Skip to main content
SpringerLink
Log in

Molecular Dynamics as a Tool for Virtual Ligand Screening

  • Protocol
  • First Online:
Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1762))

Abstract

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).

At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein–drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
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. Tarcsay A, Paragi G, Vass M, Jojart B, Bogar F, Keseru GM (2013) The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 53:2990−2999

    Article  Google Scholar 

  2. Barakat KH, Jordheim LP, Perez-Pineiro R, Wishart D, Dumontet C, Tuszynski JA (2012) Virtual screening and biological evaluation of inhibitors targeting the XPA-ERCC1 interaction. PLoS One 7:e51329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035−4061

    Google Scholar 

  4. Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71–79

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Galeazzi R (2009) Molecular dynamics as a tool in rational drug design: current status and some major applications. Curr Comput Aided Drug Des 5:225–240

    Article  CAS  Google Scholar 

  6. Hospital A, Goni JR, Orozco M, Gelpi JL (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 8:37–47

    PubMed  PubMed Central  Google Scholar 

  7. Jiang L, Zhang X, Chen X, He Y, Qiao L, Zhang Y, Li G, Xiang Y (2015) Virtual screening and molecular dynamics study of potential negative allosteric modulators of mGluR1 from Chinese herbs. Molecules 20:12769–12786

    Article  CAS  PubMed  Google Scholar 

  8. Kundu A, Dutta A, Biswas P, Das AK, Ghosh AK (2015) Functional insights from molecular modeling, docking, and dynamics study of a cypoviral RNA dependent RNA polymerase. J Mol Graph Model 61:160–174

    Article  CAS  PubMed  Google Scholar 

  9. Mirza SB, Salmas RE, Fatmi MQ, Durdagi S (2016) Virtual screening of eighteen million compounds against dengue virus: combined molecular docking and molecular dynamics simulations study. J Mol Graph Model 66:99–107

    Article  CAS  PubMed  Google Scholar 

  10. Moroy G, Sperandio O, Rielland S, Khemka S, Druart K, Goyal D, Perahia D, Miteva MA (2015) Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis. Future Med Chem 7:2317–2331

    Article  CAS  PubMed  Google Scholar 

  11. Naresh KN, Sreekumar A, Rajan SS (2015) Structural insights into the interaction between molluscan hemocyanins and phenolic substrates: an in silico study using docking and molecular dynamics. J Mol Graph Model 61:272–280

    Article  CAS  PubMed  Google Scholar 

  12. Nichols SE, Baron R, Ivetac A, McCammon JA (2011) Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 51:1439–1446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nichols SE, Riccardo B, McCammon JA (2012) On the use of molecular dynamics receptor conformations for virtual screening. Methods Mol Biol 819:93–103

    Article  CAS  PubMed  Google Scholar 

  14. Okimoto N, Futatsugi N, Fuji H, Suenaga A, Morimoto G, Yanai R, Ohno Y, Narumi T, Taiji M (2009) High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations. PLoS Comput Biol 5:e1000528

    Article  PubMed  PubMed Central  Google Scholar 

  15. Rodriguez-Bussey IG, Doshi U, Hamelberg D (2016) Enhanced molecular dynamics sampling of drug target conformations. Biopolymers 105:35–42

    Article  CAS  PubMed  Google Scholar 

  16. Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bartesaghi A, Merk A, Banerjee S, Matthies D, Wu X, Milne JLS, Subramaniam S (2015) 2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor. Science 348:1147–1151

    Article  CAS  PubMed  Google Scholar 

  18. Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162:1239–1249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kuenemann MA, Sperandio O, Labbe CM, Lagorce D, Miteva MA, Villoutreix BO (2015) In silico design of low molecular weight protein-protein interaction inhibitors: overall concept and recent advances. Prog Biophys Mol Biol 119:20–32

    Article  CAS  PubMed  Google Scholar 

  20. Ramirez D (2016) Computational methods applied to rational drug design. Open Med Chem J 10:7–20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Rognan D (2015) Rational design of protein-protein interaction inhibitors. Med Chem Commun 6:51–60

    Article  CAS  Google Scholar 

  22. B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14:394–400

    Article  CAS  PubMed  Google Scholar 

  23. Cavasotto CN, Abagyan RA (2004) Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 337:209–225

    Article  CAS  PubMed  Google Scholar 

  24. Durrant JD, McCammon JA (2010) Computer-aided drug-discovery techniques that account for receptor flexibility. Curr Opin Pharmacol 10:770–774

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Totrov M, Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18:178–184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Feher M (2006) Consensus scoring for protein–ligand interactions. Drug Discov Today 11:421–428

