Skip to main content
SpringerLink
Log in

Density Functional Theory Calculations of Enzyme–Inhibitor Interactions in Medicinal Chemistry and Drug Design

  • Chapter
  • First Online:
Application of Computational Techniques in Pharmacy and Medicine

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 17))

Abstract

The density functional theory (DFT) is currently predominating theoretical approach in quantum chemistry. It is suitable for investigating structures up to several hundreds of atoms, studying of reaction pathways and calculating precisely reaction energy values. The usage of the DFT approach for studying enzyme–substrate interactions could be a prospective way for elaborating new efficient enzyme inhibitors. This is a direct way to discovery of new drugs and modification of the existing drugs. While enzymes are still too large for the computational analysis using DFT, numerous efforts have been exerted in the last years in this field using simplified enzyme models or calculating for the substrate some valuable properties, important in the enzyme–substrate interactions. These examples have been analyzed in the current review. A rapid development of new efficient calculation routines makes it possible to increase the role of the DFT methods in medicinal chemistry in the nearest future.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ACE:

angiotensin-converting enzyme

AChE:

acetylcholinesterase

AD:

Alzheimer’s disease

AIDS:

acquired immune deficiency syndrome

BACE-1:

betasite of APP-cleaving enzyme-1

BChE:

butyrylcholinesterase

Cat B:

cathepsin B

DFT:

density functional theory

DNA:

deoxyribonucleic acid

EP:

electrostatic potential

EPS:

electrostatic potential surface

FAAH:

fatty acid amide hydrolase

FEP:

free energy perturbation

HIV:

human immunodeficiency virus

HMGR:

3-hydroxy-3-methylglutaryl-coenzyme A reductase

HOMO:

highest occupied molecular orbital

IC50 :

half maximal inhibitory concentration

IEF:

integral equation formalism

IN:

integrase

LUMO:

lowest unoccupied molecular orbital

MD:

molecular dynamics

MD/MM:

molecular mechanics/molecular dynamics

MEP:

molecular electrostatic potential

MFCC:

molecular fractionation with conjugate caps approach

MMP:

matrix metalloproteinase

MNDO:

modified neglect of diatomic overlap

MO:

molecular orbital

MP2:

second-order Møller-Plesset perturbation theory

PCM:

polarizable continuum model

PDE:

phosphodiesterase

PES:

potential energy surfaces

PLA2 :

phospholipases A2 enzymes

PM3:

parameterized model number 3 (Stewart’s semi-empirical approach)

PMF:

potential of mean force

QM/MM:

quantum mechanic/molecular mechanics hybrid approach

QSAR:

quantitative structure–activity relationship

RHF:

restricted Hartree-Fock method

RI:

resolution of the identity

RNA:

ribonucleic acid

SCC-DFTB:

self-consistent charge-density functional tight binding

SCRFPCM:

self-consistent reaction field polarizable continuum model

SIBFA:

sum of interactions between fragments ab initio computed

TSS:

transition state structures

XO:

xanthine oxidase

References

  1. Putz MV, Mingos DMP (eds) (2013) Applications of density functional theory to biological and bioinorganic chemistry. Springer, Berlin. doi:10.1007/978-3-642-32750-6

    Google Scholar 

  2. Dahan A, Khamis M, Agbaria R, Karaman R (2012) Targeted prodrugs in oral drug delivery: the modern molecular biopharmaceutical approach. Expert Opin Drug Del 9(8):1001–1013. doi:10.1517/17425247.2012.697055

    CAS  Google Scholar 

  3. Kortagere S (ed) (2013) In silico models for drug discovery: methods in molecular biology, vol 993. Humana Press, Totowa. doi:10.1007/978-1-62703-342-8

    Google Scholar 

  4. Jorgensen WL (2010) Drug discovery: pulled from a protein’s embrace. Nature 466(7302):42–43. doi:10.1038/466042a

    CAS  Google Scholar 

  5. Sharma R (ed) (2012) Enzyme inhibition and bioapplications. InTech, Rijeka

    Google Scholar 

  6. Barril X (2012) Druggability predictions: methods, limitations, and applications. WIREs Comput Mol Sci. doi:10.1002/wcms.1134. doi:10.1002/wcms.1134

    Google Scholar 

  7. Morris GM, Lim-Wilby M (2008) Molecular docking. In: Methods in molecular biology. Spinger, Clifton, p 365–382

    Google Scholar 

  8. Puzyn T, Leszczynski J, Cronin MT (eds) (2010) Recent advances in QSAR studies, in series: modern techniques and applications, vol 8. Springer, Netherlands. doi:10.1007/978-1-4020-9783-6

    Google Scholar 

  9. Utkov H, Livengood M, Cafiero M (2010) Using density functional theory methods for modeling induction and dispersion interactions in ligand–protein complexes. Ann Rep Comput Chem 6:96–112. doi:10.1016/S1574-1400(10)06007-X

