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Computational Methods for Protein Crystallization Screening

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Data Analytics for Protein Crystallization

Part of the book series: Computational Biology ((COBO,volume 25))

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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.

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Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    https://github.com/ubccr/cockatoo/.

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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.

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Authors and Affiliations

  1. iXpressGenes, Inc., Huntsville, AL, USA

    Marc L. Pusey

  2. University of Alabama in Huntsville, Huntsville, AL, USA

    Ramazan Savaş Aygün

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Correspondence to Marc L. Pusey .

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Pusey, M.L., Aygün, R.S. (2017). Computational Methods for Protein Crystallization Screening. In: Data Analytics for Protein Crystallization. Computational Biology, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-58937-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-58937-4_3

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  • Online ISBN: 978-3-319-58937-4

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