Skip to main content
SpringerLink
Log in

Van der Waals Potential in Protein Complexes

  • Protocol
  • First Online:
Book cover Docking Screens for Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2053))

Abstract

Van der Waals forces are determinants of the formation of protein-ligand complexes. Physical models based on the Lennard-Jones potential can estimate van der Waals interactions with considerable accuracy and with a computational complexity that allows its application to molecular docking simulations and virtual screening of large databases of small organic molecules. Several empirical scoring functions used to evaluate protein-ligand interactions approximate van der Waals interactions with the Lennard-Jones potential. In this chapter, we present the main concepts necessary to understand van der Waals interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. We describe the Lennard-Jones potential and its application to calculate potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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

References

  1. Wang C, Greene D, Xiao L, Qi R, Luo R (2018) Recent developments and applications of the MMPBSA method. Front Mol Biosci 4:87

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Cappel D, Sherman W, Beuming T (2017) Calculating water thermodynamics in the binding site of proteins—applications of WaterMap to drug discovery. Curr Top Med Chem 17:2586–2598

    Article  CAS  PubMed  Google Scholar 

  3. Bernetti M, Cavalli A, Mollica L (2017) Protein-ligand (un)binding kinetics as a new paradigm for drug discovery at the crossroad between experiments and modelling. Medchemcomm 8:534–550

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jaegle M, Wong EL, Tauber C, Nawrotzky E, Arkona C, Rademann J (2017) Protein-templated fragment ligations-from molecular recognition to drug discovery. Angew Chem Int Ed Engl 56:7358–7378

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL et al (2017) Overview of the SAMPL5 host-guest challenge: are we doing better? J Comput Aided Mol Des 31:1–19

    Article  CAS  PubMed  Google Scholar 

  6. de Azevedo WF Jr (2010) MolDock applied to structure-based virtual screening. Curr Drug Targets 11:327–334

    Article  PubMed  Google Scholar 

  7. Chakravarty K, Dalal DC (2018) Mathematical modelling of liposomal drug release to tumour. Math Biosci 306:82–96

    Article  CAS  PubMed  Google Scholar 

  8. Qi R, Luo R (2019) Robustness and efficiency of poisson-boltzmann modeling on graphics processing units. J Chem Inf Model 59:409–420

    Article  CAS  PubMed  Google Scholar 

  9. He X, Man VH, Ji B, Xie XQ, Wang J (2019) Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3. J Comput Aided Mol Des 33:105–117

    Article  CAS  PubMed  Google Scholar 

  10. Li A, Gilson MK (2018) Protein-ligand binding enthalpies from near-millisecond simulations: analysis of a preorganization paradox. J Chem Phys 149:072311

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Miao Y, Huang YM, Walker RC, McCammon JA, Chang CA (2018) Ligand binding pathways and conformational transitions of the HIV protease. Biochemistry 57:1533–1541

    Article  CAS  PubMed  Google Scholar 

  12. Hoffer L, Muller C, Roche P, Morelli X (2018) Chemistry-driven Hit-to-lead optimization guided by structure-based approaches. Mol Inform 37:e1800059

    Article  PubMed  CAS  Google Scholar 

  13. Yadav BS, Tripathi V (2018) Recent advances in the system biology-based target identification and drug discovery. Curr Top Med Chem 18:1737–1744

    Article  CAS  PubMed  Google Scholar 

  14. Sotriffer C (2018) Docking of covalent ligands: challenges and approaches. Mol Inform 37:e1800062

    Article  PubMed  CAS  Google Scholar 

  15. Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694–2718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Roberts NA, Martin JA, Kinchington D, Broadhurst AV, Craig JC, Duncan IB et al (1990) Rational design of peptide-based HIV proteinase inhibitors. Science 248:358–361

    Article  CAS  PubMed  Google Scholar 

  17. Erickson J, Neidhart DJ, VanDrie J, Kempf DJ, Wang XC, Norbeck DW et al (1990) Design, activity, and 2.8 A crystal structure of a C2 symmetric inhibitor complexed to HIV-1 protease. Science 249:527–533

