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Predicting protein crystallization propensity from protein sequence

  • Published:
Journal of Structural and Functional Genomics

Abstract

The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures. The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein’s propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site http://bioinformatics.anl.gov/cgi-bin/tools/pdpredictor.

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Abbreviations

GRAVY:

Grand average hydropathy

MCSG:

Midwest Center for Structural Genomics

pI:

Iso-electric point

PSI:

Protein Structure Initiative

SVM:

Support vector machine

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Acknowledgments

This work was supported by the National Institutes of Health grant GM074942 and by the U.S. Department of Energy, Office of Biological and Environmental Research, under contract DE-AC02-06CH11357.

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Authors and Affiliations

  1. Midwest Center for Structural Genomics, Biosciences Division, Argonne National Laboratory, 9700 S Cass Ave., Argonne, IL, 60439, USA

    György Babnigg & Andrzej Joachimiak

Authors
  1. György Babnigg
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  2. Andrzej Joachimiak
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Correspondence to György Babnigg or Andrzej Joachimiak.

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The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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Babnigg, G., Joachimiak, A. Predicting protein crystallization propensity from protein sequence. J Struct Funct Genomics 11, 71–80 (2010). https://doi.org/10.1007/s10969-010-9080-0

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  • DOI: https://doi.org/10.1007/s10969-010-9080-0

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