Skip to main content
Log in

Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of d-amino acid oxidase inhibitors

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Optimization of fragment size d-amino acid oxidase (DAAO) inhibitors was investigated using a combination of computational and experimental methods. Retrospective free energy perturbation (FEP) calculations were performed for benzo[d]isoxazole derivatives, a series of known inhibitors with two potential binding modes derived from X-ray structures of other DAAO inhibitors. The good agreement between experimental and computed binding free energies in only one of the hypothesized binding modes strongly support this bioactive conformation. Then, a series of 1-H-indazol-3-ol derivatives formerly not described as DAAO inhibitors was investigated. Binding geometries could be reliably identified by structural similarity to benzo[d]isoxazole and other well characterized series and FEP calculations were performed for several tautomers of the deprotonated and protonated compounds since all these forms are potentially present owing to the experimental pKa values of representative compounds in the series. Deprotonated compounds are proposed to be the most important bound species owing to the significantly better agreement between their calculated and measured affinities compared to the protonated forms. FEP calculations were also used for the prediction of the affinities of compounds not previously tested as DAAO inhibitors and for a comparative structure–activity relationship study of the benzo[d]isoxazole and indazole series. Selected indazole derivatives were synthesized and their measured binding affinity towards DAAO was in good agreement with FEP predictions.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Badner JA, Gershon ES (2002) Meta-analysis of whole-genome linkage scans of bipolar disorder and schizophrenia. Mol Psychiatry 7(4):405–411

    Article  CAS  Google Scholar 

  2. Burnet PWJ, Eastwood SL, Bristow GC, Godlewska BR, Sikka P, Walker M, Harrison PJ (2008) d-amino acid oxidase activity and expression are increased in schizophrenia. Mol Psychiatry 13(7):658–660

    Article  CAS  Google Scholar 

  3. Millan MJ (2005) N-methyl-d-aspartate receptors as a target for improved antipsychotic agents: novel insights and clinical perspectives. Psychopharmacology 179(1):30–53

    Article  CAS  Google Scholar 

  4. Ferraris D, Duvall B, Ko Y-S, Thomas AG, Rojas C, Majer P, Hashimoto K, Tsukamoto T (2008) Synthesis and biological evaluation of d-amino acid oxidase inhibitors. J Med Chem 51(12):3357–3359

    Article  CAS  Google Scholar 

  5. Wolosker H (2007) NMDA receptor regulation by d-serine: new findings and perspectives. Mol Neurobiol 36(2):152–164

    Article  CAS  Google Scholar 

  6. Adage T, Trillat A-C, Quattropani A, Perrin D, Cavarec L, Shaw J, Guerassimenko O, Giachetti C, Gréco B, Chumakov I, Halazy S, Roach A, Zaratin P (2008) In vitro and in vivo pharmacological profile of AS057278, a selective d-amino acid oxidase inhibitor with potential anti-psychotic properties. Eur Neuropsychopharmacol 18(3):200–214

    Article  CAS  Google Scholar 

  7. Lange JHM, Venhorst J, van Dongen MJP, Frankena J, Bassissi F, de Bruin NMWJ., den Besten C, de Beer SBA, Oostenbrink C, Markova N, Kruse CG (2011) Biophysical and physicochemical methods differentiate highly ligand-efficient human d-amino acid oxidase inhibitors. Eur J Med Chem 46(10):4808–4819

    Article  CAS  Google Scholar 

  8. Sparey T, Abeywickrema P, Almond S, Brandon N, Byrne N, Campbell A, Hutson PH, Jacobson M, Jones B, Munshi S, Pascarella D, Pike A, Prasad GS, Sachs N, Sakatis M, Sardana V, Venkatraman S, Young MB (2008) The discovery of fused pyrrole carboxylic acids as novel, potent d-amino acid oxidase (DAO) inhibitors. Bioorg Med Chem Lett 18(11):3386–3391

    Article  CAS  Google Scholar 

  9. Smith SM, Uslaner JM, Yao L, Mullins CM, Surles NO, Huszar SL, McNaughton CH, Pascarella DM, Kandebo M, Hinchliffe RM, Sparey T, Brandon NJ, Jones B, Venkatraman S, Young MB, Sachs N, Jacobson MA, Hutson PH (2009) The behavioral and neurochemical effects of a novel d-amino acid oxidase inhibitor compound 8 [4H-Thieno [3,2-B]pyrrole-5-carboxylic acid] and d-serine. J Pharmacol Exp Ther 328(3):921–930

    Article  CAS  Google Scholar 

  10. Duplantier AJ, Becker SL, Bohanon MJ, Borzilleri KA, Chrunyk BA, Downs JT, Hu LY, El-Kattan A, James LC, Liu S, Lu J, Maklad N, Mansour MN, Mente S, Piotrowski MA, Sakya SM, Sheehan S, Steyn SJ, Strick CA, Williams VA, Zhang L (2009) Discovery, SAR, and pharmacokinetics of a novel 3-Hydroxyquinolin-2(1H)-one series of potent d-amino acid oxidase (DAAO) inhibitors. J Med Chem 52(11):3576–3585

