Advertisement

Journal of Grid Computing

, Volume 10, Issue 4, pp 743–767 | Cite as

WeNMR: Structural Biology on the Grid

  • Tsjerk A. Wassenaar
  • Marc van Dijk
  • Nuno Loureiro-Ferreira
  • Gijs van der Schot
  • Sjoerd J. de Vries
  • Christophe Schmitz
  • Johan van der Zwan
  • Rolf Boelens
  • Andrea Giachetti
  • Lucio Ferella
  • Antonio Rosato
  • Ivano Bertini
  • Torsten Herrmann
  • Hendrik R. A. Jonker
  • Anurag Bagaria
  • Victor Jaravine
  • Peter Güntert
  • Harald Schwalbe
  • Wim F. Vranken
  • Jurgen F. Doreleijers
  • Gert Vriend
  • Geerten W. Vuister
  • Daniel Franke
  • Alexey Kikhney
  • Dmitri I. Svergun
  • Rasmus H. Fogh
  • John Ionides
  • Ernest D. Laue
  • Chris Spronk
  • Simonas Jurkša
  • Marco Verlato
  • Simone Badoer
  • Stefano Dal Pra
  • Mirco Mazzucato
  • Eric Frizziero
  • Alexandre M. J. J. Bonvin
Open Access
Article
First Online:

Abstract

The WeNMR (http://www.wenmr.eu) project is a European Union funded international effort to streamline and automate analysis of Nuclear Magnetic Resonance (NMR) and Small Angle X-Ray scattering (SAXS) imaging data for atomic and near-atomic resolution molecular structures. Conventional calculation of structure requires the use of various software packages, considerable user expertise and ample computational resources. To facilitate the use of NMR spectroscopy and SAXS in life sciences the WeNMR consortium has established standard computational workflows and services through easy-to-use web interfaces, while still retaining sufficient flexibility to handle more specific requests. Thus far, a number of programs often used in structural biology have been made available through application portals. The implementation of these services, in particular the distribution of calculations to a Grid computing infrastructure, involves a novel mechanism for submission and handling of jobs that is independent of the type of job being run. With over 450 registered users (September 2012), WeNMR is currently the largest Virtual Organization (VO) in life sciences. With its large and worldwide user community, WeNMR has become the first Virtual Research Community officially recognized by the European Grid Infrastructure (EGI).

Keywords

Web portals Nuclear magnetic resonance Small angle x-ray scattering Structural biology  Proteins Virtual research community 
Download to read the full article text