    Article  CAS  PubMed  Google Scholar 

  18. Dorsey BD, Levin RB, McDaniel SL, Vacca JP, Guare JP, Darke PL et al (1994) L-735,524: the design of a potent and orally bioavailable HIV protease inhibitor. J Med Chem 37:3443–3451

    Article  CAS  PubMed  Google Scholar 

  19. Vilar S, Sobarzo-Sanchez E, Santana L, Uriarte E (2017) Molecular docking and drug discovery in β-adrenergic receptors. Curr Med Chem 24:4340–4359

    Article  CAS  PubMed  Google Scholar 

  20. Xia X (2017) Bioinformatics and drug discovery. Curr Top Med Chem 17:1709–1726

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    Article  CAS  PubMed  Google Scholar 

  22. DesJarlais RL, Dixon JS (1994) A shape- and chemistry-based docking method and its use in the design of HIV-1 protease inhibitors. J Comput Aided Mol Des 8:231–242

    Article  CAS  PubMed  Google Scholar 

  23. Lunney EA, Hagen SE, Domagala JM, Humblet C, Kosinski J, Tait BD et al (1994) A novel nonpeptide HIV-1 protease inhibitor: elucidation of the binding mode and its application in the design of related analogs. J Med Chem 37:2664–2677

    Article  CAS  PubMed  Google Scholar 

  24. Vaillancourt M, Cohen E, Sauvé G (1995) Characterization of dynamic state inhibitors of HIV-1 protease. J Enzyme Inhib 9:217–233

    Article  CAS  PubMed  Google Scholar 

  25. Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, Fogel LJ et al (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol 2:317–324

    Article  CAS  PubMed  Google Scholar 

  26. King BL, Vajda S, DeLisi C (1996) Empirical free energy as a target function in docking and design: application to HIV-1 protease inhibitors. FEBS Lett 384:87–91

    Article  CAS  PubMed  Google Scholar 

  27. Wang S, Milne GW, Yan X, Posey IJ, Nicklaus MC, Graham L et al (1996) Discovery of novel, non-peptide HIV-1 protease inhibitors by pharmacophore searching. J Med Chem 39:2047–2054

    Article  CAS  PubMed  Google Scholar 

  28. Adeniyi AA, Soliman MES (2017) Implementing QM in docking calculations: is it a waste of computational time? Drug Discov Today 22:1216–1223

    Article  CAS  PubMed  Google Scholar 

  29. Crespo A, Rodriguez-Granillo A, Lim VT (2017) Quantum-mechanics methodologies in drug discovery: applications of docking and scoring in lead optimization. Curr Top Med Chem 17:2663–2680

    Article  CAS  PubMed  Google Scholar 

  30. Yilmazer ND, Korth M (2016) Recent progress in treating protein-ligand interactions with quantum-mechanical methods. Int J Mol Sci 17:742

    Article  PubMed Central  CAS  Google Scholar 

  31. Cavasotto CN, Adler NS, Aucar MG (2018) Quantum chemical approaches in structure-based virtual screening and lead optimization. Front Chem 29(6):188

    Article  CAS  Google Scholar 

  32. Hitzenberger M, Schuster D, Hofer TS (2017) The binding mode of the sonic hedgehog inhibitor robotnikinin, a combined docking and QM/MM MD study. Front Chem 5:76

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Salmas RE, Is YS, Durdagi S, Stein M, Yurtsever M (2018) A QM protein-ligand investigation of antipsychotic drugs with the dopamine D2 receptor (D2R). J Biomol Struct Dyn 36:2668–2677

    Article  CAS  Google Scholar 

  34. Phipps MJ, Fox T, Tautermann CS, Skylaris CK (2017) Intuitive density functional theory-based energy decomposition analysis for protein-ligand interactions. J Chem Theory Comput 13:1837–1850

    Article  CAS  PubMed  Google Scholar 

  35. Hylsová M, Carbain B, Fanfrlík J, Musilová L, Haldar S, Köprülüoğlu C et al (2017) Explicit treatment of active-site waters enhances quantum mechanical/implicit solvent scoring: Inhibition of CDK2 by new pyrazolo[1,5-a]pyrimidines. Eur J Med Chem 126:1118–1128

