Protein isoelectric point as a predictor for increased crystallization screening efficiency

Kantardjieff, Katherine A.; Rupp, Bernhard

doi:10.1093/bioinformatics/bth066

Cited by 114 publications

(86 citation statements)

References 31 publications

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“…Several methods, including XtalPred, OB-Score, ParCrys and CRYSTALP2, utilize information concerning isoelectric point which is estimated from protein sequence. This agrees with prior finding that indicate important role of this feature [46][47][48].…”

Section: Sequence-based Methods For Prediction Of Protien Crystallizasupporting

confidence: 94%

“…Based on the data from the Thermotoga maritime proteome [35], the researchers at the Joint Center for Structural Genomics discovered a few features, which include isoelectric point, sequence length, average hydropathy, existence of low complexity regions, presence of signal peptides and trans-membrane helices, that correlate with crystallization [46]. The isoelectric point calculated from the protein sequence was also used to develop a method that suggests optimal pH ranges for crystallization screening [47,48]. Experimental work by Derewenda's group shows that crystallization can be improved by application of surface entropy reduction approach in which clusters of two or three exposed amino acids with high conformational entropy side chains (such as Lys, Glu and Gln) are replaced with lower-entropy residues (like Ala) [49][50][51][52][53][54].…”

Section: Computational Models In Protein Crystallizationmentioning

confidence: 99%

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Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Kurgan

Mizianty²

2009

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Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce diffraction quality crystals for X-ray crystallography based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of insilico methods for sequence-based prediction/ assessment of the protein crystallization propensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discovered to correlate with the ability to form crystals. We also contrast and empirically compare modern sequence-based predictors of crystallization propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and complimentary predictions. Although their average accuracy is similar at around 70%, we show that application of a simple majority-vote based ensemble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystallization propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

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Section: Sequence-based Methods For Prediction Of Protien Crystallizasupporting

confidence: 94%

Section: Computational Models In Protein Crystallizationmentioning

confidence: 99%

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Kurgan

Mizianty²

2009

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“…[38][39][40] At low salt concentrations and in the absence of multivalent ions and polymers, these conditions are met when the particle is weakly charged. 39,40 To the best of our knowledge, no such prediction rules for 2D crystallization of interface-bound nanoparticles were known prior to the present study. A worthy extension of the current work would be to test the generality of the strategy outlined here by studying the assembly of charged inorganic or biological nano-objects at oppositely charged fluid interfaces.…”

Section: 5mentioning

confidence: 99%

Systematic approach to electrostatically induced 2D crystallization of nanoparticles at liquid interfaces

et al. 2011

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We report an experimental demonstration of a strategy for inducing two-dimensional (2D) crystallization of charged nanoparticles on oppositely charged fluid interfaces. This strategy aims to maximize the interfacial adsorption of nanoparticles, and hence their lateral packing density, by utilizing a combination of weakly charged particles and a high surface charge density on the planar interface. In order to test this approach, we investigated the assembly of cowpea mosaic virus (CPMV) on positively charged lipid monolayers at the aqueous solution surface, by means of in situ X-ray scattering measurements at the liquid-vapor interface. The assembly was studied as a function of the solution pH, which was used to vary the charge on CPMV, and of the mole fraction of the cationic lipid in the binary lipid monolayer, which set the interface charge density. The 2D crystallization of CPMV occurred in a narrow pH range just above the particle's isoelectric point, where the particle charge was weakly negative, and only when the cationic-lipid fraction in the monolayer exceeded a threshold. The observed 2D crystals exhibited nearly the same packing density as the densest lattice plane within the known 3D crystals of CPMV. The above electrostatic approach of maximizing interfacial adsorption may provide an efficient route to the crystallization of nanoparticles at aqueous interfaces.

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“…Favored reagents and, particularly, useful additives have been identified by such analyses. Correlations have been sought between the physical or chemical properties of specific proteins and their manner of crystallization, such as between pI and crystallization pH (Kantardjieff & Rupp, 2004), but this has had only limited success. If such correlations could be identified, however, this would prove a very powerful addition to the available approaches.…”

Section: Statistics and Data Miningmentioning

confidence: 99%

Introduction to protein crystallization

McPherson

2004

Methods

240

201

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Protein crystallization was discovered by chance about 150 years ago and was developed in the late 19th century as a powerful purification tool and as a demonstration of chemical purity. The crystallization of proteins, nucleic acids and large biological complexes, such as viruses, depends on the creation of a solution that is supersaturated in the macromolecule but exhibits conditions that do not significantly perturb its natural state. Supersaturation is produced through the addition of mild precipitating agents such as neutral salts or polymers, and by the manipulation of various parameters that include temperature, ionic strength and pH. Also important in the crystallization process are factors that can affect the structural state of the macromolecule, such as metal ions, inhibitors, cofactors or other conventional small molecules. A variety of approaches have been developed that combine the spectrum of factors that effect and promote crystallization, and among the most widely used are vapor diffusion, dialysis, batch and liquid-liquid diffusion. Successes in macromolecular crystallization have multiplied rapidly in recent years owing to the advent of practical, easy-to-use screening kits and the application of laboratory robotics. A brief review will be given here of the most popular methods, some guiding principles and an overview of current technologies.

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Protein isoelectric point as a predictor for increased crystallization screening efficiency

Abstract: A prototype example of a screen design and efficiency estimator program, CrysPred, is available at http://www-structure.llnl.gov/cryspred/

Cited by 114 publications

References 31 publications

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Systematic approach to electrostatically induced 2D crystallization of nanoparticles at liquid interfaces

Introduction to protein crystallization

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