References

  1. 1.
    Bloch, F.: Nuclear induction. Phys. Rev. 70, 460 (1946)CrossRefGoogle Scholar
  2. 2.
    Purcell, E.M., Torrey, H.C., Pound, R.V.: Resonance absorption by nuclear magnetic moments in a solid. Phys. Rev. 69, 37 (1946)CrossRefGoogle Scholar
  3. 3.
    Bonvin, A.M.J.J., Rosato, A., Wassenaar, T.A.: The eNMR platform for structural biology. J. Struct. Funct. Genomics 11, 1–8 (2010)CrossRefGoogle Scholar
  4. 4.
    Feigin, L.A., Svergun, D.I.: Structure Analysis by Small-angle X-ray and Neutron Scattering. Plenum Press, New York (1987)Google Scholar
  5. 5.
    Mertens, H.D.T., Svergun, D.I.: Structural characterization of proteins and complexes using small-angle X-ray solution scattering. J. Struct. Biol. 172, 128–141 (2010)CrossRefGoogle Scholar
  6. 6.
    Morgan, H.P., et al.: Structural basis for engagement by complement factor H of C3b on a self surface. Nat. Struct. Mol. Biol. 18, 463–470 (2011)CrossRefGoogle Scholar
  7. 7.
    Prischi, F., et al.: Structural bases for the interaction of frataxin with the central components of iron-sulphur cluster assembly. Nat. Commun. 1, 95 (2010)CrossRefGoogle Scholar
  8. 8.
    Gabel, F., et al.: A structure refinement protocol combining NMR residual dipolar couplings and small angle scattering restraints. J. Biomol. NMR 41, 199–208 (2008)CrossRefGoogle Scholar
  9. 9.
    Grishaev, A., Tugarinov, V., Kay, L.E., Trewhella, J., Bax, A.: Refined solution structure of the 82-kDa enzyme malate synthase G from joint NMR and synchrotron SAXS restraints. J. Biomol. NMR 40, 95–106 (2008)CrossRefGoogle Scholar
  10. 10.
    Grishaev, A., Wu, J., Trewhella, J., Bax, A.: Refinement of multidomain protein structures by combination of solution small-angle X-ray scattering and NMR data. J. Am. Chem. Soc. 127, 16621–16628 (2005)CrossRefGoogle Scholar
  11. 11.
    Bernado, P., Svergun, D.I.: Structural insights into intrinsically disordered proteins by small-angle X-ray scattering. In: Uversky, V.N., Longhi, S. (eds.) Instrumental Analysis of Intrinsically Disordered Proteins: Assessing Structure and Conformation, pp. 451–476 Wiley, Hoboken (2010)CrossRefGoogle Scholar
  12. 12.
    Ferrari, T., Gaido, L.: Resources and services of the EGEE production infrastructure. J. Grid Computing 9, 119–133 (2011)CrossRefGoogle Scholar
  13. 13.
    Avellino, G., et al.: The dataGrid workload management system: challenges and results. J. Grid Computing 2, 353–367 (2004)CrossRefGoogle Scholar
  14. 14.
    Wilkins-Diehr, N.: Special issue: science gateways—common community interfaces to Grid resources: editorials. Concurr. Comput.: Pract. Exp. 19, 743–749 (2007)CrossRefGoogle Scholar
  15. 15.
    Fargette, M., Barbera, R., Rotondo, R.: A simplified access to Grid resources by science gateways. In: International Symposium on Grids and Clouds and the Open Grid Forum. Taipei (2011)Google Scholar
  16. 16.
    Farkas, Z., Kacsuk, P.: P-GRADE portal: a generic workflow system to support user communities. Future Gener. Comput. Syst. 27, 454–465 (2011)CrossRefGoogle Scholar
  17. 17.
    Kacsuk, P.: P-GRADE portal family for Grid infrastructures. Concurr. Comput.: Pract. Exp. 23, 235–245 (2011)CrossRefGoogle Scholar
  18. 18.
    Barbera, R., Falzone, A., Ardizzone, V., Scardaci, D.: The GENIUS Grid portal: its architecture, improvements of features, and new implementations about authentication and authorization. In: Proceedings of the 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 279–283. IEEE Computer Society, (2007)Google Scholar
  19. 19.
    Zhang, C., Kelley, I., Allen, G.: Grid portal solutions: a comparison of GridPortlets and OGCE. Concurr. Comput.: Pract. Exp. 19, 1739–1748 (2007)CrossRefGoogle Scholar
  20. 20.
    Szejnfeld, D., et al.: Vine toolkit—towards portal based production solutions for scientific and engineering communities with Grid-enabled resources support. Scalable Comput.: Pract. Exp. 11, 161–172 (2010)Google Scholar