    Google Scholar 

  10. Zhang DW, Zhang JZH (2003) Molecular fractionation with conjugate caps for full quantum mechanical calculation of protein–molecule interaction energy. J Chem Phys 119(7):3599–3605. doi:10.1063/1.1591727

    CAS  Google Scholar 

  11. He X, Mei Y, Xiang Y, Zhang DW, Zhang JZH (2005) Quantum computational analysis for drug resistance of HIV-1 reverse transcriptase to nevirapine through point mutations. Proteins 61(2):423–432. doi:10.1002/prot.20578

    CAS  Google Scholar 

  12. York DM, Lee T-S (eds) (2009) Multi-scale quantum models for biocatalysis. In: Modern techniques and applications, vol. 7, Springer, Dordrecht. doi:10.1007/978-1-4020-9956-4

    Google Scholar 

  13. Mulholland AJ (2007) Chemical accuracy in QM/MM calculations on enzyme-catalysed reactions. Chem Cent J 1(1):19–24. doi:10.1186/1752-153X-1-19

    Google Scholar 

  14. Söderhjelm P, Aquilante F, Ryde U (2009) Calculation of protein–ligand interaction energies by a fragmentation approach combining high-level quantum chemistry with classical many-body effects. J Phys Chem B 113(32):11085–11094

    Google Scholar 

  15. Senn HM, Thiel W (2009) QM/MM methods for biomolecular systems. Angew Chem Int Ed 48(7):1198–1229. doi:10.1002/anie.200802019

    CAS  Google Scholar 

  16. de Vivo M (2011) Bridging quantum mechanics and structure-based drug design. Front Biosci 16(1):1619–1633. doi:10.2741/3809

    Google Scholar 

  17. Lodola A, de Vivo M (2012) The increasing role of QM/MM in drug discovery. Adv Prot Chem Struct Biol 87:337–362. doi:10.1016/B978-0-12-398312-1.00011-1

    CAS  Google Scholar 

  18. Cavalli A, Carloni P, Recanatini M (2006) Target-related applications of first principles quantum chemical methods in drug design. Chem Rev 106(9):3497–3519. doi:10.1021/cr050579p

    CAS  Google Scholar 

  19. Hu L, Söderhjelm P, Ryde U (2013) Accurate reaction energies in proteins obtained by combining QM/MM and large QM calculations. J Chem Theor Comput 9(1):640–649. doi:10.1021/ct3005003

    CAS  Google Scholar 

  20. Zalesny R, Papadopoulos MG, Mezey PG, Leszczynski J (eds) (2011) Linear-scaling techniques in computational chemistry and physics. In: Challenges advances computational chemistry physics, vol 13. Springer, Dordrecht. doi:10.1007/978-90-481-2853-2

    Google Scholar 

  21. Whitten JL (1973) Coulombic potential energy integrals and approximations. J Chem Phys 58(10):4496–4501. doi:10.1063/1.1679012

    CAS  Google Scholar 

  22. Baerends EJ, Ellis DE, Ros P (1973) Self-consistent molecular Hartree–Fock–Slater calculations I. The computational procedure. Chem Phys 2(1):41–51. doi:10.1016/0301-0104(73)80059-X

    CAS  Google Scholar 

  23. Dunlap BI, Connolly JWD, Sabin JR (1979) On some approximations in applications of Xα theory. J Chem Phys 71(8):3396–3402. doi:10.1063/1.438728

    CAS  Google Scholar 

  24. Dunlap BI, Connolly JWD, Sabin JR (1979) On first-row diatomic molecules and local density models. J Chem Phys 71(12):4993–4999. doi:10.1063/1.438313

    CAS  Google Scholar 

  25. Feyereisen M, Fitzgerald G, Komornicki A (1993) Use of approximate integrals in ab initio theory. An application in MP2 energy calculations. Chem Phys Lett 208 359–363. doi:10.1016/0009-2614(93)87156-W

    CAS  Google Scholar 

  26. Eichkorn K, Weigend F, Treutler O, Ahlrichs R (1997) Auxiliary basis sets for main row atoms and transition metals and their use to approximate Coulomb potentials. Theor Chem Acc 97(1-4):119–124. doi:10.1007/s002140050244

    CAS  Google Scholar 

  27. Weigend F, Häser M, Patzelt H, Ahlrichs R (1998) RI-MP2: optimized auxiliary basis sets and demonstration of efficiency. Chem Phys Lett 294(1–3):143–152. doi:10.1016/S0009-2614(98)00862-8

    CAS  Google Scholar 

  28. Furche F, Ahlrichs R, Hättig C, Klopper W, Sierka M, Weigend F (2013) Turbomole. WIREs Comput Mol Sci. doi:10.1002/wcms.1162