    Article  PubMed  CAS  Google Scholar 

  36. Pecina A, Meier R, Fanfrlík J, Lepšík M, Řezáč J, Hobza P et al (2016) The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation. Chem Commun (Camb) 52:3312–3315

    Article  CAS  Google Scholar 

  37. Yang Z, Liu Y, Chen Z, Xu Z, Shi J, Chen K et al (2015) A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions. J Mol Model 21:138

    Article  PubMed  CAS  Google Scholar 

  38. Lennard-Jones JE (1931) Cohesion. Proc Phys Soc 43:461–482

    Article  CAS  Google Scholar 

  39. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  40. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28:1145–1152

    Article  CAS  PubMed  Google Scholar 

  42. Fahmy A, Wagner G (2002) TreeDock: a tool for protein docking based on minimizing van der Waals energies. J Am Chem Soc 124:1241–1250

    Article  CAS  PubMed  Google Scholar 

  43. Demerdash ON, Buyan A, Mitchell JC (2010) ReplicOpter: a replicate optimizer for flexible docking. Proteins 78:3156–3165

    Article  CAS  PubMed  Google Scholar 

  44. Buckingham A (1938) The classical equation of state of gaseous helium, neon and argon. Proc R Soc London Ser A 168:264–283

    Article  CAS  Google Scholar 

  45. Teik-Cheng L (2007) Alternative scaling factor between Lennard-Jones and Exponential-6 potential energy functions. Mol Simul 33:1029–1032

    Article  CAS  Google Scholar 

  46. Xantheas SS, Werhahn JC (2014) Universal scaling of potential energy functions describing intermolecular interactions. I. Foundations and scalable forms of new generalized Mie, Lennard-Jones, Morse, and Buckingham exponential-6 potentials. J Chem Phys 141:064117

    Article  PubMed  CAS  Google Scholar 

  47. Bazgier V, Berka K, Otyepka M, Banáš P (2016) Exponential repulsion improves structural predictability of molecular docking. J Comput Chem 37:2485–2494

    Article  CAS  PubMed  Google Scholar 

  48. Volkart PA, Bitencourt-Ferreira G, art AA, de Azevedo WF (2019) Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr Drug Targets 20(7):716–726. https://doi.org/10.2174/1389450120666181204165344

    Article  CAS  PubMed  Google Scholar 

  49. de Azevedo WF Jr (2016) Opinion paper: targeting multiple cyclin-dependent kinases (CDKs): A new strategy for molecular docking studies. Curr Drug Targets 17:2

    Article  PubMed  CAS  Google Scholar 

  50. Perez PC, Caceres RA, Canduri F, de Azevedo WF Jr (2009) Molecular modeling and dynamics simulation of human cyclin-dependent kinase 3 complexed with inhibitors. Comput Biol Med 39:130–140

    Article  CAS  PubMed  Google Scholar 

  51. Canduri F, Perez PC, Caceres RA, de Azevedo WF Jr (2008) CDK9 a potential target for drug development. Med Chem 4:210–218

    Article  CAS  PubMed  Google Scholar 

  52. Krystof V, Cankar P, Frysová I, Slouka J, Kontopidis G, Dzubák P et al (2006) 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. J Med Chem 49:6500–6509

    Article  CAS  PubMed  Google Scholar 

  53. Leopoldino AM, Canduri F, Cabral H, Junqueira M, de Marqui AB, Apponi LH et al (2006) Expression, purification, and circular dichroism analysis of human CDK9. Protein Expr Purif 47:614–620

    Article  CAS  PubMed  Google Scholar 

  54. Canduri F, de Azevedo WF Jr (2005) Structural basis for interaction of inhibitors with cyclin-dependent kinase 2. Curr Comput Aided Drug Des 1:53–64

    Article  CAS  Google Scholar 

  55. Canduri F, Uchoa HB, de Azevedo WF Jr (2004) Molecular models of cyclin-dependent kinase 1 complexed with inhibitors. Biochem Biophys Res Commun 324:661–666