  21. 21.
    Hull, D., et al.: Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 34, W729–W732 (2006)CrossRefGoogle Scholar
  22. 22.
    De Vries, S.J., et al.: HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets. Proteins 69, 726–733 (2007)CrossRefGoogle Scholar
  23. 23.
    Dominguez, C., Boelens, R., Bonvin, A.M.J.J.: HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125, 1731–1737 (2003)CrossRefGoogle Scholar
  24. 24.
    Schwieters, C.D., Kuszewski, J.J., Tjandra, N., Clore, G.M.: The Xplor-NIH NMR molecular structure determination package. J. Magn. Reson. 160, 65–73 (2003)CrossRefGoogle Scholar
  25. 25.
    Güntert, P., Mumenthaler, C., Wüthrich, K.: Torsion angle dynamics for NMR structure calculation with the new program DYANA. J. Mol. Biol. 273, 283–298 (1997)CrossRefGoogle Scholar
  26. 26.
    Herrmann, T., Güntert, P., Wüthrich, K.: Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. J. Mol. Biol. 319, 209–227 (2002)CrossRefGoogle Scholar
  27. 27.
    Shen, Y., et al.: Consistent blind protein structure generation from NMR chemical shift data. Proc. Natl. Acad. Sci. USA. 105, 4685–4690 (2008)CrossRefGoogle Scholar
  28. 28.
    Shen, Y., Vernon, R., Baker, D., Bax, A.: De novo protein structure generation from incomplete chemical shift assignments. J. Biomol. NMR 43, 63–78 (2009)CrossRefGoogle Scholar
  29. 29.
    Case, D.A., et al.: The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668–1688 (2005)CrossRefGoogle Scholar
  30. 30.
    Van Der Spoel, D., et al.: GROMACS: fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005)CrossRefGoogle Scholar
  31. 31.
    Vranken, W.F., et al.: The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins 59, 687–696 (2005)CrossRefGoogle Scholar
  32. 32.
    Jung, Y.S., Zweckstetter, M.: Mars—robust automatic backbone assignment of proteins. J. Biomol. NMR 30, 11–23 (2004)CrossRefGoogle Scholar
  33. 33.
    Shen, Y., Delaglio, F., Cornilescu, G., Bax, A.: TALOS plus: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J. Biomol. NMR 44, 213–223 (2009)CrossRefGoogle Scholar
  34. 34.
    Jaravine, V.A., Zhuravleva, A.V., Permi, P., Ibraghimov, I., Orekhov, V.Y.: Hyperdimensional NMR spectroscopy with nonlinear sampling. J. Am. Chem. Soc. 130, 3927–3936 (2008)CrossRefGoogle Scholar
  35. 35.
    Petoukhov, M.V., Konarev, P.V., Kikhney, A.G., Svergun, D.I.: ATSAS 2.1—towards automated and web-supported small-angle scattering data analysis. J. Appl. Crystallogr. 40, S223–S228 (2007)CrossRefGoogle Scholar
  36. 36.
    Fiorito, F., Herrmann, T., Damberger, F.F., Wüthrich, K.: Automated amino acid side-chain NMR assignment of proteins using 13C- and 15N-resolved 3D [ 1H, 1H]-NOESY. J. Biomol. NMR 42, 23–33 (2008)CrossRefGoogle Scholar
  37. 37.
    Volk, J., Herrmann, T., Wüthrich, K.: Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH. J. Biomol. NMR 41, 127–138 (2008)CrossRefGoogle Scholar
  38. 38.
    Lensink, M.F., Mendez, R., Wodak, S.J.: Docking and scoring protein complexes: CAPRI 3rd edition. Proteins 69, 704–718 (2007)CrossRefGoogle Scholar
  39. 39.
    Mendez, R., Leplae, R., Lensink, M.F., Wodak, S.J.: Assessment of CAPRI predictions in rounds 3–5 shows progress in docking procedures. Proteins 60, 150–169 (2005)CrossRefGoogle Scholar
  40. 40.
    van Dijk, A.D., et al.: Data-driven docking: HADDOCK’s adventures in CAPRI. Proteins 60, 232–238 (2005)CrossRefGoogle Scholar
  41. 41.
    Schwieters, C.D., Kuszewski, J.J., Clore, G.M.: Using Xplor-NIH for NMR molecular structure determination. Prog. NMR Spectrosc. 48, 47–62 (2006)CrossRefGoogle Scholar
  42. 42.
    Berman, H.M., et al.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)CrossRefGoogle Scholar
  43. 43.
    Rohl, C.A., Strauss, C.E., Misura, K.M., Baker, D.: Protein structure prediction using Rosetta. Methods Enzymol. 383, 66–93 (2004)CrossRefGoogle Scholar
  44. 44.
