Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Parameter Sweep Workflows for Modelling Carbohydrate Recognition

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Carbohydrate recognition is a phenomenon critical to a number of biological functions in humans. Understanding the dynamic behaviour of oligosaccharides should help in the discovery of the mechanisms which lead to specific and selective recognition of carbohydrates by proteins. Computer programs which can provide insight into such biological recognition processes have significant potential to contribute to biomedical research if the results of the simulation can prove consistent with the outcome of conventional wet laboratory experiments. In order to validate these simulation tools and support their wider uptake by the bio-scientist research community, high-level easy to use integrated environments are required to run massively parallel simulation workflows. This paper describes how the ProSim Science Gateway, based on the WS-PGRADE Grid portal, has been created to execute and visualise the results of complex parameter sweep workflows for modelling carbohydrate recognition.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Jacq, N. et al.: Grid-enabled virtual Screening against malaria. Journal of Grid Computing 6(1), 29–43 (2008). Springer Netherlands, 2007. doi:10.1007/s10723-007-9085-5

    Google Scholar 

  2. Goble, C.A., De Roure, D.C.: myExperiment: social networking for workflow-using e-scientists. In: Proceedings of the 2nd Workshop on Workflows in Support of Large-Scale Science, Monterey, California, USA (2007), ISBN:978-1-59593-715-5

  3. Kacsuk, P., Sipos, G.: Multi-Grid, multi-user workflows in the P-GRADE Grid Portal. Journal of Grid Computing 3(3–4), (2005). Springer, 1570–7873; 221–238. doi:10.1007/s10723-005-9012-6

  4. Ruvinsky, A.M.: Role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions. J. Comput. Chem. 28(8), 1364–1372 (2007)

    Article  Google Scholar 

  5. Teixeira, C., Barbault, F., et al.: Molecular modeling studies of N-substituted pyrrole derivatives-Potential HIV-1 gp41 inhibitors. Bioorg. Med. Chem. 16(6), 3039–3048 (2008)

    Article  Google Scholar 

  6. Morris, G.M., et al.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19(14), 1639–1662

  7. Case, D.A. et al.: The Amber bio-molecular simulation programs. J. Comput. Chem. 26, 1668–1688 (2005)

    Article  Google Scholar 

  8. MacKerell, A.D. et al.: CHARMM: the energy function and its parameterization with an overview of the program, in The Encyclopedia of Computational Chemistry. 1, 271–277, P. v. R. Schleyer et al., editors. John Wiley & Sons, Chichester (1998)

  9. Lindahl, E., et al.: GROMACS 3.0: a package for molecular simulation and trajectory analysis. Journal of Molecular Modeling 7, 306–317 (2001)

    Google Scholar 

  10. Bernstein, F., et al.: The protein data bank: a computer-based archival file for macromolecular structures. J. Mol. Biol. 112, 535–542 (1977)

    Article  Google Scholar 

  11. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000). http://www.rcsb.org/pdb/home/home.do

    Article  Google Scholar 

  12. Morris, A.L., MacArthur, M.W. et al.: Stereochemical quality of protein structure coordinates. Proteins 12(4), 345–364 (1992)

    Article  Google Scholar 

  13. Lovell, S.C., Davis, I.W. et al.: Structure validation by Calpha geometry: phipsi and Cbeta deviation. Proteins 50(3), 437–450 (2003)

    Article  Google Scholar 

  14. Ramachandran, G.N. et al.: Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7, 95–99 (1963)

    Article  Google Scholar 

  15. Kacsuk, P. et al.: WS-PGRADE: supporting parameter sweep applications in workflows, 3rd Workshop on Workflows in Support of Large-Scale Science, In conjunction with SC 2008, pp. 1–10. IEEE, Austin, TX, USA (2008). doi:10.1109/WORKS.2008.4723955

  16. Novotny, J., et al.: GridSphere: an advanced portal framework, Euromicro Conference, 2004. In: Proceedings. 30th Volume, Issue, pp. 412–419, 31 Aug.–3 Sept. 2004

  17. Kacsuk, P., et al.: Supporting dynamic parameter sweep applications in workflows—lessons learnt from the CancerGrid project, PARA ‘08, 9th International Workshop on State-of-the-Art in Scientific and Parallel Computing, Trondheim, Norway (2008)

  18. Zhang, X.L.: Roles of glycans and glycopeptides in immune system and immune-related diseases. Curr. Med. Chem. 13(10), 1141–1147 (2006)

    Article  Google Scholar 

  19. Crocker, P.R., Paulson, J.C., Varki, A.: Siglecs and their roles in the immune system. Nat Rev Immunol. 7(4), 255–266 (2007)

    Article  Google Scholar 

  20. Erbacher, A., Gieseke, F., Handgretinger, R., Müller, I.: Dendritic cells: functional aspects of glycosylation and lectins. Hum. Immunol. 70(5), 308–312 (2009)

    Article  Google Scholar 

  21. Reading, P.C., Tate, M.D., Pickett, D.L., Brooks, A.G.: Glycosylation as a target for recognition of influenza viruses by the innate immune system. Adv. Exp. Med. Biol. 598, 279 (2007)