    Google Scholar 

  29. Neese F (2012) The ORCA program system. WIREs Comput Mol Sci 2(1):73–78. doi:10.1002/wcms.81

    CAS  Google Scholar 

  30. Aguirre D, Chifotides HT, Angeles-Boza AM, Chouai A, Turro C, Dunbar KR (2009) Redox-regulated inhibition of T7 RNA polymerase via establishment of disulfide linkages by substituted Dppz dirhodium(I, II) complexes. Inorg Chem 48(10):4435–4444. doi:10.1021/ic900164j

    CAS  Google Scholar 

  31. Villar JAFP, Lima FTD, C.L. Veber, A.R.M. Oliveira, A.K. Calgarotto, S. Marangoni, da Silva SL (2008) Synthesis and evaluation of nitrostyrene derivative compounds, new snake venom phospholipase A2 inhibitors. Toxicon 51(8):1467–1478. doi:10.1016/j.toxicon.2008.03.023

    CAS  Google Scholar 

  32. Parameswari AR, Kumaradhas P (2013) Exploring the conformation, charge density distribution and the electrostatic properties of galanthamine molecule in the active site of AChE using DFT and AIM theory. Int J Quant Chem 113(8):1200–1208. doi:10.1002/qua.24251

    Google Scholar 

  33. Silva JRA, Lameira J, Alves CN (2012) Insights for design of Trypanosoma cruzi GAPDH inhibitors: a QM/MM MD study of 1,3-bisphospo-d-glyceric acid analogs. Int J Quant Chem 112(20):3398–3402. doi:10.1002/qua.24253

    Google Scholar 

  34. Negri M, Recanatini M, Hartmann RW (2011) Computational investigation of the binding mode of bis(hydroxylphenyl) arenes in 17β-HSD1: molecular dynamics simulations, MM-PBSA free energy calculations, and molecular electrostatic potential maps. J Comp Aid Mol Des 25(9):795–811. doi:10.1007/s10822-011-9464-7

    CAS  Google Scholar 

  35. Arooj M, Thangapandian S, John S, Hwang S, Park JK, Lee KW (2012) Computational studies of novel chymase inhibitors against cardiovascular and allergic diseases: mechanism and inhibition. Chem Biol Drug Des 80(6):862–875. doi:10.1111/cbdd.12006

    CAS  Google Scholar 

  36. Muzet N, Guillot B, Jelsch C, Howardt E, Lecomte C (2003) Electrostatic complementarity in an aldose reductase complex from ultra-high-resolution crystallography and first-principles calculations. Proc Nat Acad Sci U S A 100(15):8747. doi:10.1073/pnas.1432955100

    Google Scholar 

  37. Van Damme S, Bultinck P (2009) Conceptual DFT properties-based 3D QSAR: analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme. J Comput Chem 30(12):1749–1757. doi:10.1002/jcc.21177

    CAS  Google Scholar 

  38. Wan J, Zhang LI, Yang G (2004) Quantitative structure-activity relationships for phenyl triazolinones of protoporphyrinogen oxidase inhibitors: a density functional theory study. J Comput Chem 25(15):1827–1832. doi:10.1002/jcc.20122

    CAS  Google Scholar 

  39. Zhang L, Hao G-F, Tan Y, Xi Z, Huang M-Z, Yang G-F (2009) Bioactive conformation analysis of cyclic imides as protoporphyrinogen oxidase inhibitor by combining DFT calculations, QSAR and molecular dynamic simulations. Bioorgan Med Chem 17(14):4935–4942. doi:10.1016/j.bmc.2009.06.003

    CAS  Google Scholar 

  40. Lodola A, Capoferri L, Rivara S, Tarzia G, Piomelli D, Mulholland A, Mor M (2013) Quantum mechanics/molecular mechanics modeling of fatty acid amide hydrolase reactivation distinguishes substrate from irreversible covalent inhibitors. J Med Chem 56(6):2500–2512. doi:10.1021/jm301867x

    CAS  Google Scholar 

  41. Gueto-Tettay C, Drosos JC, Vivas-Reyes R (2011) Quantum mechanics study of the hydroxyethylamines-BACE-1 active site interaction energies. J Comp Aid Mol Des 25(6):583–597. doi:10.1007/s10822-011-9443-z

    CAS  Google Scholar 

  42. Evin G, Weidemann A (2002) Biogenesis and metabolism of Alzheimer’s disease Abeta amyloid peptides. Peptides 23(7):1285–1297. doi:10.1016/S0196-9781(02)00063-3

    CAS  Google Scholar 

  43. McGeer PL, McGeer EG (2001) Inflammation, autotoxicity and Alzheimer disease. Neurobiol Aging 22(6):799–809. doi:10.1016/S0197-4580(01)00289-5

    CAS  Google Scholar 

  44. Zhao Y, Truhlar DG (2008) The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other function. Theor Chem Acc 120(1–3):215–241. doi:10.1007/s00214-007-0310-x

    CAS  Google Scholar 

  45. Xu X, Goddard WA (2004) From the cover: The X3LYP extended density functional for accurate descriptions of nonbond interactions, spin states, and thermochemical properties. Proc Nat Acad Sci U S A 101(9):2673–2677. doi:10.1073/pnas.0308730100

