Protein Crystallization Process Research Articles

Spectroscopic insights into multi-phase protein crystallization in complex lysate using Raman spectroscopy and a particle-free bypass.

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (LkADH) was crystallized from clarified Escherichia coli (E. coli) lysate on a 300mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of LkADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.

Insights into Early Phases of Phycocyanin Crystal Formation via SONICC Spectroscopy

This research delves into the early nucleation stages of phycocyanin, a protein pivotal for its fluorescent properties and crystalline stability and holding considerable potential for biotechnological applications. The paper contrasts traditional crystallization methods with the innovative Langmuir–Blodgett nanotemplate approach, aiming to enhance molecular assembly and nucleation processes. The study employs Langmuir–Blodgett nanotemplates alongside second-order nonlinear imaging of chiral crystal (SONICC) spectroscopy. This combination is designed to orderly organize phycocyanin molecules and provide a sensitive visualization of early-stage crystal formation, capturing the intricate dynamics of protein crystallization. The experiments were conducted under controlled conditions, where surface pressure was maintained at 26 mN/m and barrier speed at 70 cm/min to optimize the monolayer formation at the air–water interface. The Langmuir–Blodgett method, compared to traditional vapor diffusion techniques, shows improvements in the uniformity and efficiency of nucleation. The sensitivity of SONICC spectroscopy significantly enhances the visualization of the nucleation process, revealing a more structured and uniform crystalline assembly in the early stages of formation. This method demonstrates a substantial improvement in nucleation dynamics, leading to a more orderly growth process and potentially larger, well-ordered crystals. Integrating Langmuir–Blodgett nanotemplates with SONICC spectroscopy offers a significant step in understanding protein crystallization processes with insights into the nucleation and growth of protein crystals and broad implications for refining crystallography methodologies of protein-based biomaterials, contributing to the advancement of structural biology and materials science.

Recent Advancements in Continuous Crystallization of Proteins

AbstractRecently, there have been many studies and reports on various continuous protein crystallization processes. Due to the increasing demand for pharmaceutical proteins such as insulin and monoclonal antibodies, it has become essential to optimize the current industrial processes to meet these demands. There are various comparisons to be made between current batch crystallization processes and continuous processes for proteins. This review paper aims to review the recent advancements in continuous protein crystallization processes, such as slug flow crystallizers, oscillatory baffled crystallizers, and mixed suspension mixed product removal (MSMPR) crystallizers.

Improving protein crystal quality using deep eutectic solvents

Protein crystallization is an essentially important process for its valuable applications in molecular structure determination of proteins, the study of biomolecular interaction mechanism, research and development of special functional crystal materials, theoretical study of crystallization mechanism, industrial production of proteins, and so on. Improving the quality of protein crystals is desirable research because it can provide high-quality samples or products for these studies and applications. Here we report that the utilization of DESs as additives in protein crystallization can significantly affect the crystallization process and effectively improve the crystal quality. We synthesized 9 types of DESs, studied their physicochemical properties, and selected 3 DESs suitable as additives for protein crystallization. Using three model proteins (lysozyme, thaumatin, and proteinase K), we confirmed that DESs can significantly affect the protein crystallization process. Crystals obtained at appropriate concentrations of DESs have higher quality, higher diffraction resolution, and better diffraction data collection. A comparison of the 3D structure of protein molecules showed that the addition of DESs did not affect the molecular structure of the proteins. These results proved that the presence of DESs during protein crystallization can help to improve crystal quality without affecting the structure of proteins, showing that DESs can be employed as a novel class of additives for protein crystallization.

Protein Nucleation and Crystallization Process with Process Analytical Technologies in a Batch Crystallizer.

Protein crystallization has drawn great attention to replacing the traditional downstream processing for protein-based pharmaceuticals due to its advantages in stability, storage, and delivery. Limited understanding of the protein crystallization processes requires essential information based on real-time tracking during the crystallization process. A batch crystallizer of 100 mL fitted with a focused beam reflectance measurement (FBRM) probe and a thermocouple was designed for in situ monitoring of the protein crystallization process, with simutaneously record of off-line concentrations and crystal images. Three stages in the protein batch crystallization process were identified: long-period slow nucleation, rapid crystallization, and slow growth and breakage. The induction time was estimated by FBRM, i.e., increasing numbers of particles in the solution, which could be half of the time required for detecting the decrease of the concentration, by offline measurement. The induction time decreased with an increase in supersaturation within the same salt concentration. The interfacial energy for nucleation was analyzed based on each experimental group with equal salt concentration and different concentrations of lysozyme. The interfacial energy reduced with an increase in salt concentration in the solution. The yield of the experiments was significantly affected by the protein and salt concentrations and could achieve up to 99% yield with a 26.5 μm median crystal size upon stabilized concentration readings.

