Advertisement

Computational Methods for Protein Crystallization Screening

Chapter
  • 606 Downloads
Part of the Computational Biology book series (COBO, volume 25)

Abstract

The goal of protein crystallization screening is to determine the main factors of importance to crystallize a protein under investigation. The protein crystallization screening is often expanded to many hundreds or thousands of conditions to maximize combinatorial chemical space coverage for maximizing the chances of a successful (crystalline) outcome. Available commercial screens may not generate crystalline conditions for some proteins difficult to crystallize. Nevertheless, the previous crystallization trials could be analyzed to recommend screens with crystalline conditions. This chapter presents computational methods for protein crystallization screening.

Notes

Acknowledgements

The the first and second paragraphs (except the first sentences) of Sect. 3.3 are Reprinted from Progress in Biophysics and Molecular Biology, Volume 88, Issue 3, Lawrence J. DeLucas, David Hamrick, Larry Cosenza, Lisa Nagy, Debbie McCombs, Terry Bray, Arnon Chait, Brad Stoops, Alexander Belgovskiy, W. William Wilson, Marc Parham, Nikolai Chernov, Protein crystallization: virtual screening and optimization, Pages 285–309, Copyright (2005) with permission from Elsevier.

      The second paragraph (except the first two sentences) and the third paragraph of Sect. 3.4 are Reprinted (adapted) with permission from Crystal Growth and Design 2011 11 (7), Emmanuel Saridakis, Novel Genetic Algorithm-Inspired Concept for Macromolecular Crystal Optimization, 2993–2998. Copyright (2011) American Chemical Society. \(\copyright \)2016 IEEE. Reprinted, with permission, from I. Dinç, M. L. Pusey, and R. S. Aygün, “Optimizing Associative Experimental Design for Protein Crystallization Screening,” in IEEE Transactions on NanoBioscience, vol. 15, no. 2, pp. 101–112, March 2016. doi: https://doi.org/10.1109/TNB.2016.2536030.