    CAS  Google Scholar 

  46. Durrington P (2003) Dyslipidaemia. Lancet 362(9385):717–731. doi:10.1016/S0140-6736(03)14234–1

    CAS  Google Scholar 

  47. Istvan ES, Deisenhofer J (2001) Structural mechanism for statin inhibition of HMG-CoA reductase. Science 292(5519):1160–1164. doi:10.1126/science.1059344

    CAS  Google Scholar 

  48. Utkov HE, Price AM, Cafiero M (2011) MP2, density functional theory, and semi-empirical calculations of the interaction energies between a series of statin-drug-like molecules and the HMG-CoA reductase active site. Comp Theor Chem 967(1):171–178. doi:10.1016/j.comptc.2011.04.013

    CAS  Google Scholar 

  49. Leopoldini M, Malaj N, Toscano M, Sindona G, Russo N (2010) On the inhibitor effects of bergamot juice flavonoids binding to the 3-hydroxy-3-methylglutaryl-CoA Reductase (HMGR) Enzyme. J Agr Food Chem 58(19):10768–10773. doi:10.1021/jf102576j

    CAS  Google Scholar 

  50. Hebert PR, Gaziano JM, Chan KS, Hennekens CH (1997) Cholesterol lowering with statin drugs, risk of stroke and total mortality. An overview of randomized trials. J Am Med Assoc 278(4):313–321. doi:10.1001/jama.1997.03550040069040

    CAS  Google Scholar 

  51. Leopoldini M, Marino T, Russo N, Toscano M (2009) Potential energy surfaces for reaction catalyzed by metalloenzymes from quantum chemical computations. In: Russo N, Antonchenko VY, Kryachko ES (eds) Self-organization of molecular systems: from molecules and clusters to nanotubes and proteins, NATO Science for Peace and Security Series A: Chemistry and Biology. Springer, New York, p 275–313. doi:10.1007/978-90-481-2590-6_13

    Google Scholar 

  52. Antony J, Gresh N, Olsen L, Hemmingsen L, Schofield CJ, Bauer R (2002) Binding of d- and l-captopril inhibitors to metallo-β-lactamase studied by polarizable molecular mechanics and quantum mechanics. J Comput Chem 23(13):1281–1296. doi:10.1002/jcc.10111

    CAS  Google Scholar 

  53. Antony J, Piquemal J-P, Gresh N (2005) Complexes of thiomandelate and captopril mercaptocarboxylate inhibitors to metallo-beta-lactamase by polarizable molecular mechanics. Validation on model binding sites by quantum chemistry. J Comput Chem 26(11):1131–1147. doi:10.1002/jcc.20245

    CAS  Google Scholar 

  54. Chen X, Gao F, Zhou Z-X, Yang W-Y, Guo L-T, Ji L-N (2010) Effect of ancillary ligands on the topoisomerases II and transcription inhibition activity of polypyridyl ruthenium(II) complexes. J Inorg Biochem 104(5):576–582. doi:10.1016/j.jinorgbio.2010.01.010

    CAS  Google Scholar 

  55. Casini A, Edafe F, Erlandsson M, Gonsalvi L, Ciancetta A, Re N, Ienco A, Messori L, Peruzzini M, Dyson PJ (2010) Rationalization of the inhibition activity of structurally related organometallic compounds against the drug target cathepsin B by DFT. Dalton Trans 39(23):5556–5563. doi:10.1039/c003218b

    CAS  Google Scholar 

  56. Shokhen M, Khazanov N, Albeck A (2011) The mechanism of papain inhibition by peptidyl aldehydes. Proteins 79(3):975–985. doi:10.1002/prot.22939

    CAS  Google Scholar 

  57. Greig NH, Sambamurti K, Yu Q, Brossi A, Bruinsma GB, Lahiri DK (2005) An overview of phenserine tartrate, a novel acetylcholinesterase inhibitor for the treatment of Alzheimer’s disease. Curr Alzheimer Res 2(3):281–290. doi:10.2174/1567205054367829

    CAS  Google Scholar 

  58. Lahiri DK, Farlow MR, Hintz N, Utsuki T, Greig NH (2000) Cholinesterase inhibitors, beta-amyloid precursor protein and amyloid beta-peptides in Alzheimer’s disease. Acta Neurol Scand 102(s176):60–67. doi:10.1034/j.1600-0404.2000.00309.x

    Google Scholar 

  59. Leader H, Wolfe AD, Chiang PK, Gordon RK (2002) Pyridophens: binary pyridostigmine-aprophen prodrugs with differential inhibition of acetylcholinesterase, butyrylcholinesterase, and muscarinic receptors. J Med Chem 45(4):902–910. doi:10.1021/jm010196t

