskuldsson A (1988) Pls regression methods. J Chemom 2:211–228
Carey RN, Wold S, Westgard JO (1975) Principal component analysis: an alternative to “referee” methods in method comparison studies. Anal Chem 47:1824–1829
Gramatica P, Giani E, Papa E (2007) Statistical external validation and consensus modeling: a qspr case study for Koc prediction. J Mol Graph Model 25:755–766
Ganguly M, Brown N, Schuffenhauer A et al (2006) Introducing the consensus modeling concept in genetic algorithms: application to interpretable discriminant analysis. J Chem Inf Model 46:2110–2124
Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization and performance of mmff94. J Comput Chem 17:490–451
Spssversion 15.0 (2009) SPSS Inc, Chicago
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 27:286–298
Ehrlich P (1909) Ueber den jetzigenstand der chemotherapie. Ber Dtsch Chem Ges 42:17–47
Smellie A, Teig SL, Towbin P (1995) Poling: promoting conformational variation. J Comput Chem 16:171–187
Catalyst, version 4.11 (2005) Accelrys Inc, San Diego
Barnum D, Greene J, Smellie A et al (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:563–571
Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857
Fontaine F, Pastor M, Sanz F (2004) Incorporating molecular shape into the alignment-free grid-independent descriptors. J Med Chem 47:2805–2815
Baroni M, Costantino G, Cruciani G et al (1993) Generating optimal linear pls estimations (golpe): an advanced chemometric tool for handling 3d-qsar problems. Quant Struct Act Relat 12:9–20
Cho SJ, Zheng W, Tropsha A (1998) Rational combinatorial library design. 2. Rational design of targeted combinatorial peptide libraries using chemical similarity probe and the inverse qsar approaches. J Chem Inf Comput Sci 38:259–268
OpenEye (2006) OpenEye Scientific Software, Santa Fe
Nicholls A, McGaughey GB, Sheridan RP et al (2010) Molecular shape and medicinal chemistry: a perspective. J Med Chem 53:3862–3886
Mills JE, Dean PM (1996) Three-dimensional hydrogen-bond geometry and probability information from a crystal survey. J Comput Aided Mol Des 6:607–622
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Manoharan, P., Ghoshal, N. (2018). Computational Modeling of Gamma-Secretase Inhibitors as Anti-Alzheimer Agents. In: Roy, K. (eds) Computational Modeling of Drugs Against Alzheimer’s Disease. Neuromethods, vol 132. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7404-7_12
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DOI: https://doi.org/10.1007/978-1-4939-7404-7_12
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