    Article  CAS  PubMed  Google Scholar 

  56. de Azevedo WF Jr, Gaspar RT, Canduri F, Camera JC Jr, da Silveira NJ (2002) Molecular model of cyclin-dependent kinase 5 complexed with roscovitine. Biochem Biophys Res Commun 297:1154–1158

    Article  CAS  Google Scholar 

  57. de Azevedo WF Jr, Canduri F, da Silveira NJ (2002) Structural basis for inhibition of cyclin-dependent kinase 9 by flavopiridol. Biochem Biophys Res Commun 293:566–571

    Article  PubMed  Google Scholar 

  58. de Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH (1997) Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human CDK2 complexed with roscovitine. Eur J Biochem 243:518–526

    Article  PubMed  Google Scholar 

  59. de Azevedo WF Jr, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH (1996) Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci U S A 93:2735–2740

    Article  PubMed  PubMed Central  Google Scholar 

  60. Pang X, Liu Z, Zhai G (2014) Advances in non-peptidomimetic HIV protease inhibitors. Curr Med Chem 21:1997–2011

    Article  CAS  PubMed  Google Scholar 

  61. Calugi C, Guarna A, Trabocchi A (2013) Heterocyclic HIV-protease inhibitors. Curr Med Chem 20:3693–3710

    Article  CAS  PubMed  Google Scholar 

  62. Smith JM (1970) Natural selection and the concept of a protein space. Nature 225:563–564

    Article  CAS  PubMed  Google Scholar 

  63. Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16:3–50

    Article  CAS  PubMed  Google Scholar 

  64. Parish T, Stoker NG (2002) The common aromatic amino acid biosynthesis pathway is essential in Mycobacterium tuberculosis. Microbiology 148:3069–3077

    Article  CAS  PubMed  Google Scholar 

  65. Pereira JH, Canduri F, de Oliveira JS, da Silveira NJ, Basso LA, Palma MS et al (2003) Structural bioinformatics study of EPSP synthase from Mycobacterium tuberculosis. Biochem Biophys Res Commun 312:608–614

    Article  CAS  PubMed  Google Scholar 

  66. Arcuri HA, Canduri F, Pereira JH, da Silveira NJ, Camera JC Jr, de Oliveira JS et al (2004) Molecular models for shikimate pathway enzymes of Xylella fastidiosa. Biochem Biophys Res Commun 320:979–991

    Article  CAS  PubMed  Google Scholar 

  67. Dias MV, Ely F, Canduri F, Pereira JH, Frazzon J, Basso LA et al (2004) Crystallization and preliminary X-ray crystallographic analysis of chorismate synthase from Mycobacterium tuberculosis. Acta Crystallogr D Biol Crystallogr 60:2003–2005

    Article  PubMed  CAS  Google Scholar 

  68. Uchôa HB, Jorge GE, Freitas Da Silveira NJ, Camera JC Jr, Canduri F, De Azevedo WF Jr (2004) Parmodel: a web server for automated comparative modeling of proteins. Biochem Biophys Res Commun 325:1481–1486

    Article  CAS  PubMed  Google Scholar 

  69. Pereira JH, de Oliveira JS, Canduri F, Dias MV, Palma MS, Basso LA et al (2004) Structure of shikimate kinase from Mycobacterium tuberculosis reveals the binding of shikimic acid. Acta Crystallogr D Biol Crystallogr 60:2310–2319

    Article  PubMed  CAS  Google Scholar 

  70. Silveira NJ, Uchôa HB, Pereira JH, Canduri F, Basso LA, Palma MS et al (2005) Molecular models of protein targets from Mycobacterium tuberculosis. J Mol Model 11:160–166

    Article  PubMed  CAS  Google Scholar 

  71. Dias MV, Borges JC, Ely F, Pereira JH, Canduri F, Ramos CH et al (2006) Structure of chorismate synthase from Mycobacterium tuberculosis. J Struct Biol 154:130–143

    Article  CAS  PubMed  Google Scholar 

  72. da Silveira NJ, Bonalumi CE, Uchõa HB, Pereira JH, Canduri F, de Azevedo WF (2006) DBMODELING: a database applied to the study of protein targets from genome projects. Cell Biochem Biophys 44:366–374