    CAS  Google Scholar 

  60. Yu Q, Holloway HW, Flippen-Anderson JL, Hoffman B, Brossi A, Greig NH (2001) Methyl analogues of the experimental Alzheimer drug phenserine: synthesis and structure/activity relationships for acetyl- and butyrylcholinesterase inhibitory action. J Med Chem 44(24):4062–4071. doi:10.1021/jm010080x

    CAS  Google Scholar 

  61. Correa-Basurto J, Flores-Sandoval C, Marín-Cruz J, Rojo-Domínguez A, Espinoza-Fonseca LM, Trujillo-Ferrara JG (2007) Docking and quantum mechanic studies on cholinesterases and their inhibitors. Eur J Med Chem 42(1):10–19. doi:10.1016/j.ejmech.2006.08.015

    CAS  Google Scholar 

  62. Zhang Y, Kua J, McCammon JA (2002) Role of the catalytic triad and oxyanion hole in acetylcholinesterase catalysis: an ab initio QM/MM study. J Am Chem Soc 124(35):10572–10577. doi:10.1021/ja020243m

    CAS  Google Scholar 

  63. Nascimento ECM, Martins JBL, dos Santos ML, Gargano R (2008) Theoretical study of classical acetylcholinesterase inhibitors. Chem Phys Lett 458(4–6):285–289. doi:10.1016/j.cplett.2008.05.006

    CAS  Google Scholar 

  64. Khan MAS, Lo R, Bandyopadhyay TT, Ganguly BB (2011) Probing the reactivation process of sarin-inhibited acetylcholinesterase with α-nucleophiles: hydroxylamine anion is predicted to be a better antidote with DFT calculations. J Mol Graph Model 29(8):1039–1046. doi:10.1016/j.jmgm.2011.04.009

    CAS  Google Scholar 

  65. Khan MAS, Ganguly B (2012) Assessing the reactivation efficacy of hydroxylamine anion towards VX-inhibited AChE: A computational study. J Mol Model 18(5):1801–1808. doi:10.1007/s00894-011-1209-y

    CAS  Google Scholar 

  66. Shi Y-B, Fu L, Hasebe T, Ishizuya-Oka A (2007) Regulation of extracellular matrix remodeling and cell fate determination by matrix metalloproteinase stromelysin-3 during thyroid hormone-dependent post-embryonic development. Pharmacol Therapeut 116(3):391–400. doi:10.1016/j.pharmthera.2007.07.005

    CAS  Google Scholar 

  67. Smith MF, Ricke WA, Bakke LJ, Dow MPD, Smith GW (2002) Ovarian tissue remodeling: role of matrix metalloproteinases and their inhibitors. Mol Cell Endocrinol 191(1):45–56. doi:10.1016/S0303-7207(02)00054-0

    CAS  Google Scholar 

  68. Kawasaki Y, Xu Z-Z, Wang X, Park JY, Zhuang Z-Y, Tan P-H, Gao Y-J, Roy K, Corfas G, Lo EH, Ji R-R (2008) Distinct roles of matrix metalloproteases in the early- and late-phase development of neuropathic pain. Nat Med 14(3):331–336. doi:10.1038/nm1723

    CAS  Google Scholar 

  69. Egeblad M, Werb Z (2002) New functions for the matrix metalloproteinases in cancer progression. Nat Rev Cancer 2(3):161–174. doi:10.1038/nrc745

    CAS  Google Scholar 

  70. Noël A, Jost M, Maquoi E (2008) Matrix metalloproteinases at cancer tumor–host interface. Semin Cell Dev Biol 19(1):52–60. doi:10.1016/j.semcdb.2007.05.011

    Google Scholar 

  71. Tao P, Fisher JF, Shi Q, Mobashery S, Schlegel HB (2010) Matrix metalloproteinase 2 (MMP2) inhibition: DFT and QM/MM studies of the deprotonation-initialized ring-opening reaction of the sulfoxide analogue of SB-3CT. J Phys Chem B 114(2):1030–1037. doi:10.1021/jp909327y

    CAS  Google Scholar 

  72. Tao P, Fisher JF, Shi Q, Vreven T, Mobashery S, Schlegel HB (2009) Matrix metalloproteinase 2 inhibition: combined quantum mechanics and molecular mechanics studies of the inhibition mechanism of (4-phenoxyphenylsulfonyl)methylthiirane and its oxirane analogue. Biochemistry 48(41):9839–9847. doi:10.1021/bi901118r

    CAS  Google Scholar 

  73. Augé F, Hornebeck W, Decarme M, Laronze J-Y (2003) Improved gelatinase a selectivity by novel zinc binding groups containing galardin derivatives. Bioorg Med Chem Lett 13(10):1783–1786. doi:10.1016/S0960-894X(03)00214-2

    Google Scholar 

  74. Rouffet M, Denhez C, Bourguet E, Bohr F, Guillaume D (2009) In silico study of MMP inhibition. Org Biomol Chem 7(18):3817–3825. doi:10.1039/b910543c