Recent Advances in the Monitoring of Protein Crystallization Processes in Downstream Processing

Protein crystallization is nowadays performed at the micro to macro scale in academia and industry, being particularly interesting for pharmaceutical applications. Protein crystallization offers an attractive alternative to chromatography as a downstream processing step in the biotechnology industry, but also in the food and chemical industries. Monitoring of the protein crystallization process is required to understand the crystal growth mechanism and to obtain the information necessary for efficient process control, which needs to comply with the critical quality attributes of the product. Since a wide range of monitoring techniques have already been developed and established, this review focuses on the recent advances of selected techniques in monitoring protein crystallization processes such as the focused beam reflectance method (FBRM), and machine learning-based image analysis for solid-phase monitoring, as well as the spectroscopic methods for liquid-phase monitoring, such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) and UV/Vis spectroscopy.

Positive and negative effects of graphite flake and monolayer graphene oxide templates on protein crystallization

Heterogeneous template-induced nucleation is a promising way to regulate protein crystallization events and could be employed for purification processes and crystallographic studies. Protein crystallization process with graphite and graphene oxide, as heterogeneous templates, were investigated. More than 640 hanging drops with different concentrations of Lysozyme (30, 50, 70, 100 mg/mL) and NaCl (0.7, 0.9, 1.1, 1.3, 1.5 M) were crystallised at 4 °C with or without graphite/graphene oxide templates. The induction times and crystallization process were observed under the microscope. The lysozyme in the solutions with graphite flakes nucleated faster under all the conditions than the lysozyme with equal experimental conditions without templates. The crystals preferred to grow around the edge of graphite flakes than on the flat surfaces. In the droplets with monolayer graphene oxide, more crystals appeared around graphene oxide particles, and the faster or slower nucleation processes with templates were dependent on the lysozyme and NaCl concentrations. Graphene oxide templates strongly inhibited nucleation at high lysozyme concentrations but promoted nucleation at low lysozyme concentrations. Both heterogeneous templates changed the crystal morphology and the crystallization kinetics. More crystals were observed in the solution with graphite templatesthan with graphene oxide templates and without any template.

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images

Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad application of crystallization as a purification method, advances in molecular biology, as well as computational methods are pushing the applicability towards integration in biotechnological downstream processes. However, in industrial and academic settings, monitoring protein crystallization processes non-invasively by microscopic photography and automated image evaluation remains a challenging problem. Recently, the identification of single crystal objects using deep learning has been the subject of increased attention for various model systems. However, the advancement of crystal detection using deep learning for biotechnological applications is limited: robust models obtained through supervised machine learning tasks require large-scale and high-quality data sets usually obtained in large projects through extensive manual labeling, an approach that is highly error-prone for dense systems of transparent crystals. For the first time, recent trends involving the use of synthetic data sets for supervised learning are transferred, thus generating photorealistic images of virtual protein crystals in suspension (PCS) through the use of ray tracing algorithms, accompanied by specialized data augmentations modelling experimental noise. Further, it is demonstrated that state-of-the-art models trained with the large-scale synthetic PCS data set outperform similar fine-tuned models based on the average precision metric on a validation data set, followed by experimental validation using high-resolution photomicrographs from stirred tank protein crystallization processes.

Enhancement of protein crystallization with the application of Taylor vortex and Poly(ionic liquid)s

With the increase demands for biopharmaceuticals, the development of efficient protein crystallization processes for manufacturing highly pure crystalline products has become critical for downstream biomanufacturing. This work focused on the combined effects of the uniform shear rate in a solution created by using the Taylor vortex and the macromolecular architecture of a solution developed using poly(ionic liquid)s during protein crystallization. The results were as follows: (1) the accelerated primary nucleation rate with a uniform shear force that was generated by the Taylor vortex; (2) the stability of the protein product prepared using the poly(ionic liquid)s was high in the crystallization solution; and (3) improved control of supersaturation of the polymer was achieved with the salting-out effect. The average crystal size in the control group was considerably lower than 1 μm with low quality (9.00 Å) and yield (56%) of crystals, but the average size of lysozyme crystals obtained using the Taylor vortex and poly(ionic liquid)s increased up to 5 μm with high quality (1.86 Å) of crystals and yield (86%). A higher diffraction resolution indicated a better ordered crystalline structure, demonstrating that the Taylor vortex and poly(ionic liquid)s are useful for improving the crystal quality.