    Article  CAS  PubMed  Google Scholar 

  27. Politi R, Convertino M, Popov K, Dokholyan NV, Tropsha A (2016) Docking and scoring with target-specific pose classifier succeeds in native-like pose identification but not binding affinity prediction in the CSAR 2014 benchmark exercise. J Chem Inf Model 56:1032–1041

    Article  CAS  PubMed  Google Scholar 

  28. Quiroga R, Villarreal MA (2016) Vinardo: a scoring function based on autodock vina improves scoring, docking, and virtual screening. PLoS One 11:e0155183

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wang Z, Sun H, Yao X, Li D, Xu L, Li Y, Tian S, Hou T (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964–12975

    Article  CAS  PubMed  Google Scholar 

  30. Seifert MHJ (2009) Targeted scoring functions for virtual screening. Drug Discov Today 14:562–569

    Article  PubMed  Google Scholar 

  31. Leach AR (2001) Molecular modelling: principles and applications, 2nd edn. Pearson, Dorchester

    Google Scholar 

  32. Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341

    Article  CAS  Google Scholar 

  33. Schlick T (2002) Molecular modeling and simulation: an interdisciplinary guide. Springer, New York

    Book  Google Scholar 

  34. Stanley N, De Fabritiis G (2015) High throughput molecular dynamics for drug discovery. Silico Pharmacol 3:3–6

    Article  Google Scholar 

  35. Lin JH, Perryman AL, Schames JR, McCammon JA (2003) The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 68:47–62

    Article  CAS  PubMed  Google Scholar 

  36. Sinko W, Lindert S, McCammon JA (2013) Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design. Chem Biol Drug Des 81:41–49

    Article  CAS  PubMed  Google Scholar 

  37. Cala O, Remy M-H, Guillet V, Merdes A, Mourey L, Milon A, Czaplicki G (2013) Virtual and biophysical screening targeting the gamma-tubulin complex – a new target for the inhibition of microtubule nucleation. PLoS One 8:e63908

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Alonso H, Bliznyuk AA, Gready JE (2006) Combining docking and molecular dynamic simulations in drug design. Med Res Rev 26:531–568

    Article  CAS  PubMed  Google Scholar 

  39. Mori T, Miyashita N, Im W, Feig M, Sugita Y (2016) Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms. Biochim Biophys Acta 1858:1635–1651

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Arthur EJ, Brooks CL III (2016) Efficient Implementation of constant pH molecular dynamics on modern graphics processors. J Comput Chem 37:2171–2180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ge H, Wang Y, Li C et al (2013) Molecular dynamics-based virtual screening: accelerating the drug discovery process by high-performance computing. J Chem Inf Model 53:2757–2764

    Article  CAS  PubMed  Google Scholar 

  42. Iakovou G, Hayward S, Laycock SD (2015) Adaptive GPU-accelerated force calculation for interactive rigid molecular docking using haptics. J Mol Graph Model 61:1–12

    Article  CAS  PubMed  Google Scholar 

  43. Kazachenko S, Giovinazzo M, Hall KW, Cann NM (2015) Algorithms for GPU-based molecular dynamics simulations of complex fluids: applications to water, mixtures, and liquid crystals. J Comput Chem 36:1787–1804

    Article  CAS  PubMed  Google Scholar 

  44. Kutzner C, Pall S, Fechner M, Esztermann A, de Groot BL, Grubmüller H (2015) Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J Comput Chem 36:1990–2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular simulation package. WIREs Comput Mol Sci 3:198–210

    Article  CAS  Google Scholar 

  46. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Eswar N, Eramian D, Webb B, Shen M-Y, Sali A (2008) Protein structure modeling with MODELLER. In: Kobe B, Guss M, Huber T (eds) Structural proteomics. High-throughput methods. Methods in molecular biology, vol 426. Humana, Totowa, NJ, pp 145–159

    Google Scholar 

  49. Song Y, DiMaio F, Wang RY-R, Kim D, Miles C, Brunette T, Thompson J, Baker D (2013) High resolution comparative modeling with RosettaCM. Structure 21:1735–1742

    Article  CAS  PubMed  Google Scholar 

  50. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. McGann M (2011) FRED pose prediction and virtual screening accuracy. J Chem Inf Model 51:578–596

    Article  CAS  PubMed  Google Scholar 

  52. Irwin JJ, Shoichet BK (2005) ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Baell J, Walters MA (2014) Chemical con artists foil drug discovery. Nature 513:481–483

    Article  CAS  PubMed  Google Scholar 

  54. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  PubMed  Google Scholar 

  55. Chong S-H, Ham S (2015) Structural versus energetic approaches for protein conformational entropy. Chem Phys Lett 627:90–95