    Article  PubMed  Google Scholar 

  73. Borges JC, Pereira JH, Vasconcelos IB, dos Santos GC, Olivieri JR, Ramos CH et al (2006) Phosphate closes the solution structure of the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) from Mycobacterium tuberculosis. Arch Biochem Biophys 452:156–164

    Article  CAS  PubMed  Google Scholar 

  74. da Silveira NJF, Bonalumi CE, Arcuri HA, de Azevedo WF Jr (2007) Molecular modeling databases: a new way in the search of proteins targets for drug development. Curr Bioinf 2:1–10

    Article  Google Scholar 

  75. Dias MV, Faím LM, Vasconcelos IB, de Oliveira JS, Basso LA, Santos DS et al (2007) Effects of the magnesium and chloride ions and shikimate on the structure of shikimate kinase from Mycobacterium tuberculosis. Acta Crystallogr Sect F Struct Biol Cryst Commun 63:1–6

    Article  CAS  PubMed  Google Scholar 

  76. Dias MV, Ely F, Palma MS, de Azevedo WF Jr, Basso LA, Santos DS (2007) Chorismate synthase: an attractive target for drug development against orphan diseases. Curr Drug Targets 8:437–444

    Article  CAS  PubMed  Google Scholar 

  77. Marques MR, Pereira JH, Oliveira JS, Basso LA, de Azevedo WF Jr, Santos DS et al (2007) The inhibition of 5-enolpyruvylshikimate-3-phosphate synthase as a model for development of novel antimicrobials. Curr Drug Targets 8:445–457

    Article  CAS  PubMed  Google Scholar 

  78. Pereira JH, Vasconcelos IB, Oliveira JS, Caceres RA, de Azevedo WF Jr, Basso LA et al (2007) Shikimate kinase: a potential target for development of novel antitubercular agents. Curr Drug Targets 8:459–468

    Article  CAS  PubMed  Google Scholar 

  79. Marques MR, Vaso A, Neto JR, Fossey MA, Oliveira JS, Basso LA et al (2008) Dynamics of glyphosate-induced conformational changes of Mycobacterium tuberculosis 5-enolpyruvylshikimate-3-phosphate synthase (EC 2.5.1.19) determined by hydrogen-deuterium exchange and electrospray mass spectrometry. Biochemistry 47:7509–7522

    Article  CAS  PubMed  Google Scholar 

  80. Arcuri HA, Borges JC, Fonseca IO, Pereira JH, Neto JR, Basso LA et al (2008) Structural studies of shikimate 5-dehydrogenase from Mycobacterium tuberculosis. Proteins 72:720–730

    Article  CAS  PubMed  Google Scholar 

  81. Pauli I, Caceres RA, de Azevedo WF Jr (2008) Molecular modeling and dynamics studies of Shikimate Kinase from Bacillus anthracis. Bioorg Med Chem 16:8098–8108

    Article  CAS  PubMed  Google Scholar 

  82. de Azevedo WF Jr (2008) Protein-drug interactions. Curr Drug Targets 9:1030

    Article  PubMed  Google Scholar 

  83. de Azevedo WF Jr, Dias R (2008) Computational methods for calculation of ligand-binding affinity. Curr Drug Targets 92:1031–1039

    Article  Google Scholar 

  84. Dias R, de Azevedo WF Jr (2008) Molecular docking algorithms. Curr Drug Targets 9:1040–1047

    Article  CAS  PubMed  Google Scholar 

  85. Canduri F, de Azevedo WF (2008) Protein crystallography in drug discovery. Curr Drug Targets 9:1048–1053

    Article  CAS  PubMed  Google Scholar 

  86. Pauli I, Timmers LF, Caceres RA, Soares MB, de Azevedo WF Jr (2008) In silico and in vitro: identifying new drugs. Curr Drug Targets 9:1054–1061