Protein Crystallization from a Preordered Metastable Intermediate Phase Followed by Real-Time Small-Angle Neutron Scattering

We present a systematic study using real-time small-angle neutron scattering (SANS) and optical microscopy to follow the protein crystallization process in the presence of a metastable intermediate...

Droplet-Based Evaporative System for the Estimation of Protein Crystallization Kinetics.

Crystallization is a potential cost-effective alternative to chromatography for the purification of biotherapeutic proteins. Crystallization kinetics are required for the design and control of such processes, but only a limited quantity of proteins is available during the initial stage of process development. This article describes the design of a droplet-based evaporative system for the evaluation of candidate crystallization conditions and the estimation of kinetics using only a droplet (on the order of μL) of protein solution. The temperature and humidity of air fed to a flow cell containing the droplet are controlled for evaporation and rehydration of the droplet, which are used for manipulating supersaturation. Dual-angle images of the droplet are taken and analyzed on-line to obtain the droplet volume and crystal sizes. Crystallization kinetics are estimated based on a first-principles process model and experimental data. Tight control of temperature and humidity of the air, fast and accurate image analysis, and accurate estimation of crystallization kinetics are experimentally demonstrated for a model protein lysozyme. The estimated kinetics are suitable for the model-based design and control of protein crystallization processes.

Influence of Divalent Cations in the Protein Crystallization Process Assisted by Lanthanide-Based Additives.

The use of lanthanide complexes as powerful auxiliaries for biocrystallography prompted us to systematically analyze the influence of the commercial crystallization kit composition on the efficiency of two lanthanide additives: [Eu(DPA)3]3- and Tb-Xo4. This study reveals that the tris(dipicolinate) complex presents a lower chemical stability and a strong tendency toward false positives, which are detrimental for its use in a high-throughput robotized crystallization platform. In particular, the crystal structures of (Mg(H2O)6)3[Eu(DPA)3]2·7H2O (1), {(Ca(H2O)4)3[Eu(DPA)3]2}n·10nH2O (2), and {Cu(DPA)(H2O)2}n (3), resulting from spontaneous crystallization in the presence of a divalent alkaline-earth cation and transmetalation, are reported. On the other hand, Tb-Xo4 is perfectly soluble in the crystallization media, stable in the presence of alkaline-earth dications, and slowly decomposes (within days) by transmetalation with transition metals. The original structure of [Tb4L4(H2O)4]Cl4·15H2O (4) is also described, where L represents a bis(pinacolato)triazacyclononane ligand. This paper also highlights a potential synergy of interactions between Tb-Xo4 and components of the crystallization mixtures, leading to the formation of complex adducts like {AdkA/Tb-Xo4/Mg2+/glycerol} in the protein binding sites. The observation of such multicomponent adducts illustrated the complexity and versatility of the supramolecular chemistry occurring at the surface of the proteins.

Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.

X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew's correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

Following Protein Dynamics in Real Time during Crystallization

The process of protein crystallization from aqueous protein solutions is still insufficiently understood. During macroscopic crystal formation, occurring often on time scales from a few hours to se...

Development and Workflow of a Continuous Protein Crystallization Process: A Case of Lysozyme

In the present work, a workflow on the development of a continuous protein crystallization is introduced, with lysozyme as a model protein, from microliter screening experiments, to small scale batch crystallization experiments in a shaking crystallization platform, and to batch and continuous crystallization experiments in an oscillatory flow platform. The lysozyme crystallizations investigated were for a concentration range from 30 to 100 mg/mL, shaking conditions from 100 to 200 rpm in the batch shaking crystallization platform, and oscillatory conditions with amplitude (x0) from 5 to 30 mm and frequency (f) from 0.1 to 1.0 Hz in the batch oscillatory flow crystallization platform. We propose the use of the Reynold’s number (Re) for scaling up of the process from the shaking batch to the continuous oscillatory flow platform. Additionally, it is shown that the nucleation rate increased with increase in concentration of initial lysozyme solution, or increase in shear rate, inducing smaller size of lysozy...

Crystal Structure Determination of Ubiquitin by Fusion to a Protein That Forms a Highly Porous Crystal Lattice.