    CAS  Google Scholar 

  75. Li D, Zheng Q, Fang X, Ji H, Yang J, Zhang H (2008) Theoretical study on potency and selectivity of novel non-peptide inhibitors of matrix metalloproteinases MMP-1 and MMP-3. Polymer 49(15):3346–3351. doi:10.1016/j.polymer.2008.05.026

    CAS  Google Scholar 

  76. da Silva SL, Calgarotto AK, Maso V, Damico DC, Baldasso P, Veber CL, Villar JAFP, Oliveira ARM, Comar M, Oliveira KMT, Marangoni S (2009) Molecular modeling and inhibition of phospholipase A2 by polyhydroxy phenolic compounds. Eur J Med Chem 44(1):312–321. doi:10.1016/j.ejmech.2008.02.043

    CAS  Google Scholar 

  77. Šramko M, Garaj V, Remko M (2008) Thermodynamics of binding of angiotensin-converting enzyme inhibitors to enzyme active site model, J Mol Struct—Theochem 869(1–3):19–28. 10.1016/j.theochem.2008.08.018

    Google Scholar 

  78. Opie LH, Gersh BJ (2009) Drugs for the Heart. WB Saunders, Philadelphia

    Google Scholar 

  79. Lorthiois E, Bernardelli P, Vergne F, Oliveira C, Mafroud A-K, Proust E, Heuze L, Moreau F, Idrissi M, Tertre A, Bertin B, Coupe M, Wrigglesworth R, Descours A, Soulard P, Berna P (2004) Spiroquinazolinones as novel, potent, and selective PDE7 inhibitors. Part 1. Bioorg Med Chem Lett 14(18):4623–4626. doi:10.1016/j.bmcl.2004.07.011

    CAS  Google Scholar 

  80. Bernardelli P, Lorthiois E, Vergne F, Oliveira C, Mafroud A-K, Proust E, Pham N, Ducrot P, Moreau F, Idrissi M, Tertre A, Bertin B, Coupe M, Chevalier E, Descours A, Berlioz-Seux F, Berna P, Li M (2004) Spiroquinazolinones as novel, potent, and selective PDE7 inhibitors. Part 2: Optimization of 5,8-disubstituted derivatives. Bioorg Med Chem Lett 14(18):4627–4631. doi:10.1016/j.bmcl.2004.07.010

    CAS  Google Scholar 

  81. Daga PR, Doerksen RJ (2008) Stereoelectronic properties of spiroquinazolinones in differential PDE7 inhibitory activity. J Comput Chem 29(12):1945–1954. doi:10.1002/jcc.20960

    CAS  Google Scholar 

  82. Horenstein BA, Schramm VL (1993) Correlation of the molecular electrostatic potential surface of an enzymatic transition state with novel transition-state inhibitors. Biochemistry 32(38):9917–9925. doi:10.1021/bi00089a007

    CAS  Google Scholar 

  83. Braunheim BB, Miles RW, Schramm VL, Schwartz SD (1999) Prediction of inhibitor binding free energies by quantum neural networks. Nucleoside analogues binding to trypanosomal nucleoside hydrolase. Biochemistry 38(49):16076–16083. doi:10.1021/bi990830t

    CAS  Google Scholar 

  84. Ehrlich JI, Schramm VL (1994) Electrostatic potential surface analysis of the transition state for AMP nucleosidase and for formycin 5’-phosphate, a transition-state inhibitor. Biochemistry 33(30):8890–8896. doi:10.1021/bi00196a005

    CAS  Google Scholar 

  85. Debnath AK (2013) Rational design of HIV-1 entry inhibitors. In: Kortagere S (ed) Methods in molecular biology. Springer, Clifton, p 185–204. doi:10.1007/978-1-62703-342-8_13

    Google Scholar 

  86. Świderek K, Martí S., Moliner V (2012) Theoretical studies of HIV-1 reverse transcriptase inhibition. Phys Chem Chem Phys 14(36):12614–12624. doi:10.1039/c2cp40953d

    Google Scholar 

  87. Liang YH, Chen FE (2007) ONIOM DFT/PM3 calculations on the interaction between dapivirine and HIV-1 reverse transcriptase, a theoretical study. Drug Discov Ther 1(1):57–60.

