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Data Mining Techniques for the Life Sciences pp 341–370Cite as

Protein Crystallizability

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1415))

Abstract

Obtaining diffracting quality crystals remains a major challenge in protein structure research. We summarize and compare methods for selecting the best protein targets for crystallization, construct optimization and crystallization condition design. Target selection methods are divided into algorithms predicting the chance of successful progression through all stages of structural determination (from cloning to solving the structure) and those focusing only on the crystallization step. We tried to highlight pros and cons of different approaches examining the following aspects: data size, redundancy and representativeness, overfitting during model construction, and results evaluation. In summary, although in recent years progress was made and several sequence properties were reported to be relevant for crystallization, the successful prediction of protein crystallization behavior and selection of corresponding crystallization conditions continue to challenge structural researchers.

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Authors and Affiliations

  1. Biomedical Center Munich, Ludwig-Maximilians-University, Großhaderner Strasse 9, 82152, Martinsried, Germany

    Pawel Smialowski & Philip Wong

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  1. Pawel Smialowski
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  2. Philip Wong
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Correspondence to Pawel Smialowski .

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  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

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Smialowski, P., Wong, P. (2016). Protein Crystallizability. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_17

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  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_17

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