    Article  CAS  PubMed  Google Scholar 

  87. Dias R, Timmers LF, Caceres RA, de Azevedo WF Jr (2008) Evaluation of molecular docking using polynomial empirical scoring functions. Curr Drug Targets 9:1062–1070

    Article  CAS  PubMed  Google Scholar 

  88. de Azevedo WF Jr, Dias R (2008) Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr Drug Targets 9:1071–1076

    Article  PubMed  Google Scholar 

  89. Caceres RA, Pauli I, Timmers LF, de Azevedo WF Jr (2008) Molecular recognition models: a challenge to overcome. Curr Drug Targets 9:1077–1083

    Article  CAS  PubMed  Google Scholar 

  90. Barcellos GB, Caceres RA, de Azevedo WF Jr (2009) Structural studies of shikimate dehydrogenase from Bacillus anthracis complexed with cofactor NADP. J Mol Model 15:147–155

    Article  CAS  PubMed  Google Scholar 

  91. de Azevedo WF Jr, Dias R, Timmers LF, Pauli I, Caceres RA, Soares MB (2009) Bioinformatics tools for screening of antiparasitic drugs. Curr Drug Targets 10:232–239

    Article  PubMed  Google Scholar 

  92. Arcuri HA, Zafalon GF, Marucci EA, Bonalumi CE, da Silveira NJ, Machado JM et al (2010) SKPDB: a structural database of shikimate pathway enzymes. BMC Bioinformatics 11:12

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Hernandes MZ, Cavalcanti SM, Moreira DR, de Azevedo WF Jr, Leite AC (2010) Halogen atoms in the modern medicinal chemistry: hints for the drug design. Curr Drug Targets 11:303–314

    Article  CAS  PubMed  Google Scholar 

  94. De Azevedo WF Jr (2010) Structure-based virtual screening. Curr Drug Targets 11:261–263

    Article  PubMed  Google Scholar 

  95. de Azevedo WF Jr (2011) Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem 18:1353–1366

    Article  PubMed  Google Scholar 

  96. de Azevedo WF Jr (2011) Protein targets for development of drugs against Mycobacterium tuberculosis. Curr Med Chem 18:1255–1257

    Article  PubMed  Google Scholar 

  97. Vianna CP, de Azevedo WF Jr (2012) Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. J Mol Model 18:755–764

    Article  CAS  PubMed  Google Scholar 

  98. Azevedo LS, Moraes FP, Xavier MM, Pantoja EO, Villavicencio B, Finck JA et al (2012) Recent progress of molecular docking simulations applied to development of drugs. Curr Bioinf 7:352–365

    Article  CAS  Google Scholar 

  99. Coracini JD, de Azevedo WF Jr (2014) Shikimate kinase, a protein target for drug design. Curr Med Chem 21:592–604

    Article  CAS  PubMed  Google Scholar 

  100. de Avila MB, de Azevedo WF (2014) Data mining of docking results. Application to 3-dehydroquinate dehydratase. Curr Bioinf 9:361–379

    Article  CAS  Google Scholar 

  101. Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF (2017) Supervised machine learning methods applied to predict ligand-binding affinity. Curr Med Chem 24:2459–2470

    Article  CAS  PubMed  Google Scholar 

  102. de Ávila MB, Bitencourt-Ferreira G, de Azevedo WF Jr (2019) Structural basis for inhibition of enoyl-[Acyl Carrier Protein] reductase (InhA) from Mycobacterium tuberculosis. Curr Med Chem. https://doi.org/10.2174/0929867326666181203125229

  103. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K et al (2002) The protein data bank. Acta Crystallogr D Biol Crystallogr 58:899–907

    Article  PubMed  CAS  Google Scholar 

  105. Westbrook J, Feng Z, Chen L, Yang H, Berman HM (2003) The protein data bank and structural genomics. Nucleic Acids Res 31:489–491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL et al (2016) SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb Chem High Throughput Screen 19:801–812

    Article  CAS  PubMed  Google Scholar 

  107. Levin NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr (2017) Understanding the structural basis for inhibition of cyclin-dependent kinases. New pieces in the molecular puzzle. Curr Drug Targets 18:1104–1111