    Article  Google Scholar 

  22. Hricovíni, M.: Structural aspects of carbohydrates and the relation with their biological properties. Curr. Med. Chem. 11(19), 2565–2583 (2004)

    Google Scholar 

  23. Davis, I.W., Chen, V.B.: The KiNG manual (2007). http://kinemage.biochem.duke.edu/software/king.php

  24. Delaittre, T. et al.: GEMLCA: running legacy code applications as Grid services. Journal of Grid Computing 3, 1–2 (2005). Springer, 1570–7873, pp. 75–90

    Google Scholar 

  25. Kiefer, F., Arnold, K., Künzli, M., Bordoli, L., Schwede, T.: The SWISS-MODEL repository and associated resources. Nucleic Acids Res. 37, D387–D392 (2009). http://swissmodel.expasy.org/

    Article  Google Scholar 

  26. Kenny, P.W., Sadowski, J.: Structure modification in chemical databases. In: Oprea, T. I. (ed.) Chemoinformatics in Drug Discovery, pp. 271–285. Wiley (2005)

  27. Weininger, D.: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988)

    Google Scholar 

  28. Tantoso, E. et al.: Molecular Docking, an example of Grid enabled applications. New Gener. Comput. 22(2) (2004). Page numbers

  29. Tantar, A.-A., et al.: A Grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing Journal, Springer Verlag, Special issue on Distributed Bioinspired Algorithms (2008)

  30. Roh, Y. et al.: A molecular docking system using CUDA, ACM International Conference Proceeding Series; In: Proceedings of the 2009 International Conference on Hybrid Information Technology, vol. 321, pp. 28–33, Daejeon, Korea (2009)

  31. The UK National Grid Service. http://www.ngs.ac.uk/

  32. Laure, E., Jones, B.: Enabling Grids for e-Science: The EGEE Project, Grid Computing: Infrastructure, Service, and Application. CRC Press (2008). http://www.eu-egee.org/

  33. Gibbins, H. et al.: The Australian BioGrid Portal: empowering the molecular docking research community. In: Proceedings of the 3rd APAC Conference and Exhibition on Advanced Computing, Grid Applications and eResearch (APAC 2005), Gold Coast, Australia, 26–30 Sept. 2005

  34. Ewing, A. (ed.): DOCK Version 4.0 Reference Manual. University of California at San Francisco (UCSF), U.S.A. (1998). http://www.cmpharm.ucsf.edu/kuntz/dock.html

  35. Foster, I.: Globus toolkit version 4: software for service-oriented systems. IFIP International Conference on Network and Parallel Computing. LNCS, vol. 3779, pp. 2–13. Springer-Verlag (2006). http://www.globus.org/

  36. Sukhwani, B., Herbordt, M.: GPU acceleration of a production molecular docking code. In: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units, ACM International Conference Proceeding Series, vol. 383, pp. 19–27 (2009)

  37. Friedrichs, M.S. et al.: Accelerating molecular dynamic simulation on graphics processing units. J. Comp. Chem. 30(6), 864–872 (2009)

    Article  Google Scholar 

  38. Buyya, R., et al.: The virtual laboratory: enabling molecular modelling for drug design on the world wide Grid, Concurrency and Computation: Practice and Experience (CCPE) Journal, vol. 15, Issue 1, pp. 1–25. Wiley Press, USA (2003)

  39. Tantar, A.-A., et al.: Docking and biomolecular simulations on computer Grids: status and trends, Current Computer-Aided Drug Design, vol. 4, No 3, pp. 235–249, Bentham Science Publishers (2008)

  40. Tantar, A.-A., Melab, N., Talbi, E.-G.: A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing Journal, Springer Verlag, Special issue on Distributed Bioinspired Algorithms (2008). doi:10.1007/s00500-008-0298-8

  41. The Chemomentum Project. http://www.chemomentum.org/c9m

  42. Schuller, B., et al.: Chemomentum—UNICORE 6 based infrastructure for complex applications in science and technology, Euro-Par 2007 Workshops: Parallel Processing, Lecture Notes in Computer Science, vol. 4854/2008, pp. 82–93, Springer (2008)

  43. Kluszczyński, R., Bała, P.: Supporting NAMD application on the Grid using GPE, In: Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science, vol. 4967, pp. 762–769, Springer (2008)

  44. Keator, D.B., Wei, D., Gadde, S., Bockholt, J., Grethe, J.S., Marcus, D., Aucoin, N., Ozyurt, I.B.: Derived data storage and exchange workflow for large-scale neuroimaging analyses on the BIRN grid. Frontiers in Neuroscience (2009, in press). http://www.birncommunity.org/

  45. Baru, C., et al.: The SDSC Storage Resource Broker. In: Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research, IBM press (1998)

  46. Anderson, D.P.: BOINC: a system for public-resource computing and storage. In: Fifth IEEE/ACM International Workshop on Grid Computing (GRID’04), pp. 4–10, IEE Computer Society (2004)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tamas Kiss.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kiss, T., Greenwell, P., Heindl, H. et al. Parameter Sweep Workflows for Modelling Carbohydrate Recognition. J Grid Computing 8, 587–601 (2010). https://doi.org/10.1007/s10723-010-9166-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-010-9166-8

Keywords

Navigation