    Shen, Y., Bax, A.: Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J. Biomol. NMR 38, 289–302 (2007)CrossRefGoogle Scholar
  45. 45.
    Cornilescu, G., Delaglio, F., Bax, A.: Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J. Biomol. NMR 13, 289–302 (1999)CrossRefGoogle Scholar
  46. 46.
    McGuffin, L.J., Bryson, K., Jones, D.T.: The PSIPRED protein structure prediction server. Bioinformatics 16, 404–405 (2000)CrossRefGoogle Scholar
  47. 47.
    Wang, Y.J., Jardetzky, O.: Investigation of the neighboring residue effects on protein chemical shifts. J. Am. Chem. Soc. 124, 14075–14084 (2002)CrossRefGoogle Scholar
  48. 48.
    Berjanskii, M.V., Wishart, D.S.: A simple method to predict protein flexibility using secondary chemical shifts. J. Am. Chem. Soc. 127, 14970–14971 (2005)CrossRefGoogle Scholar
  49. 49.
    Ulrich, E.L., et al.: BioMagResBank. Nucleic Acids Res. 36, D402–D408 (2008)CrossRefGoogle Scholar
  50. 50.
    Fogh, R.H., et al.: MEMOPS: data modelling and automatic code generation. J. Integr. Bioinform. 7, 123 (2010)Google Scholar
  51. 51.
    Bertini, I., Case, D.A., Ferella, L., Giachetti, A., Rosato, A.: A Grid-enabled web portal for NMR structure refinement with AMBER. Bioinformatics 27, 2384–2390 (2011)CrossRefGoogle Scholar
  52. 52.
    Lindahl, E., Hess, B., van der Spoel, D.: GROMACS 3.0: a package for molecular simulation and trajectory analysis. J. Mol. Model. 7, 306–317 (2001)Google Scholar
  53. 53.
    Oostenbrink, C., Villa, A., Mark, A.E., Van Gunsteren, W.F.: A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem. 25, 1656–1676 (2004)CrossRefGoogle Scholar
  54. 54.
    Schuler, L.D., Daura, X., Van Gunsteren, W.F.: An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J. Comput. Chem. 22, 1205–1218 (2001)CrossRefGoogle Scholar
  55. 55.
    Scott, W.R.P., et al.: The GROMOS biomolecular simulation program package. J. Phys. Chem. A 103, 3596–3607 (1999)CrossRefGoogle Scholar
  56. 56.
    van Gunsteren, W.F., et al.: Biomolecular Simulation: The Gromos 96 Manual and User Guide. BIOMOS BV, Zürich, Groningen (1996)Google Scholar
  57. 57.
    Ponder, J.W., Case, D.A.: Force fields for protein simulations. Adv. Protein Chem. 66, 27–85 (2003)CrossRefGoogle Scholar
  58. 58.
    Brooks, B.R., et al.: Charmm—a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4, 187–217 (1983)CrossRefGoogle Scholar
  59. 59.
    Jorgensen, W.L., Maxwell, D.S., TiradoRives, J.: Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118, 11225–11236 (1996)CrossRefGoogle Scholar
  60. 60.
    Van Dijk, M., Wassenaar, T., Bonvin, A.M.J.J.: A flexible, Grid-enabled web portal for GROMACS molecular dynamics simulations. J. Chem. Theory Comput. (2012). doi: 10.1021/ct300102d Google Scholar
  61. 61.
    Villa, A., Fan, H., Wassenaar, T., Mark, A.E.: How sensitive are nanosecond molecular dynamics simulations of proteins to changes in the force field? J. Phys. Chem. B 111, 6015–6025 (2007)CrossRefGoogle Scholar
  62. 62.
    Wassenaar, T.A., Mark, A.E.: The effect of box shape on the dynamic properties of proteins simulated under periodic boundary conditions. J. Comput. Chem. 27, 316–325 (2006)CrossRefGoogle Scholar
  63. 63.
    De Vries, S.J., van Dijk, M., Bonvin, A.M.J.J.: The HADDOCK web server for data-driven biomolecular docking. Nat. Protoc. 5, 883–897 (2010)CrossRefGoogle Scholar
  64. 64.
    Herrmann, T., Güntert, P., Wüthrich, K.: Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS. J. Biomol. NMR 24, 171–189 (2002)CrossRefGoogle Scholar
  65. 65.
    Doreleijers, J.F., Sousa da Silva, A.W., Krieger, E., Nabuurs, S.B., Spronk, C.A.E.M., Stevens, T.J., Vranken, W.F., Vriend, G., Vuister, G.W.: CING: an integrated residue-based structure validation program suite. J. Biomol. NMR 54, 267–283 (2012)CrossRefGoogle Scholar
  66. 66.
    Doreleijers, J.F., et al.: NRG-CING: integrated validation reports of remediated experimental biomolecular NMR data and coordinates in wwPDB. Nucleic Acids Res. (2011). doi: 10.1093/nar/gkr1134 Google Scholar