    Article  CAS  Google Scholar 

  11. Berry JF, Ferraris DV, Duvall B, Hin N, Rais R, Alt J, Thomas AG, Rojas C, Hashimoto K, Slusher BS, Tsukamoto T (2012) Synthesis and SAR of 1-hydroxy-1H-Benzo[d]imidazol-2(3H)-ones as inhibitors of d-amino acid oxidase. ACS Med Chem Lett 3(10):839–843

    Article  CAS  Google Scholar 

  12. Hopkins SC, Heffernan MLR, Saraswat LD, Bowen CA, Melnick L, Hardy LW, Orsini MA, Allen MS, Koch P, Spear KL, Foglesong RJ, Soukri M, Chytil M, Fang QK, Jones SW, Varney MA, Panatier A, Oliet SHR, Pollegioni L, Piubelli L, Molla G, Nardini M, Large TH (2013) Structural, kinetic, and pharmacodynamic mechanisms of d-amino acid oxidase inhibition by small molecules. J Med Chem 56(9):3710–3724

    Article  CAS  Google Scholar 

  13. Abel R, Mondal S, Masse C, Greenwood J, Harriman G, Ashwell MA, Bhat S, Wester R, Frye L, Kapeller R, Friesner RA (2017) Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 43:38–44

    Article  CAS  Google Scholar 

  14. Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R (2015) 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. J Am Chem Soc 137(7):2695–2703

    Article  CAS  Google Scholar 

  15. Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL, Kaus JW, Cerutti DS, Krilov G, Jorgensen WL, Abel R, Friesner RA (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296

    Article  CAS  Google Scholar 

  16. Kuhn B, Tichý M, Wang L, Robinson S, Martin RE, Kuglstatter A, Benz J, Giroud M, Schirmeister T, Abel R, Diederich F, Hert J (2017) Prospective evaluation of free energy calculations for the prioritization of cathepsin L inhibitors. J Med Chem 60(6):2485–2497

    Article  CAS  Google Scholar 

  17. Steinbrecher TB, Dahlgren M, Cappel D, Lin T, Wang L, Krilov G, Abel R, Friesner R, Sherman W (2015) Accurate binding free energy predictions in fragment optimization. J Chem Inf Model 55(11):2411–2420

    Article  CAS  Google Scholar 

  18. Fang QK, Hopkins S, Jones S (2005) Espacenet - Bibliographic Data

  19. FEP+, Schrödinger LLC (2017) New York, NY

  20. Schrödinger Suite 2017–2 Protein Preparation Wizard, Epik, Prime, Schrödinger LLC (2017) New York, NY

  21. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank Nucleic Acids Research, 28(1):235–242

  22. Glide, Schrödinger LLC (2017) New York, NY

  23. Götz AW, Clark MA, Walker RC (2014) An extensible interface for QM/MM molecular dynamics simulations with AMBER. J Comput Chem 35(2):95–108

    Article  Google Scholar 

  24. Hégely B, Bogár F, Ferenczy GG, Kállay M (2015) A QM/MM program using frozen localized orbitals and the Huzinaga equation. Theor Chem Acc 134(11):132

    Article  Google Scholar 

  25. Song Z, Ogaya T, Ishii K, Ichiba H, Iizuka H, Fukushima T (2010) Utilization of kynurenic acid produced from d-Kynurenine in an in vitro assay of d-amino acid oxidase activity. J Heal Sci 56(3):341–346

    Article  CAS  Google Scholar 

  26. Ku HH (1966) Notes on the use of propagation of error formulas. J Res 70c(4):263–273

    Google Scholar 

  27. Wang L, Deng Y, Knight JL, Wu Y, Kim B, Sherman W, Shelley JC, Lin T, Abel R (2013) Modeling local structural rearrangements using FEP/REST: application to relative binding affinity predictions of CDK2 inhibitors. J Chem Theory Comput 9(2):1282–1293

    Article  CAS  Google Scholar 

  28. Brown SP, Muchmore SW, Hajduk PJ (2009) Healthy skepticism: assessing realistic model performance. Drug Discov Today 14(7–8):420–427

    Article  Google Scholar 

  29. Ferenczy GG, Keserű GM (2016) On the enthalpic preference of fragment binding. Med Chem Commun 7(2):332–337

    Article  CAS  Google Scholar 

  30. Freire E (2008) Do enthalpy and entropy distinguish first in class from best in class?. Drug Discovery Today 13(19–20):869–874

    Article  CAS  Google Scholar 

  31. Ferenczy GG, Keserű GM (2015) The impact of binding thermodynamics on medicinal chemistry optimizations. Future Med Chem 7(10):1285–1303

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Financial support by the National Brain Research Program (project KTIA NAP_13) and by the Hungarian Science Foundation OTKA (project K111862) is gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to György M. Keserű.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 28 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Orgován, Z., Ferenczy, G.G., Steinbrecher, T. et al. Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of d-amino acid oxidase inhibitors. J Comput Aided Mol Des 32, 331–345 (2018). https://doi.org/10.1007/s10822-018-0097-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-018-0097-y

Keywords

Navigation