    Article  CAS  Google Scholar 

  56. Kassem S, Marawan A, El-Sheikh S, Barakat KH (2015) Entropy in bimolecular simulations: a comprehensive review of atomic fluctuations-based methods. J Mol Graph Model 62:105–117

    Article  CAS  PubMed  Google Scholar 

  57. Procacci P (2016) Reformulating the entropic contribution in molecular docking scoring functions. J Comput Chem 37:1819–1827

    Article  CAS  PubMed  Google Scholar 

  58. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discovery 10:449–461

    Article  CAS  Google Scholar 

  59. Vosmeer CR, Pool R, van Stee MF, Peric-Hassler L, Vermeulen NPE, Geerke DP (2014) Towards automated binding affinity prediction using an iterative linear interaction energy approach. Int J Mol Sci 15:798–816

    Article  PubMed  PubMed Central  Google Scholar 

  60. Rosendahl Kjellgren E, Skytte Glue OE, Reinholdt P, Egeskov Meyer J, Kongsted J, Poongavanam V (2015) A comparative study of binding affinities for6,7-dimethoxy-4-pyrrolidylquinazolines as phosphodiesterase 10 A inhibitors using the linear interaction energy method. J Mol Graph Model 61:44–52

    Article  Google Scholar 

  61. Stjernschantz E, Oostenbrink C (2010) Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 98:2682–2691

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Miller BR III, McGee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA.py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8:3314–3321

    Article  CAS  PubMed  Google Scholar 

  63. Borhani DW, Shaw DE (2012) The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 26:15–26

    Article  CAS  PubMed  Google Scholar 

  64. Decherchi S, Masetti M, Vyalov I, Rocchia W (2015) Implicit solvent methods for free energy estimation. Eur J Med Chem 91:27–42

    Article  CAS  PubMed  Google Scholar 

  65. Le L (2012) Incorporating molecular dynamics simulations into rational drug design: a case study on influenza a neuraminidases. In: Pérez-Sánchez H (ed) Bioinformatics. InTech, Rijeka

    Google Scholar 

  66. Mortier J, Rakers C, Bermudez M, Murgueitio MS, Riniker S, Wolber G (2015) The impact of molecular dynamics on drug design: applications for the characterization of ligand–macromolecule complexes. Drug Discov Today 20:686–702

    Article  CAS  PubMed  Google Scholar 

  67. Tautermann CS, Seeliger D, Kriegl JM (2015) What can we learn from molecular dynamics simulations for GPCR drug design? Comput Struct Biotechnol J 13:111–121

    Article  CAS  PubMed  Google Scholar 

  68. Zhao H, Caflisch A (2015) Molecular dynamics in drug design. Eur J Med Chem 91:4–14

    Article  CAS  PubMed  Google Scholar 

  69. Okimoto N, Suenaga A, Taiji M (2016) Evaluation of protein–ligand affinity prediction using steered molecular dynamics simulations. J Biomol Struct Dyn 7:1–11

    Google Scholar 

  70. Li MS, Mai BK (2012) Steered molecular dynamics—a promising tool for drug design. Curr Bioinformatics 7:342–351

    Article  CAS  Google Scholar 

  71. Pang Y-P, Xu K, El Yazal J, Prendergast FG (2000) Successful molecular dynamics simulation of the zinc-bound farnesyltransferase using the cationic dummy atom approach. Protein Sci 9:1857–1865

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Menchon G, Bombarde O, Trivedi M et al (2016) Structure-based virtual ligand screening on the XRCC4/DNA ligase IV interface. Sci Rep 6:22878–22890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgment

We acknowledge financial support from PICT—GenoToul platform of Toulouse, CNRS, Université de Toulouse-UPS, European structural funds, the Midi-Pyrénées region, CNRS. G.M. was supported by Ph.D. fellowships from Ministère de l’enseignement supérieur et de la Recherche (3 years) and from Ligue Nationale Contre le Cancer (1 year). We thank Alain Milon and Pascal Demange for their critical reading of the manuscript, which improved its final quality. 

Author information

Authors and Affiliations

  1. Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen PSI, Switzerland

    Grégory Menchon

  2. Institute of Pharmacology and Structural Biology, UMR 5089, University of Toulouse III, Toulouse, France

    Laurent Maveyraud & Georges Czaplicki

Authors
  1. Grégory Menchon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laurent Maveyraud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Georges Czaplicki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Georges Czaplicki .

Editor information

Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, KA, India

    Mohini Gore

  2. Department of Biotechnology, Shivaji University, Kolhapur, MH, India

    Umesh B. Jagtap

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Menchon, G., Maveyraud, L., Czaplicki, G. (2018). Molecular Dynamics as a Tool for Virtual Ligand Screening. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7756-7_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7755-0

  • Online ISBN: 978-1-4939-7756-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation