Fragment Based Molecular Dynamics for Drug Design

  • Lucia Sessa
  • Luigi Di Biasi
  • Simona Concilio
  • Stefano Piotto
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 830)


Molecular docking is a computationally efficient method used to predict the conformations adopted by the ligand within a target-binding site. A positive aspect of conventional docking is the possibility of easily distributing the calculation on dedicated grid or cluster. The receptor is usually kept rigid, therefore the changes in the binding pocket geometry induced by the ligand is overlooked. Here we present a new docking approach (DynDock) that exploits molecular dynamics to preserve the flexibility of the receptor. To maintain high computational efficiency, DynDock has been developed to be distributed on a grid. The main advantages of this method are the full flexible molecular docking achieved during the simulation and the reduced number of compounds collected.


Docking Drug design Molecular dynamics 


  1. 1.
    Sliwoski, G., Kothiwale, S., Meiler, J., Lowe, E.W.: Computational methods in drug discovery. Pharmacol. Rev. 66, 334–395 (2014)CrossRefGoogle Scholar
  2. 2.
    Salum, L.B., Polikarpov, I., Andricopulo, A.D.: Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity. J. Chem. Inf. Model. 48, 2243–2253 (2008)CrossRefGoogle Scholar
  3. 3.
    Ferreira, L.G., Dos Santos, R.N., Oliva, G., Andricopulo, A.D.: Molecular docking and structure-based drug design strategies. Molecules 20, 13384–13421 (2015)CrossRefGoogle Scholar
  4. 4.
    Sessa, L., Biasi, L.D., Concilio, S., Cattaneo, G., De Santis, A., Iannelli, P., Piotto, S.: A new flexible protocol for docking studies. Commun. Comput. Inf. Sci. 587, 117–126 (2016)Google Scholar
  5. 5.
    Lin, J.-H.: Accommodating protein flexibility for structure-based drug design. Curr. Top. Med. Chem. 11, 171–178 (2011)CrossRefGoogle Scholar
  6. 6.
    Durrant, J.D., McCammon, J.A.: Molecular dynamics simulations and drug discovery. BMC Biol. 9, 71 (2011)CrossRefGoogle Scholar
  7. 7.
    de Ruyck, J., Brysbaert, G., Blossey, R., Lensink, M.F.: Molecular docking as a popular tool in drug design, an in silico travel. Adv. Appl. Bioinf. Chem. AABC 9, 1 (2016)Google Scholar
  8. 8.
    Geng, C., Narasimhan, S., Rodrigues, J.P., Bonvin, A.M.: Information-driven, ensemble flexible peptide docking using HADDOCK. In: Schueler-Furman, O., London, N. (eds.) Modeling Peptide-Protein Interactions. MMB, vol. 1561, pp. 109–138. Springer, New York (2017). Scholar
  9. 9.
    Sessa, L., Concilio, S., Piotto, S.: Molecular dynamics and morphing protocols for high accuracy molecular docking. In: Piotto, S., Rossi, F., Concilio, S., Reverchon, E., Cattaneo, G. (eds.) Advances in Bionanomaterials. LNB, pp. 85–96. Springer, Cham (2018). Scholar
  10. 10.
    James, M.N.G., Sielecki, A.R., Brayer, G.D., Delbaere, L.T.J., Bauer, C.A.: Structures of product and inhibitor complexes of Streptomyces griseus protease A at 1.8 Å resolution: a model for serine protease catalysis. J. Mol. Biol. 144, 43–88 (1980)CrossRefGoogle Scholar
  11. 11.
    Bartholomae, M., Buivydas, A., Viel, J.H., Montalban-Lopez, M., Kuipers, O.P.: Major gene-regulatory mechanisms operating in ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthesis. Mol. Microbiol. 106(2), 186–206 (2017)CrossRefGoogle Scholar
  12. 12.
    Harish, B., Uppuluri, K.B.: Microbial serine protease inhibitors and their therapeutic applications. International J. Biol. Macromol. 107, 1373–1387 (2017)CrossRefGoogle Scholar
  13. 13.
    Piotto, S., Di Biasi, L., Concilio, S., Castiglione, A., Cattaneo, G.: GRIMD: distributed computing for chemists and biologists. Bioinformation 10, 43–47 (2014)CrossRefGoogle Scholar
  14. 14.
    Wagner, J.R., Sorgentini, D.A., Añón, M.C.: Relation between solubility and surface hydrophobicity as an indicator of modifications during preparation processes of commercial and laboratory-prepared soy protein isolates. J. Agric. Food Chem. 48, 3159–3165 (2000)CrossRefGoogle Scholar
  15. 15.
    UniProt Consortium: UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2016)CrossRefGoogle Scholar
  16. 16.
    Lee, J., Cheng, X., Swails, J.M., Yeom, M.S., Eastman, P.K., Lemkul, J.A., Wei, S., Buckner, J., Jeong, J.C., Qi, Y.: CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2015)CrossRefGoogle Scholar
  17. 17.
    Krieger, E., Vriend, G.: YASARA view-molecular graphics for all devices-from smartphones to workstations. Bioinformatics 30, 2981–2982 (2014)CrossRefGoogle Scholar
  18. 18.
    Di Biasi, L., Fino, R., Parisi, R., Sessa, L., Cattaneo, G., De Santis, A., Iannelli, P., Piotto, S.: Novel algorithm for efficient distribution of molecular docking calculations. Commun. Comput. Inf. Sci. 587, 65–74 (2016)Google Scholar
  19. 19.
    Piotto, S., Di Biasi, L., Fino, R., Parisi, R., Sessa, L., Concilio, S.: Yada: a novel tool for molecular docking calculations. J. Comput. Aided Mol. Des. 30, 753–759 (2016)CrossRefGoogle Scholar
  20. 20.
    Berezin, C., Glaser, F., Rosenberg, J., Paz, I., Pupko, T., Fariselli, P., Casadio, R., Ben-Tal, N.: ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics 20, 1322–1324 (2004)CrossRefGoogle Scholar
  21. 21.
    Copeland, R.A., Pompliano, D.L., Meek, T.D.: Drug–target residence time and its implications for lead optimization. Nat. Rev. Drug Discovery 5, 730–739 (2006)CrossRefGoogle Scholar
  22. 22.
    Piotto, S., Sessa, L., Iannelli, P., Concilio, S.: Computational study on human sphingomyelin synthase 1 (hSMS1). Biochim. Biophys. Acta (BBA) Biomembr. 1859, 1517–1525 (2017)CrossRefGoogle Scholar
  23. 23.
    Casas, J., Ibarguren, M., Álvarez, R., Terés, S., Lladó, V., Piotto, S.P., Concilio, S., Busquets, X., López, D.J., Escribá, P.V.: G protein-membrane interactions II: effect of G protein-linked lipids on membrane structure and G protein-membrane interactions. Biochim. Biophys. Acta (BBA) Biomembr. 1859, 1526–1535 (2017)CrossRefGoogle Scholar
  24. 24.
    Piotto, S., Trapani, A., Bianchino, E., Ibarguren, M., López, D.J., Busquets, X., Concilio, S.: The effect of hydroxylated fatty acid-containing phospholipids in the remodeling of lipid membranes. Biochim. Biophys. Acta (BBA) Biomembr. 1838, 1509–1517 (2014)CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PharmacyUniversity of SalernoFiscianoItaly
  2. 2.Department of Industrial EngineeringUniversity of SalernoFiscianoItaly

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