    CAS  Google Scholar 

  88. Garrec J, Sautet P, Fleurat-Lessard P (2011) Understanding the HIV-1 protease reactivity with DFT: what do we gain from recent functionals?. J Phys Chem B 115(26):8545–8558. doi:10.1021/jp200565w

    CAS  Google Scholar 

  89. Garrec J, Cascella M, Rothlisberger U, Fleurat-Lessard P (2010) Low inhibiting power of N…CO based peptidomimetic compounds against HIV-1 protease: insights from a QM/MM study. J Chem Theor Comput 6(4):1369–1379. doi:10.1021/ct9004728

    CAS  Google Scholar 

  90. Gautier A, Pitrat D, Hasserodt J (2006) An unusual functional group interaction and its potential to reproduce steric and electrostatic features of the transition states of peptidolysis. Bioorg Med Chem 14(11):3835–3847. doi:10.1016/j.bmc.2006.01.031

    CAS  Google Scholar 

  91. Waibel M, Hasserodt J (2008) Diversity-oriented synthesis of a drug-like system displaying the distinctive N → C=O interaction. J Org Chem 73(16):6119–6126. doi:10.1021/jo800719j

    CAS  Google Scholar 

  92. Waibel M, Pitrat D, Hasserodt J (2009) On the inhibition of HIV-1 protease by hydrazino-ureas displaying the N → C=O interaction. Bioorg Med Chem 17(10):3671–3679. doi:10.1016/j.bmc.2009.03.059

    CAS  Google Scholar 

  93. Park C, Koh JS, Son YC, Choi H, Lee CS, Choy N, Moon KY, Jung WH, Kim SC, Yoon H (1995) Rational design of irreversible, pseudo-C2-symmetric hiv-1 protease inhibitors. Bioorg Med Chem Lett 5(16):1843–1848. doi:10.1016/0960-894X(95)00306-E

    CAS  Google Scholar 

  94. Lee CS, Choy N, Park C, Choi H, Son YC, Kim S, Ok JH, Yoon H, Kim SC (1996) Design, synthesis, and characterization of dipeptide isostere containing cis-epoxide for the irreversible inactivation of HIV protease. Bioorg Med Chem Lett 6(6):589–594. doi:10.1016/0960-894X(96)00087-X

    CAS  Google Scholar 

  95. Choy N, Choi H, Jung WH, Kim CR, Yoon H, Kim SC, Lee TG, Koh JS (1997) Synthesis of irreversible HIV-1 protease inhibitors containing sulfonamide and sulfone as amide bond isosteres. Bioorg Med Chem Lett 7(20):2635–2638. doi:10.1016/S0960-894X(97)10054-3

    CAS  Google Scholar 

  96. Kóňa J (2008) Theoretical study on the mechanism of a ring-opening reaction of oxirane by the active-site aspartic dyad of HIV-1 protease. Org Biomol Chem 6(2):359–365. doi:10.1039/b715828a

    Google Scholar 

  97. Pommier Y, Johnson AA, Marchand C (2005) Integrase inhibitors to treat HIV/AIDS. Nat Rev Drug Discov 4(3):236–248. doi:10.1038/nrd1660

    CAS  Google Scholar 

  98. Ingale KB, Bhatia MS (2011) HIV-1 integrase inhibitors: a review of their chemical development. Antivir Chem Chemoth 22(3):95–105. doi:10.3851/IMP1740

    CAS  Google Scholar 

  99. Messiaen P, Wensing AMJ, Fun A, Nijhuis M, Brusselaers N, Vandekerckhove L (2013) Clinical use of HIV integrase inhibitors: a systematic review and meta-analysis. PloS One 8(1):e52562. doi:10.1371/journal.pone.0052562

    CAS  Google Scholar 

  100. Liao C, Nicklaus MC (2010) Tautomerism and magnesium chelation of HIV-1 integrase inhibitors: a theoretical study. Chem Med Chem 5(7):1053–1066. doi:10.1002/cmdc.201000039

    CAS  Google Scholar 

  101. Agrawal A, DeSoto J, Fullagar JL, Maddali K, Rostami S, Richman DD, Pommier Y, Cohen SM (2012) Probing chelation motifs in HIV integrase inhibitors. Proc Nat Acad Sci U S A 109(7):2251–2256. doi:10.1073/pnas.1112389109

    CAS  Google Scholar 

  102. Thalheim T, Vollmer A, Ebert R-U, Kühne R, Schüürmann G (2010) Tautomer identification and tautomer structure generation based on the InChI code. J Chem Inf Model 50(7):1223–1232. doi:10.1021/ci1001179

    CAS  Google Scholar 

  103. Nunthaboot N, Pianwanit S, Parasuk V, Kokpol S, Wolschann P (2007) Theoretical study on the HIV-1 integrase inhibitor 1-(5-chloroindol-3-yl)-3-hydroxy-3-(2H-tetrazol-5-yl)-propenone (5CITEP). J Mol Struct 844–845:208–214. doi:10.1016/j.molstruc.2007.06.026

    Google Scholar 

  104. Alves CN, Martí S, Castillo R, Andrés J, Moliner V, Tuñón I, Silla E (2007) Calculation of binding energy using BLYP/MM for the HIV-1 integrase complexed with the S-1360 and two analogues. Bioorgan Med Chem 15(11):3818–3824. doi:10.1016/j.bmc.2007.03.027

    CAS  Google Scholar 

  105. Lespade L, Bercion S (2010) Theoretical study of the mechanism of inhibition of xanthine oxydase by flavonoids and gallic acid derivatives. J Phys Chem B 114(2):921–928. doi:10.1021/jp9041809

