Tables for
Volume F
Crystallography of biological macromolecules
Edited by E. Arnold, D. M. Himmel and M. G. Rossmann

International Tables for Crystallography (2012). Vol. F, ch. 4.3, pp. 134-135   | 1 | 2 |

Section 4.3.9. Surface-entropy reduction (SER)

Z. S. Derewendaa*

aDepartment of Molecular Physiology and Biological Physics, University of Virginia School of Medicine, Charlottesville, VA 22908–0736, USA
Correspondence e-mail:

4.3.9. Surface-entropy reduction (SER)

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For well ordered intrinsically stable proteins that show none of the problems addressed above, the propensity of the molecules to associate together and form crystals mediated by weak but specific interactions is ultimately defined by the physical chemistry and topology of the molecular surface. As already pointed out, large flexible amino acids on the surface, such as Lys, Glu and Gln, constitute an impediment to intermolecular interactions and consequently to protein crystallization (Cieślik & Derewenda, 2009[link]; Price et al., 2009[link]). In fact, it has been suggested that these residues and the `entropy shield' that they form play a role in protein evolution which, given the high average concentration of proteins in cells, disfavours protein–protein interactions unless they are bio­logically functional (Doye, 2004[link]). Thus, an intuitively obvious way to generate crystallizable variants is to replace selected large and surface-exposed residues with smaller residues such as alanine. This crystal-engineering strategy based on the surface-entropy reduction (SER) concept was extensively tested using as a model system the globular domain of the human Rho-specific guanine nucleotide-dissociation inhibitor (RhoGDI), which is recalcitrant to crystallization in its wild-type form owing to a high content of Lys and Glu residues, which constitute more than 20% of the sequence (Longe­necker, Garrard et al., 2001[link]; Mateja et al., 2002[link]; Derewenda, 2004[link]; Cooper et al., 2007[link]). These experiments established that in order to be most effective the SER strategy requires simultaneous mutations of clusters of two to three solvent-exposed high-entropy amino acids, typically Lys, Glu or Gln, located in close sequence proximity. These amino acids are replaced with alanine, although threonine and tyrosine, which is known to make a positive contribution at protein–protein interfaces such as antibody–antigen complexes (Fellouse et al., 2006[link]), can also be used (Cooper et al., 2007[link]). Engineered low-surface-entropy variants of RhoGDI produced new and unique crystal forms, many with superior diffraction quality when compared with the wild-type protein. Importantly, in the vast majority of these crystals the mutated surface patches mediated crystal contacts, suggesting that SER engineering directly drives crystallization in a rational fashion by creating suitable crystal-contact-forming interfaces. The general utility of the method was further established by the crystallization of several novel protein targets found to be recalcitrant to crystallization in their wild-type form (Longenecker, Lewis et al., 2001[link]; Derewenda et al., 2004[link]; Devedjiev et al., 2004[link]; Janda et al., 2004[link]).

SER is quickly becoming a method of choice for engineering crystallizable variants of both individual proteins and protein–protein complexes. To date (December 2009), there have already been more than 100 depositions made to the Protein Data Bank (Berman et al., 2007[link]) based on diffraction studies of crystals generated by SER and corresponding to 47 novel structures, seven novel protein–protein complexes, several studies of proteins in complexes with drug leads aimed at rational drug development and two membrane proteins. The current list of crystal structures obtained using SER crystals includes a number of cases of exceptionally high biological interest. For example, the EscJ protein from enteropathogenic E. coli, the oligomerization of which initiates assembly of the type III bacterial secretion system, was crystallized with three entropy-reducing mutations (E62A, K63A and E64A) forming a key contact in the crystal structure (Yip et al., 2005[link]). Likewise, the HIV CcmK4 capsid protein only crystallized after an entropy-reducing mutation (E104Y) was introduced into the protein (Pornillos et al., 2009[link]). The protein–protein complexes solved to date underscore the utility of the method, which extends beyond individual proteins because high-entropy patches occur outside complex interfaces. For example, the complex of c-Src and its inactivator Csk was crystallized using a variant of Csk carrying K361A and K362A mutations (Levinson et al., 2008[link]). Similarly, it was possible to crystallize the complex of two pseudo­pilins EpsI and EpsJ from the type 2 secretion system of Vibrio vulnificus when a variant of EpsI carrying two mutations (E128T and K129T) was used (Yanez et al., 2008[link]; Fig.[link]).


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Two examples of proteins crystallized by the surface-entropy reduction (SER) method. (a) The RGSL domain of the PDZRhoGEF nucleotide-exchange factor (PDB code 1htj ; Longenecker, Lewis et al., 2001[link]); the yellow spheres show the alanines introduced by mutagenesis, which mediate an isologous crystal contact across a crystallographic twofold axis. (b) The crystal structure of EpsI complexed with EpsJ (PDB code 2ret ; Yanez et al., 2008[link]); the EpsI protein (pale blue) contains two surface mutations, shown by yellow spheres, which mediate heterologous crystal contacts.

An interesting variation of the SER method was used in the investigation of the RACK1 protein, which was crystallized as an in-line fusion with an MBP variant carrying D82A, K83A and K239A mutations (Ullah et al., 2008[link]). This is the first example of the application of the surface-entropy reduction strategy to a carrier protein and not the crystallization target itself.

The SER strategy is attractive not only because of its efficacy but also because of its simplicity: once an expression construct for a target protein is available several rounds of mutagenesis can easily create variants with systematically enhanced crystallizability. To assist in the design of crystallizable variants, a server has been developed that uses the amino-acid sequence of the target to identify suitable mutation sites (Goldschmidt et al., 2007[link]).


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