    Article  CAS  PubMed  Google Scholar 

  108. de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF (2017) Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun 494:305–310

    Article  CAS  PubMed  Google Scholar 

  109. Pintro VO, Azevedo WF (2017) Optimized virtual screening workflow. Towards target-based polynomial scoring functions for HIV-1 protease. Comb Chem High Throughput Screen 20:820–827

    Article  CAS  PubMed  Google Scholar 

  110. Freitas PG, Elias TC, Pinto IA, Costa LT, de Carvalho PVSD, Omote DQ et al (2018) Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein. Lett Drug Des Discovery 15:488–499

    Article  CAS  Google Scholar 

  111. Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo WF Jr (2018) Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem 235:1–8

    Article  CAS  PubMed  Google Scholar 

  112. Amaral MEA, Nery LR, Leite CE, de Azevedo WF Jr, Campos MM (2018) Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs 36:782–796

    Article  CAS  PubMed  Google Scholar 

  113. de Ávila MB, de Azevedo WF Jr (2018) Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des 92:1468–1474

    Article  PubMed  CAS  Google Scholar 

  114. Bitencourt-Ferreira G, de Azevedo WF Jr (2018) Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem 240:63–69

    Article  CAS  PubMed  Google Scholar 

  115. de Azevedo WF Jr, Dias R (2008) Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorg Med Chem 16:9378–9382

    Article  PubMed  CAS  Google Scholar 

  116. Delatorre P, Rocha BA, Souza EP, Oliveira TM, Bezerra GA, Moreno FB et al (2007) Structure of a lectin from Canavalia gladiata seeds: new structural insights for old molecules. BMC Struct Biol 7:52

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. de Azevedo WF Jr, Canduri F, dos Santos DM, Pereira JH, Bertacine Dias MV, Silva RG et al (2003) Crystal structure of human PNP complexed with guanine. Biochem Biophys Res Commun 312:767–772

    Article  PubMed  CAS  Google Scholar 

  118. Filgueira de Azevedo W Jr, dos Santos GC, dos Santos DM, Olivieri JR, Canduri F, Silva RG et al (2003) Docking and small angle X-ray scattering studies of purine nucleoside phosphorylase. Biochem Biophys Res Commun 309:923–928

    Article  CAS  PubMed  Google Scholar 

  119. Canduri F, Perez PC, Caceres RA, de Azevedo WF Jr (2007) Protein kinases as targets for antiparasitic chemotherapy drugs. Curr Drug Targets 8:389–398

    Article  CAS  PubMed  Google Scholar 

  120. Silva RG, Pereira JH, Canduri F, de Azevedo WF Jr, Basso LA, Santos DS (2005) Kinetics and crystal structure of human purine nucleoside phosphorylase in complex with 7-methyl-6-thio-guanosine. Arch Biochem Biophys 442:49–58

    Article  CAS  PubMed  Google Scholar 

  121. Timmers LF, Caceres RA, Vivan AL, Gava LM, Dias R, Ducati RG et al (2008) Structural studies of human purine nucleoside phosphorylase: towards a new specific empirical scoring function. Arch Biochem Biophys 479:28–38

    Article  CAS  PubMed  Google Scholar 

  122. Caceres RA, Saraiva Timmers LF, Dias R, Basso LA, Santos DS, de Azevedo WF Jr (2008) Molecular modeling and dynamics simulations of PNP from Streptococcus agalactiae. Bioorg Med Chem 16:4984–4993

    Article  CAS  PubMed  Google Scholar 

  123. de Azevedo WF Jr, Ward RJ, Canduri F, Soares A, Giglio JR, Arni RK (1998) Crystal structure of piratoxin-I: a calcium-independent, myotoxic phospholipase A2-homologue from Bothrops pirajai venom. Toxicon 36:1395–1406

    Article  PubMed  Google Scholar 

  124. da Silveira NJ, Uchôa HB, Canduri F, Pereira JH, Camera JC Jr, Basso LA et al (2004) Structural bioinformatics study of PNP from Schistosoma mansoni. Biochem Biophys Res Commun 322:100–104

