Novel Algorithm for Efficient Distribution of Molecular Docking Calculations

  • Luigi Di Biasi
  • Roberto Fino
  • Rosaura Parisi
  • Lucia Sessa
  • Giuseppe Cattaneo
  • Alfredo De Santis
  • Pio Iannelli
  • Stefano PiottoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 587)


Molecular docking is a computational method to study the formation of intermolecular complexes between two molecules. In drug discovery, it is employed to estimate the binding between a small ligand (the drug candidate), and a protein of known three-dimensional structure. Docking is becoming a standard part of workflow in drug discovery. Recently, we have used the software VINA, a de facto standard in molecular docking, to perform extensive docking analysis. Unfortunately, performing a successful blind docking procedure requires large computational resources that can be obtained by the use of clusters or dedicated grid. Here we present a new tool to distribute efficiently a molecular docking calculation onto a grid changing the distribution paradigm: we define portions on the protein surface, named hotspots, and the grid will perform a local docking for each region. Performance studies have been conducted via the software GRIMD.


Molecular Docking Markov Chain Monte Carlo Method Docking Software Blind Docking Conservation Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by the “Data-Driven Genomic Computing (GenData 2020)” PRIN project (2013–2015), funded by the Italian Ministry of the University and Research (MIUR).


  1. 1.
    Trott, O., Olson, A.J.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31(2), 455–461 (2010)Google Scholar
  2. 2.
    de Vries, S.J., van Dijk, A.D., Bonvin, A.M.: WHISCY: What information does surface conservation yield? Application to data-driven docking. Proteins Struct. Funct. Bioinf. 63(3), 479–489 (2006)CrossRefGoogle Scholar
  3. 3.
    Ouzounis, C., Pérez-Irratxeta, C., Sander, C., Valencia, A.: Are binding residues conserved? In: Pacific Symposium on Biocomputing, pp. 401–412 (1997)Google Scholar
  4. 4.
    Hooft, R.W., Sander, C., Scharf, M., Vriend, G.: The PDBFINDER database: a summary of PDB, DSSP and HSSP information with added value. Comput. Appl. Biosci. CABIOS 12(6), 525–529 (1996)Google Scholar
  5. 5.
    Piotto, S., Di Biasi, L., Concilio, S., Castiglione, A., Cattaneo, G.: GRIMD: distributed computing for chemists and biologists. Bioinformation 10(1), 43 (2014)CrossRefGoogle Scholar
  6. 6.
    Concilio, S., Bugatti, V., Neitzert, H.C., Landi, G., De Sio, A., Parisi, J., Piotto, S., Iannelli, P.: Zn-complex based on oxadiazole/carbazole structure: synthesis, optical and electric properties. Thin Solid Films 556, 419–424 (2014)CrossRefGoogle Scholar
  7. 7.
    Lopez, D.H., Fiol-deRoque, M.A., Noguera-Salvà, M.A., Terés, S., Campana, F., Piotto, S., Castro, J.A., Mohaibes, R.J., Escribá, P.V., Busquets, X.: 2-Hydroxy arachidonic acid: a new non-steroidal anti-inflammatory drug. PLoS ONE 8(8), e72052 (2013)CrossRefGoogle Scholar
  8. 8.
    Piotto, S., Concilio, S., Bianchino, E., Iannelli, P., López, D.J., Terés, S., Ibarguren, M., Barceló-Coblijn, G., Martin, M.L., Guardiola-Serrano, F.: Differential effect of 2-hydroxyoleic acid enantiomers on protein (sphingomyelin synthase) and lipid (membrane) targets. Biochim. Biophys. Acta (BBA)-Biomembr. 1838(6), 1628–1637 (2014)CrossRefGoogle Scholar
  9. 9.
    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(6), 1509–1517 (2014)CrossRefGoogle Scholar
  10. 10.
    Scrima, M., Di Marino, S., Grimaldi, M., Campana, F., Vitiello, G., Piotto, S.P., D’Errico, G., D’Ursi, A.M.: Structural features of the C8 antiviral peptide in a membrane-mimicking environment. Biochim. Biophys. Acta (BBA)-Biomembr. 1838(3), 1010–1018 (2014)CrossRefGoogle Scholar
  11. 11.
    Caracciolo, G., Piotto, S., Bombelli, C., Caminiti, R., Mancini, G.: Segregation and phase transition in mixed lipid films. Langmuir 21(20), 9137–9142 (2005)CrossRefGoogle Scholar
  12. 12.
    Piotto, S., Concilio, S., Mavelli, F., Iannelli, P.: Computer simulations of natural and synthetic polymers in confined systems. Macromol. Symp. 286(1), 25–33 (2009)CrossRefGoogle Scholar
  13. 13.
    Piotto, S., Nesper, R.: CURVIS: a program to study and analyse crystallographic structures and phase transitions. J. Appl. Crystallogr. 38(1), 223–227 (2005)CrossRefGoogle Scholar
  14. 14.
    Acierno, D., Amendola, E., Bugatti, V., Concilio, S., Giorgini, L., Iannelli, P., Piotto, S.: Synthesis and characterization of segmented liquid crystalline polymers with the azo group in the main chain. Macromolecules 37(17), 6418–6423 (2004)CrossRefGoogle Scholar
  15. 15.
    Piotto, S., Mavelli, F.: Monte Carlo simulations of vesicles and fluid membranes transformations. Orig. Life Evol. Biosph. 34(1–2), 225–235 (2004)CrossRefGoogle Scholar
  16. 16.
    Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J.: AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30(16), 2785–2791 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Luigi Di Biasi
    • 1
    • 2
  • Roberto Fino
    • 1
  • Rosaura Parisi
    • 1
  • Lucia Sessa
    • 1
  • Giuseppe Cattaneo
    • 2
  • Alfredo De Santis
    • 2
  • Pio Iannelli
    • 1
  • Stefano Piotto
    • 1
    Email author
  1. 1.Department of PharmacyUniversity of SalernoFiscianoItaly
  2. 2.Department of InformaticsUniversity of SalernoFiscianoItaly

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