Supervised Molecular Dynamics (SuMD) Approaches in Drug Design

  • Davide Sabbadin
  • Veronica Salmaso
  • Mattia Sturlese
  • Stefano MoroEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1824)


Supervised MD (SuMD) is a computational method that enables the exploration of ligand–receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation. SuMD enables the investigation of ligand–receptor binding events independently from the starting position, chemical structure of the ligand (small molecules or peptides), and also from its receptor-binding affinity. The application of SuMD highlights an appreciable capability of the technique to reproduce the crystallographic structures of several ligand–protein complexes and can provide high-quality protein–ligand models of for which yet experimental confirmation of binding mode is not available.

Key words

Ligand–protein binding Peptide–protein binding Recognition pathway Molecular dynamics Supervised molecular dynamics Meta-binding site 


  1. 1.
    Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Cooke RM, Brown AJH, Marshall FH, Mason JS (2015) Structures of G protein-coupled receptors reveal new opportunities for drug discovery. Drug Discov Today 20(11):1355–1364. CrossRefPubMedGoogle Scholar
  3. 3.
    Dror RO, Pan AC, Arlow DH et al (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci U S A 108(32):13118–13123. CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci U S A 108(25):10184–10189. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ferruz N, Harvey MJ, Mestres J, De Fabritiis G (2015) Insights from fragment hit binding assays by molecular simulations. J Chem Inf Model 55(10):2200–2205. CrossRefPubMedGoogle Scholar
  6. 6.
    Kuhn B, Guba W, Hert J et al (2016) A real-world perspective on molecular design. J Med Chem 59(9):4087–4102. CrossRefPubMedGoogle Scholar
  7. 7.
    Sabbadin D, Moro S (2014) Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J Chem Inf Model 54(2):372–376. CrossRefPubMedGoogle Scholar
  8. 8.
    Cuzzolin A, Sturlese M, Deganutti G et al (2016) Deciphering the complexity of ligand-protein recognition pathways using supervised molecular dynamics (SuMD) simulations. J Chem Inf Model 56(4):687–705. CrossRefPubMedGoogle Scholar
  9. 9.
    Jaakola V-P, Griffith MT, Hanson MA et al (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322(5905):1211–1217. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Congreve M, Andrews SP, Doré AS et al (2012) Discovery of 1,2,4-triazine derivatives as adenosine A(2A) antagonists using structure based drug design. J Med Chem 55(5):1898–1903. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Doré AS, Robertson N, Errey JC et al (2011) Structure of the adenosine A(2A) receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 19(9):1283–1293. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Salmaso V, Sturlese M, Cuzzolin A, Moro S (2017) Exploring protein–peptide recognition pathways using a supervised molecular dynamics approach. Structure 25(4):655–662.e2. CrossRefPubMedGoogle Scholar
  13. 13.
    Scott DE, Bayly AR, Abell C, Skidmore J (2016) Small molecules, big targets: drug discovery faces the protein–protein interaction challenge. Nat Rev Drug Discov 15(8):533–550. CrossRefPubMedGoogle Scholar
  14. 14.
    Kussie PH, Gorina S, Marechal V et al (1996) Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain. Science 274(5289):948–953. CrossRefPubMedGoogle Scholar
  15. 15.
    Petros AM, Nettesheim DG, Wang Y et al (2000) Rationale for Bcl-xL/bad peptide complex formation from structure, mutagenesis, and biophysical studies. Protein Sci 9(12):2528–2534. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Baek S, Kutchukian PS, Verdine GL et al (2012) Structure of the stapled p53 peptide bound to Mdm2. J Am Chem Soc 134(1):103–106. CrossRefPubMedGoogle Scholar
  17. 17.
    Deganutti G, Welihinda A, Moro S (2017) Comparison of the human A2A adenosine receptor recognition by adenosine and Inosine: new insight from supervised molecular dynamics simulations. ChemMedChem 12(16):1319–1326. CrossRefPubMedGoogle Scholar
  18. 18.
    Cheng RKY, Segala E, Robertson N et al (2017) Structures of human A1 and A2A adenosine receptors with Xanthines reveal determinants of selectivity. Structure 25(8):1275–1285.e4. CrossRefPubMedGoogle Scholar
  19. 19.
    Williams T, Kelley C Gnuplot 4.5: an interactive plotting program, version 4.5; (Accessed October 2015)
  20. 20.
    Harvey MJ, Giupponi G, Fabritiis GD (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5(6):1632–1639. CrossRefPubMedGoogle Scholar
  21. 21.
    Case DA, Babin V, Berryman JT et al (2014) AMBER 14Google Scholar
  22. 22.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. CrossRefGoogle Scholar
  23. 23.
    MacKerell AD, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616. CrossRefPubMedGoogle Scholar
  24. 24.
    MacKerell AD, Banavali N, Foloppe N (2000) Development and current status of the CHARMM force field for nucleic acids. Biopolymers 56(4):257–265.<257::AID-BIP10029>3.0.CO;2-WCrossRefPubMedGoogle Scholar
  25. 25.
    Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52(12):3144–3154. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Jakowiecki J, Filipek S (2016) Hydrophobic ligand entry and exit pathways of the CB1 cannabinoid receptor. J Chem Inf Model 56(12):2457–2466. CrossRefPubMedGoogle Scholar
  28. 28.
    Fronik P, Gaiser BI, Sejer Pedersen D (2017) Bitopic ligands and metastable binding sites: opportunities for G protein-coupled receptor (GPCR) medicinal chemistry. J Med Chem 60(10):4126–4134. CrossRefPubMedGoogle Scholar
  29. 29.
    Sabbadin D, Ciancetta A, Moro S (2014) Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study. J Chem Inf Model 54(10):2846–2855. CrossRefPubMedGoogle Scholar
  30. 30.
    Paoletta S, Sabbadin D, von Kügelgen I et al (2015) Modeling ligand recognition at the P2Y12 receptor in light of X-ray structural information. J Comput Aided Mol Des 29(8):737–756. CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Lipkowitz K (1995) Abuses of molecular mechanics: pitfalls to avoid. J Chem Educ 72(12):1070. CrossRefGoogle Scholar
  32. 32.
    Brameld KA, Kuhn B, Reuter DC, Stahl M (2008) Small molecule conformational preferences derived from crystal structure data. A medicinal chemistry focused analysis. J Chem Inf Model 48(1):1–24. CrossRefGoogle Scholar
  33. 33.
    Lovell SC, Word JM, Richardson JS, Richardson DC (2000) The penultimate rotamer library. Proteins 40(3):389–408.<389::AID-PROT50>3.0.CO;2-2CrossRefPubMedGoogle Scholar
  34. 34.
    Allen FH (2002) The Cambridge structural database: a quarter of a million crystal structures and rising. Acta Crystallogr B 58:380–388CrossRefPubMedGoogle Scholar
  35. 35.
    Stahl M, Baier S (2015) How many molecules does it take to tell a story? Case studies, language, and an epistemic view of medicinal chemistry. ChemMedChem 10(6):949–956. CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Davide Sabbadin
    • 1
  • Veronica Salmaso
    • 2
  • Mattia Sturlese
    • 2
  • Stefano Moro
    • 2
    Email author
  1. 1.Syngenta Crop Protection AGSteinSwitzerland
  2. 2.Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of PadovaPadovaItaly

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