Figure 5 - uploaded by Yancho Devedjiev
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Eectron-density maps. (a) At the region of crystal contact involving the mutated sites at 154 and 155. (b) At the region of crystal contact involving Tyr149 and Tyr156, and Val67 from the adjacent subunit. The ®gure was prepared using BOBSCRIPT (Esnouf, 1997).
Source publication
It is hypothesized that surface residues with high conforma-tional entropy, speci®cally lysines and glutamates, impede protein crystallization. In a previous study using a model system of Rho-speci®c guanine nucleotide dissociation inhibitor (RhoGDI), it was shown that mutating Lys residues to Ala results in enhanced crystallizability, particularly...
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Context 1
... particular interest is the packing arrangement of the protein molecules in the triclinic crystal form reported here, revealing the importance of the point mutations for the crystal lattice. The Glu3Ala mutations of residues 154 and 155 allow the side chain of Arg117 to adopt a novel conformation favorable for intermolecular packing (Fig. 5a). The guanidi- nium group of Arg117 interacts closely with the side chains of Ser124 and Ser148 of a neighboring molecule, forming the central portion of the interface between molecules A and B. Adjacent to this interaction, the N-terminus of molecule A lays against molecule B, forming a hydrogen bond between the main-chain atoms ValA67 ...
Context 2
... a neighboring molecule, forming the central portion of the interface between molecules A and B. Adjacent to this interaction, the N-terminus of molecule A lays against molecule B, forming a hydrogen bond between the main-chain atoms ValA67 NH and CO of GluB157 (3.1 A Ê ), with the side chain of Val67 ®tting into a hydrophobic pocket of molecule B (Fig. 5b). Ordered solvent molecules provide additional bridging interactions across this interface. The packing within the lattice is such that the interactions of molecule A with molecule B are repeated for molecule B interacting with a crystallographically related copy of mole- cule A translated by one unit-cell ...
Citations
... This result of AlaRCys can be well recognized from the analysis of Table 6. The conformational entropy reduction of surface residues in the surface entropy reduction strategy is considered as a main reason for the XRAla mutation [23,24,[27][28][29][30][31][32][33][34] where Ala has the lowest conformational entropy. The amino acids Glu and Lys having the (conformational entropy, rank) equal to (1.81, 17) and (1.94, 18), respectively, are frequently replaced by Ala. ...
... The amino acids Glu and Lys having the (conformational entropy, rank) equal to (1.81, 17) and (1.94, 18), respectively, are frequently replaced by Ala. However, the mutation LysRAla in these studies [23,24,[28][29][30][31][32][33]35] has larger probability than the mutation GluRAla in these studies [23,[27][28][29][30][32][33][34][35][36] of successfully enhancing crystallizability. This statistic finding can be explained by analyzing the results of SCM that Glu has the largest crystallizability score and the second largest solubility score. ...
Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p = 0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.
... Limited proteolysis can also help provide a protein form that is, by chance, more conducive to crystallization (McPherson, 1982). Introduction of point mutations, truncations, or deletions has also been demonstrated to help improve crystallization success rates (Lawson et al., 1991; McElroy et al., 1992; D'Arcy et al., 1999, Longenecker et al., 2001 Mateja et al., 2002; Charron et al., 2002, Chen et al., 1996 Dale et al., 1994; Ay et al., 1998, Betton et al., 1997 Nagi and Regan, 1997; Nugent et al., 1996; Thompson and Eisenberg, 1999; Zhou et al., 1996). A critical component of X-ray crystallography is obtaining well-ordered crystals of the target protein. ...
Advances in genomics have yielded entire genetic sequences for a variety of prokaryotic and eukaryotic organisms. This accumulating information has escalated the demands for three-dimensional protein structure determinations. As a result, high-throughput structural genomics has become a major international research focus. This effort has already led to several significant improvements in X-ray crystallographic and nuclear magnetic resonance methodologies. Crystallography is currently the major contributor to three-dimensional protein structure information. However, the production of soluble, purified protein and diffraction-quality crystals are clearly the major roadblocks preventing the realization of high-throughput structure determination. This paper discusses a novel approach that may improve the efficiency and success rate for protein crystallization. An automated nanodispensing system is used to rapidly prepare crystallization conditions using minimal sample. Proteins are subjected to an incomplete factorial screen (balanced parameter screen), thereby efficiently searching the entire "crystallization space" for suitable conditions. The screen conditions and scored experimental results are subsequently analyzed using a neural network algorithm to predict new conditions likely to yield improved crystals. Results based on a small number of proteins suggest that the combination of a balanced incomplete factorial screen and neural network analysis may provide an efficient method for producing diffraction-quality protein crystals.
... The human RhoGDI GTPase was used by Derewenda and colleagues as a model system to investigate the effect of Lys to Ala, Glu to Ala, and Glu to Asp mutagenesis on the proteinÕs crystallization properties (Longenecker et al., 2001;Mateja et al., 2002). The authors hypothesized that surface residues with high conformational entropy, specifically lysines and glutamates, impede protein crystallization. ...
Strategies for growing protein crystals have for many years been essentially empirical, the protein, once purified to a certain homogeneity, being mixed with a selection of crystallization agents selected in a more or less trial-and-error fashion. Screening for the correct conditions has been made easier through automation and by the introduction of commercially available crystallization kits. Many parameters can be changed in these experiments, such as temperature, pH, and ionic strength, but perhaps the most important variable has been ignored, namely the protein. The crystallization properties of a protein vary greatly: some crystallize readily, whereas others have proven extremely difficult or even impossible to obtain in a crystalline state. The possibility of altering the intrinsic characteristics of a protein for crystallization has become a feasible strategy. Some historical perspectives and advances in this area will be reviewed.
Correspondence: David S. Waugh, (301) 846-1842 (phone), (301) 846-7148 (fax)
The production of diffraction-quality crystals remains a difficult obstacle on the road to high-resolution structural characterization of proteins. This is primarily a result of the empirical nature of the process. Although crystallization is not predictable, factors inhibiting it are well established. First, crystal formation is always entropically unfavorable. Reducing the entropic cost of crystallizing a given protein is thus desirable. It is common practice to map boundaries and remove unstructured regions surrounding the folded protein domain. However, a problem arises when flexible regions are not at the boundaries but within a domain. Such regions cannot be deleted without adding new restraints to the domain. We encountered this problem during an attempt to crystallize the beta subunit of the eukaryotic signal recognition particle (SRbeta), bearing a long and flexible internal loop. Native SRbeta did not crystallize. However, after circularly permuting the protein by connecting the spatially close N and C termini with a short heptapeptide linker GGGSGGG and removing 26 highly flexible loop residues within the domain, we obtained diffraction-quality crystals. This protein-engineering method is simple and should be applicable to other proteins, especially because N and C termini of protein domains are often close in space. The success of this method profits from prior knowledge of the domain fold, which is becoming increasingly common in today's postgenomic era.