References

  1. 1.
    Hampton Research Screen HT. https://hamptonresearch.com/documents/product/hr000783_crystal_screen_2.xls. Accessed 1 November 2015.
  2. 2.
    Microlytics MCSG-3 Screen. http://www.microlytic.com/sites/default/files/MCSG3_Formulations_0_0_0.pdf. Accessed 1 November 2015.
  3. 3.
    Molecular Dynamics JCGS+ Screen. http://www.moleculardimensions.com/applications/upload/Md1-40%20JCSG%20Plus%20HT-96.pdf. Accessed 1 November 2015.
  4. 4.
    Abergel, C., Moulard, M., Moreau, H., Loret, E., Cambillau, C., & Fontecilla-Camps, J. C. (1991). Systematic use of the incomplete factorial approach in the design of protein crystallization experiments. Journal of Biological Chemistry, 266(30), 20131–20138.Google Scholar
  5. 5.
    Asenjo, J. A., & Andrews, B. A. (2011). Aqueous two-phase systems for protein separation: a perspective. Journal of Chromatography A, 1218(49), 8826–8835.CrossRefGoogle Scholar
  6. 6.
    Asenjo, J. A., & Andrews, B. A. (2012). Aqueous two-phase systems for protein separation: phase separation and applications. Journal of Chromatography A, 1238, 1–10.CrossRefGoogle Scholar
  7. 7.
    Bray, J. R., & Curtis, J. T. (1957). An ordination of the upland forest communities of southern wisconsin. Ecological Monographs, 27(4), 325–349.CrossRefGoogle Scholar
  8. 8.
    Brodersen, D. E., Andersen, G. R., & Andersen, C. B. F. (2013). Mimer: an automated spreadsheet-based crystallization screening system. Acta Crystallographica Section F, 69(7), 815–820.Google Scholar
  9. 9.
    Bruno, A.E., Ruby, A.M., Luft, J.R., Grant, T.D., Seetharaman, J., Montelione, G.T., Hunt, J.F., and Snell, E.H. Comparing chemistry to outcome: the development of a chemical distance metric, coupled with clustering and hierarchal visualization applied to macromolecular crystallography.Google Scholar
  10. 10.
    Carter, C. W, Jr., & Carter, C. W. (1979). Protein crystallization using incomplete factorial experiments. The Journal of Biological Chemistry, 254(23), 12219–12223.Google Scholar
  11. 11.
    D’Arcy, A., Bergfors, T., Cowan-Jacob, S. W., & Marsh, M. (2014). Microseed matrix screening for optimization in protein crystallization: what have we learned? Acta Crystallographica Section F: Structural Biology Communications, 70(9), 1117–1126.Google Scholar
  12. 12.
    DeLucas, L. J., Hamrick, D., Cosenza, L., Nagy, L., McCombs, D., Bray, T., et al. (2005). Protein crystallization: virtual screening and optimization. Progress in Biophysics and Molecular Biology, 88(3), 285–309.CrossRefGoogle Scholar
  13. 13.
    Dinc, I. (2016). Associtiave Data Analytics and its Application to Protein Crystallization Analysis. Ph.D dissertation, University of Alabama in Huntsville.Google Scholar
  14. 14.
    Dinç, İ., Pusey, M.L., and Aygün, R.S. (2015). Protein crystallization screening using associative experimental design. In Bioinformatics Research and Applications (pp. 84–95). Springer.Google Scholar
  15. 15.
    Dinç, İ., Pusey, M. L., & Aygün, R. S. (2016). Optimizing Associative Experimental Design for Protein Crystallization Screening. IEEE Transactions on NanoBioscience, 15(2), 101–112.CrossRefGoogle Scholar
  16. 16.
    Doudna, J. A., Grosshans, C., Gooding, A., & Kundrot, C. E. (1993). Crystallization of ribozymes and small rna motifs by a sparse matrix approach. Proceedings of the National Academy of Sciences, 90(16), 7829–7833.CrossRefGoogle Scholar
  17. 17.
    Forsythe, E., Achari, A., & Pusey, M. L. (2006). Trace fluorescent labeling for high-throughput crystallography. Acta Crystallographica Section D: Biological Crystallography, 62(3), 339–346.CrossRefGoogle Scholar
  18. 18.
    Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M.R., Appel, R.D., and Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server. Springer.Google Scholar
  19. 19.
    Giegé, R. (2013). A historical perspective on protein crystallization from 1840 to the present day. FEBS Journal, 280(24), 6456–6497.CrossRefGoogle Scholar
  20. 20.
    Jancarik, J., & Kim, S.-H. (1991). Sparse matrix sampling: a screening method for crystallization of proteins. Journal of Applied Crystallography, 24(4), 409–411.CrossRefGoogle Scholar
  21. 21.
    Kwon, J. S.-I., Nayhouse, M., Christofides, P. D., & Orkoulas, G. (2013). Modeling and control of protein crystal shape and size in batch crystallization. AIChE Journal, 59(7), 2317–2327.CrossRefGoogle Scholar
  22. 22.
    Luft, J. R., Newman, J., & Snell, E. H. (2014). Crystallization screening: the influence of history on current practice. Structural Biology and Crystallization Communications, 70(7), 835–853.CrossRefGoogle Scholar
  23. 23.
    McPherson, A., & Cudney, B. (2014). Optimization of crystallization conditions for biological macromolecules. Structural Biology and Crystallization Communications, 70(11), 1445–1467.CrossRefGoogle Scholar
  24. 24.
    McPherson, A., & Gavira, J. A. (2014). Introduction to protein crystallization. Acta Crystallographica Section F: Structural Biology Communications, 70(1), 2–20.CrossRefGoogle Scholar
  25. 25.
    Newman, J., Fazio, V. J., Lawson, B., & Peat, T. S. (2010). The c6 web tool: a resource for the rational selection of crystallization conditions. Crystal Growth and Design, 10(6), 2785–2792.CrossRefGoogle Scholar
  26. 26.
    Ng, J. D., Gavira, J. A., & García-Ruíz, J. M. (2003). Protein crystallization by capillary counterdiffusion for applied crystallographic structure determination. Journal of structural biology, 142(1), 218–231.CrossRefGoogle Scholar
  27. 27.
    Petersen, B., Petersen, T. N., Andersen, P., Nielsen, M., & Lundegaard, C. (2009). A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC structural biology, 9(1), 1.CrossRefGoogle Scholar
  28. 28.
    Pikuta, E. V., Marsic, D., Itoh, T., Bej, A. K., Tang, J., Whitman, W. B., et al. (2007). Thermococcus thioreducens sp. nov., a novel hyperthermophilic, obligately sulfur-reducing archaeon from a deep-sea hydrothermal vent. International Journal of Systematic and Evolutionary Microbiology, 57(7), 1612–1618.CrossRefGoogle Scholar
  29. 29.
    Pusey, M., Barcena, J., Morris, M., Singhal, A., Yuan, Q., & Ng, J. (2015). Trace fluorescent labeling for protein crystallization. Structural Biology and Crystallization Communications, 71, 7.Google Scholar
  30. 30.
    Raja, S., Murty, V. R., Thivaharan, V., Rajasekar, V., & Ramesh, V. (2011). Aqueous two phase systems for the recovery of biomolecules-a review. Science and Technology, 1(1), 7–16.CrossRefGoogle Scholar
  31. 31.
    Saridakis, E. (2011). Novel Genetic Algorithm-Inspired Concept for Macromolecular Crystal Optimization. Crystal Growth and Design, 11(7), 2993–2998.CrossRefGoogle Scholar
  32. 32.
    Sigdel, M., Pusey, M. L., & Aygun, R. S. (2013). Real-time protein crystallization image acquisition and classification system. Crystal Growth and Design, 13(7), 2728–2736.CrossRefGoogle Scholar
  33. 33.
    Snell, E. H., Nagel, R. M., Wojtaszcyk, A., O’Neill, H., Wolfley, J. L., & Luft, J. R. (2008). The application and use of chemical space mapping to interpret crystallization screening results. Acta Crystallographica Section D: Biological Crystallography, 64(12), 1240–1249.CrossRefGoogle Scholar
  34. 34.
    Stevens, R. C. (2000). High-throughput protein crystallization. Current Opinion in Structural Biology, 10(5), 558–563.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.iXpressGenes, Inc.HuntsvilleUSA
  2. 2.University of Alabama in HuntsvilleHuntsvilleUSA

Personalised recommendations