  67. 67.
    Franke, D., Svergun, D.I.: DAMMIF, a program for rapid ab-initio shape determination in small-angle scattering. J. Appl. Crystallogr. 42, 342–346 (2009)CrossRefGoogle Scholar
  68. 68.
    Volkov, V.V., Svergun, D.I.: Uniqueness of ab initio shape determination in small-angle scattering. J. Appl. Crystallogr. 36, 860–864 (2003)CrossRefGoogle Scholar
  69. 69.
    Svergun, D.I.: Restoring low resolution structure of biological macromolecules from solution scattering using simulated annealing. Biophys. J. 76, 2879–2886 (1999)CrossRefGoogle Scholar
  70. 70.
    Svergun, D.I., Petoukhov, M.V., Koch, M.H.: Determination of domain structure of proteins from X-ray solution scattering. Biophys. J. 80, 2946–2953 (2001)CrossRefGoogle Scholar
  71. 71.
    Bradley, D., Sfiligoi, I., Padhi, S., Frey, J., Tannenbaum, T.: Scalability and interoperability within glideinWMS. J. Phys.: Conf. Ser. 219, 062036 (2010)CrossRefGoogle Scholar
  72. 72.
    Verlato, M.: Extending WeNMR e-Infrastructure outside Europe. In: EGI Community Forum 2012/EMI Second Technical Conference, Munich (2012)Google Scholar
  73. 73.
    Bertini, I., et al.: Conformational space of flexible biological macromolecules from average data. J. Am. Chem. Soc. 132, 13553–13558 (2010)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Tsjerk A. Wassenaar
    • 1
    • 14
  • Marc van Dijk
    • 1
  • Nuno Loureiro-Ferreira
    • 1
    • 15
  • Gijs van der Schot
    • 1
  • Sjoerd J. de Vries
    • 1
  • Christophe Schmitz
    • 1
  • Johan van der Zwan
    • 1
  • Rolf Boelens
    • 1
  • Andrea Giachetti
    • 2
  • Lucio Ferella
    • 2
  • Antonio Rosato
    • 2
  • Ivano Bertini
    • 2
  • Torsten Herrmann
    • 3
  • Hendrik R. A. Jonker
    • 4
  • Anurag Bagaria
    • 5
  • Victor Jaravine
    • 5
  • Peter Güntert
    • 5
  • Harald Schwalbe
    • 4
  • Wim F. Vranken
    • 6
    • 16
  • Jurgen F. Doreleijers
    • 7
  • Gert Vriend
    • 8
  • Geerten W. Vuister
    • 9
  • Daniel Franke
    • 10
  • Alexey Kikhney
    • 10
  • Dmitri I. Svergun
    • 10
  • Rasmus H. Fogh
    • 11
  • John Ionides
    • 11
  • Ernest D. Laue
    • 11
  • Chris Spronk
    • 12
  • Simonas Jurkša
    • 12
  • Marco Verlato
    • 13
  • Simone Badoer
    • 13
  • Stefano Dal Pra
    • 13
    • 17
  • Mirco Mazzucato
    • 13
  • Eric Frizziero
    • 13
  • Alexandre M. J. J. Bonvin
    • 1
  1. 1.Bijvoet Center for Biomolecular Research, Faculty of ScienceUtrecht UniversityUtrechtThe Netherlands
  2. 2.Magnetic Resonance CenterUniversity of FlorenceSesto FiorentinoItaly
  3. 3.Centre de RMN à très Hauts Champs, Institut des Sciences AnalytiquesUniversité de LyonVilleurbanneFrance
  4. 4.Institute of Organic Chemistry and Chemical Biology and Biomolecular Magnetic Resonance CenterGoethe University FrankfurtFrankfurt am MainGermany
  5. 5.Institute of Biophysical Chemistry and Biomolecular Magnetic Resonance CenterGoethe University FrankfurtFrankfurt am MainGermany
  6. 6.European Bioinformatics InstituteCambridgeUK
  7. 7.Protein Biophysics/IMMRadboud University NijmegenNijmegenThe Netherlands
  8. 8.CMBIRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
  9. 9.Department of Biochemistry, School of Biological Sciences, Henry Wellcome BuildingUniversity of LeicesterLeicesterUK
  10. 10.European Molecular Biology Laboratory, Hamburg OutstationHamburgGermany
  11. 11.Department of BiochemistryUniversity of CambridgeCambridgeUK
  12. 12.UAB “Spronk NMR Consultancy”VilniusLithuania
  13. 13.Istituto Nazionale di Fisica NuclearePadovaItaly
  14. 14.Biocomputing Group, Department of Biological SciencesUniversity of CalgaryCalgaryCanada
  15. 15.European Grid Infrastructure (EGI)AmsterdamThe Netherlands
  16. 16.Department of Structural Biology, VIB, and Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
  17. 17.Istituto Nazionale di Fisica NucleareCNAFBolognaItaly

Personalised recommendations