    CAS  Google Scholar 

  106. Lin C-M, Chen C-S, Chen C-T, Liang Y-C, Lin J-K (2002) Molecular modeling of flavonoids that inhibits xanthine oxidase. Biochem Biophys Res Commun 294(1):167–172. doi:10.1016/S0006-291X(02)00442-4

    CAS  Google Scholar 

  107. Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information,. J Chem Inf Model 35(6):1039–1045. doi:10.1021/ci00028a014

    CAS  Google Scholar 

  108. Fogliani B, Raharivelomanana P, Bianchini J-P, Bouraïma-Madjèbi S, Hnawia E (2005) Bioactive ellagitannins from Cunonia macrophylla, an endemic Cunoniaceae from New Caledonia. Phytochemistry 66(2):241–247. doi:10.1016/j.phytochem.2004.11.016

    CAS  Google Scholar 

  109. Fenton JW, Ofosu FA, Moon DG, Maraganore JM (1991) Thrombin structure and function: why thrombin is the primary target for antithrombotics. Blood Coagul Fibrin 2(1):69–75.

    CAS  Google Scholar 

  110. Alzate-Morales JH, Contreras R, Soriano A, Tuñon I, Silla E (2007) A computational study of the protein-ligand interactions in CDK2 inhibitors: using quantum mechanics/molecular mechanics interaction energy as a predictor of the biological activity. Biophys J 92:430–439. doi:10.1529/biophysj.106.091512

    CAS  Google Scholar 

  111. De Oliveira EB, Humeau C, Maia ER, Chebil L, Ronat E, Monard G, Ruiz-Lopez MF, Ghoul M, Engasser J-M (2010) An approach based on Density Functional Theory (DFT) calculations to assess the candida antarctica lipase B selectivity in rutin, isoquercitrin and quercetin acetylation. J Mol Catal B—Enzym 66(3–4):325–331. doi:10.1016/j.molcatb.2010.06.009

    CAS  Google Scholar 

  112. Benini S, Rypniewski WR, Wilson KS, Ciurli S, Mangani S (2001) Structure-based rationalization of urease inhibition by phosphate: novel insights into the enzyme mechanism. J Biol Inorg Chem 6(8):778–790. doi:10.1007/s007750100254

    CAS  Google Scholar 

  113. Karplus PA, Pearson MA, Hausinger RP (1997) 70 Years of crystalline urease: what have we learned?. Acc Chem Res 30(8):330–337. doi:10.1021/ar960022j

    CAS  Google Scholar 

  114. Leopoldini M, Marino T, Russo N, Toscano M (2008) On the binding mode of urease active site inhibitors: a density functional study. Int J Quant Chem 108(11):2023–2029. doi:10.1002/qua.21758

    CAS  Google Scholar 

  115. Wei D, Lei B, Tang M, Zhan C-G (2012) Fundamental reaction pathway and free energy profile for inhibition of proteasome by epoxomicin. J Am Chem Soc 134(25):10436–10450. doi:10.1021/ja3006463

    CAS  Google Scholar 

  116. Weigend F (2008) Hartree–Fock exchange fitting basis sets for H to Rn. J Comput Chem 29(2):167–175. doi:10.1002/jcc.20702

    CAS  Google Scholar 

  117. Neese F, Wennmohs F, Hansen A, Becker U (2009) Efficient, approximate and parallel Hartree–Fock and hybrid DFT calculations. A “chain-of-spheres” algorithm for the Hartree–Fock exchange. Chem Phys 356(1–3):98–109. 10.1016/j.chemphys.2008.10.036

    CAS  Google Scholar 

  118. Ferrer S, Ruiz-Pernía J, Martí S et al (2011) Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives. Adv Protein Chem Struct Biol 85:81–142. doi:10.1016/B978-0-12-386485-7.00003-X

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmans’ka str. 5, 02660, Kyiv, Ukraine

    Alexander B. Rozhenko

Authors
  1. Alexander B. Rozhenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander B. Rozhenko .

Editor information

Editors and Affiliations

  1. Department of Molecular Biophysics, Laboratory of Computational Structural Biology, Kiev, Ukraine

    Leonid Gorb

  2. Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute, National Academy of Sciences of Ukraine, Odessa, Ukraine

    Victor Kuz'min

  3. Department of Chemical Biology and Medicinal Chemistry, University of North Carolina, Laboratory for Molecular Modeling, Chapel Hill, North Carolina, USA

    Eugene Muratov

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Rozhenko, A. (2014). Density Functional Theory Calculations of Enzyme–Inhibitor Interactions in Medicinal Chemistry and Drug Design. In: Gorb, L., Kuz'min, V., Muratov, E. (eds) Application of Computational Techniques in Pharmacy and Medicine. Challenges and Advances in Computational Chemistry and Physics, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9257-8_7

Download citation

Publish with us

Policies and ethics

Search

Navigation