    Article  PubMed  CAS  Google Scholar 

  125. Bezerra GA, Oliveira TM, Moreno FB, de Souza EP, da Rocha BA, Benevides RG et al (2007) Structural analysis of Canavalia maritima and Canavalia gladiata lectins complexed with different dimannosides: new insights into the understanding of the structure-biological activity relationship in legume lectins. J Struct Biol 160:168–176

    Article  CAS  PubMed  Google Scholar 

  126. Canduri F, Fadel V, Dias MV, Basso LA, Palma MS, Santos DS et al (2005) Crystal structure of human PNP complexed with hypoxanthine and sulfate ion. Biochem Biophys Res Commun 326:335–338

    Article  CAS  PubMed  Google Scholar 

  127. Delatorre P, Rocha BA, Gadelha CA, Santi-Gadelha T, Cajazeiras JB, Souza EP et al (2006) Crystal structure of a lectin from Canavalia maritima (ConM) in complex with trehalose and maltose reveals relevant mutation in ConA-like lectins. J Struct Biol 154:280–286

    Article  CAS  PubMed  Google Scholar 

  128. Rádis-Baptista G, Moreno FB, de Lima Nogueira L, Martins AM, de Oliveira Toyama D, Toyama MH et al (2006) Crotacetin, a novel snake venom C-type lectin homolog of convulxin, exhibits an unpredictable antimicrobial activity. Cell Biochem Biophys 44:412–423

    Article  PubMed  Google Scholar 

  129. Breda A, Basso LA, Santos DS, de Azevedo WF Jr (2008) Virtual screening of drugs: score functions, docking, and drug design. Curr Comput Aided Drug Des 4:265–272

    Article  CAS  Google Scholar 

  130. Nolasco DO, Canduri F, Pereira JH, Cortinóz JR, Palma MS, Oliveira JS et al (2004) Crystallographic structure of PNP from Mycobacterium tuberculosis at 1.9A resolution. Biochem Biophys Res Commun 324:789–794

    Article  CAS  PubMed  Google Scholar 

  131. Soares MB, Silva CV, Bastos TM, Guimarães ET, Figueira CP, Smirlis D et al (2012) Anti-Trypanosoma cruzi activity of nicotinamide. Acta Trop 12:224–229

    Article  CAS  Google Scholar 

  132. Rocha BA, Delatorre P, Oliveira TM, Benevides RG, Pires AF, Sousa AA et al (2011) Structural basis for both pro- and anti-inflammatory response induced by mannose-specific legume lectin from Cymbosema roseum. Biochimie 93:806–816

    Article  CAS  PubMed  Google Scholar 

  133. Ducati RG, Basso LA, Santos DS, de Azevedo WF Jr (2010) Crystallographic and docking studies of purine nucleoside phosphorylase from Mycobacterium tuberculosis. Bioorg Med Chem 18:4769–4774

    Article  CAS  PubMed  Google Scholar 

  134. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321

    Article  CAS  PubMed  Google Scholar 

  135. Heberlé G, de Azevedo WF Jr (2011) Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem 18:1339–1352

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. MV-A acknowledges support from PUCRS/IC Jr. WFA is a senior researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).

Author information

Authors and Affiliations

  1. Escola de Ciências da Saúde, Pontifícia Universidade Católica do Rio Grande do Sul—PUCRS, Porto Alegre, RS, Brazil

    Gabriela Bitencourt-Ferreira, Martina Veit-Acosta & Walter Filgueira de Azevedo Jr.

Authors
  1. Gabriela Bitencourt-Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martina Veit-Acosta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Walter Filgueira de Azevedo Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Walter Filgueira de Azevedo Jr. .

Editor information

Editors and Affiliations

  1. Escola de Ciências da Saúde, Pontifícia Universidade Católica do Rio Grande do Sul—PUCRS, Porto Alegre, Rio Grande do Sul, Brazil

    Walter Filgueira de Azevedo Jr.

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Bitencourt-Ferreira, G., Veit-Acosta, M., de Azevedo, W.F. (2019). Van der Waals Potential in Protein Complexes. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9752-7_6

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9751-0

  • Online ISBN: 978-1-4939-9752-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation