Soft matter perspective on protein crystal assembly

Fusco, Diana; Charbonneau, Patrick

doi:10.1016/j.colsurfb.2015.07.023

Cited by 62 publications

(73 citation statements)

References 165 publications

(212 reference statements)

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“…(v) The “dumbbell” model predicts a region where three liquid phase are in equilibrium – the region is just out of the domain where measurements are currently available. The calculation demonstrates, in agreement with articles listed in the recent review papers 7,8 , that methods of the condensed matter physics may be useful in analysis of experimental data of protein systems.…”

Section: Discussionsupporting

confidence: 85%

“…1 On the other hand, many aspects of protein aggregation may be pictured by the models that include anisotropic protein–protein interactions of short range. 7,8,18–21 This strategy was successfully used to model a wide class of globular proteins and to analyze the liquid–liquid phase separation 22,23 , crystallization 24,25 , osmotic pressures 26 , distribution of aggregates 27,28 , percolation threshold 27 , and other physico–chemical properties. 21 …”

Section: Introductionmentioning

confidence: 99%

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Modeling phase transitions in mixtures of β–γ lens crystallins

2016

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We analyze experimentally determined phase diagram of γD–βB1 crystallin mixture. Proteins are described as dumbbells decorated with attractive sites to allow inter–particle interaction. We use thermodynamic perturbation theory to calculate the free energy of such mixtures and, by applying equilibrium conditions, also the compositions and concentrations of the co–existing phases. Initially we fit the Tcloud versus packing fraction η measurements for pure (x2 = 0) γD solution in 0.1 M phosphate buffer at pH = 7.0. Another piece of experimental data, used to fix the model parameters, is the isotherm x2 vs η at T = 268.5 K, at the same pH and salt content. We use the conventional Lorentz–Berthelot mixing rules to describe cross interactions. This enables us to determine (i) model parameters for pure βB1 crystallin protein and to calculate (ii) complete equilibrium surface (Tcloud – x2 – η) for the crystallin mixtures. iii) We present the results for several isotherms, including the tie–lines, as also the temperature–packing fraction curves. Good agreement with the available experimental data is obtained. An interesting result of these calculations is an evidence of coexistence of three phases. This domain appears for the region of temperatures just out of the experimental range studied so far. The input parameters, leading good description of experimental data, revealed large difference between the numbers of the attractive sites for γD and βB1 proteins. This interesting result may be related to the fact that γD has more than nine times smaller quadrupole moment than its partner in the mixture.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Modeling phase transitions in mixtures of β–γ lens crystallins

2016

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“…1,3 Concurrently, development of patchy particle models has occurred in which isotropic interaction potentials are replaced by those incorporating anisotropy, by specifically defining regions on the particle surface with a number of fixed (attractive) potentials. [4][5][6] Here, there is a direct analogy to be drawn with proteins. Proteins can also be considered as patchy particles, in which the diversity in the chemical surface, due to variations in the surface exposed amino acids, creates anisotropy at a molecular level.…”

Section: Introductionmentioning

confidence: 99%

How fluorescent labelling alters the solution behaviour of proteins

Quinn

Gnan²,

James

et al. 2015

Phys. Chem. Chem. Phys.

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A complete understanding of the role of molecular anisotropy in directing the self assembly of colloids and proteins remains a challenge for soft matter science and biophysics. For proteins in particular, the complexity of the surface at a molecular level poses a challenge for any theoretical and numerical description. A soft matter approach, based on patchy models, has been useful in describing protein phase behaviour. In this work we examine how chemical modification of the protein surface, by addition of a fluorophore, affects the physical properties of protein solutions. By using a carefully controlled experimental protein model (human gamma-D crystallin) and numerical simulations, we demonstrate that protein solution behaviour defined by anisotropic surface effects can be captured by a custom patchy particle model. In particular, the chemical modification is found to be equivalent to the addition of a large hydrophobic surface patch with a large attractive potential energy well, which produces a significant increase in the temperature at which liquid-liquid phase separation occurs, even for very low fractions of fluorescently labelled proteins. These results are therefore directly relevant to all applications based on the use of fluorescent labelling by chemical modification, which have become increasingly important in the understanding of biological processes and biophysical interactions.

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“…Our current understanding of protein crystallization owes much to experimental and theoretical soft matter physics, and particularly to the study of colloids [15]. More than two decades ago, it has been observed that both colloidal particles and proteins tend to crystallize when the osmotic second virial coefficient, B 2 , which depends only on the pair interaction between the particles, lies in the favorable, crystallization ‘slot’[16].…”

Section: Theoretical and Experimental Evidence For The ‘Sticky Patcmentioning

confidence: 99%

The “Sticky Patch” Model of Crystallization and Modification of Proteins for Enhanced Crystallizability

Derewenda

Godzik

2017

Methods in Molecular Biology

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Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e. crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular ‘sticky patch’ model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer ‘sticky patches’ which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the ‘sticky patch’ model. We discuss state-of-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design of variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis.

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Soft matter perspective on protein crystal assembly

Cited by 62 publications

References 165 publications

Modeling phase transitions in mixtures of β–γ lens crystallins

Modeling phase transitions in mixtures of β–γ lens crystallins

How fluorescent labelling alters the solution behaviour of proteins

The “Sticky Patch” Model of Crystallization and Modification of Proteins for Enhanced Crystallizability

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