The protein crystallization process requires screening of a large number of conditions using a large quantity of high-purity protein, which makes crystal structure analysis difficult. Thus, the development of easy and versatile protein crystallization techniques is both extremely desirable and highly challenging. Here I demonstrate the crystallization and structure determination of ubiquitin by genetic fusion to the highly porous honeycomb lattice of R1EN. I successfully crystallized and collected X-ray data from three R1EN-ubiquitin constructs with various linker lengths under the same conditions as the original R1EN. The crystals diffracted to 1.7-2.4 Å resolution, and the ubiquitin structures were determined with results almost identical to the previously published structure. Moreover, the ubiquitin structure could be solved by molecular replacement using R1EN alone. This method may reduce the effort required for crystallization screening and is applicable to de novo protein structure determination.

From microscale phase screening to bulk evaporative crystallization of proteins

Protein phase diagrams obtained from material-efficient microscale phase screenings can be used successfully for designing a macroscale preparative protein crystallization process to gain the crystal morphology desired. In this experimental study, the phase behavior of three globular proteins: hen egg white lysozyme, bovine serum albumin, and alcohol dehydrogenase from Lactobacillus brevis, is evaluated by isothermal microbatch phase screenings in 96-well plates. Vacuum bulk evaporative crystallization experiments in the permissible temperature range are performed at physicochemical conditions based on the phase diagrams determined, and the products are compared to those of the microscale screenings in terms of morphology and size. Crystals of the desired morphology are obtained in evaporative crystallization, when the phase diagram shows a broad region of this morphology. Even multiple crystal types or both crystals and amorphous precipitate are obtained parallelly in evaporative crystallization, when this is predicted by the phase screening. Crystals that have grown together in the screenings appear as individual crystals in the macroscale experiments, and most of the crystals are substantially smaller due to agitation. The findings are expected to pave the way for a more systematic approach in macroscale protein crystallization.

Scoring of Side-Chain Packings: An Analysis of Weight Factors and Molecular Dynamics Structures.

The protein side-chain packing problem (PSCPP) is a central task in computational protein design. The problem is usually modeled as a combinatorial optimization problem, which consists of searching for a set of rotamers, from a given rotamer library, that minimizes a scoring function (SF). The SF is a weighted sum of terms, that can be decomposed in physics-based and knowledge-based terms. Although there are many methods to obtain approximate solutions for this problem, all of them have similar performances and there has not been a significant improvement in recent years. Studies on protein structure prediction and protein design revealed the limitations of current SFs to achieve further improvements for these two problems. In the same line, a recent work reported a similar result for the PSCPP. In this work, we ask whether or not this negative result regarding further improvements in performance is due to (i) an incorrect weighting of the SFs terms or (ii) the constrained conformation resulting from the protein crystallization process. To analyze these questions, we (i) model the PSCPP as a bi-objective combinatorial optimization problem, optimizing, at the same time, the two most important terms of two SFs of state-of-the-art algorithms and (ii) performed a preprocessing relaxation of the crystal structure through molecular dynamics to simulate the protein in the solvent and evaluated the performance of these two state-of-the-art SFs under these conditions. Our results indicate that (i) no matter what combination of weight factors we use the current SFs will not lead to better performances and (ii) the evaluated SFs will not be able to improve performance on relaxed structures. Furthermore, the experiments revealed that the SFs and the methods are biased toward crystallized structures.

Laser processing of protein crystals for native SAD data collection

Laser processing of protein crystals for native SAD data collection

Comparative Study on Seeded and Unseeded Bulk Evaporative Batch Crystallization of Tetragonal Lysozyme

Seeding is highlighted as a promising tool to improve controllability, reproducibility, and quality of the process and of the product in bulk protein crystallization. In this experimental study, hen egg white lysozyme is crystallized from its buffered aqueous solution in a stirred tank by evaporative batch crystallization at low pressure. Experiments with and without the use of lysozyme seed crystals are evaluated and compared in terms of nucleation behavior, supersaturation evolution, and product quality. The supersaturations needed for spontaneous nucleation are shown to vary strongly in between the unseeded experiments, resulting in a large variation of the supersaturation evolutions during the experiments and the final crystal size distributions. In contrast, seeding is proved to lead to controlled and reproducible secondary nucleation. The reproducibility of the product crystal size distribution is demonstrated to be dramatically increased by seeding compared to the unseeded experiments. Moreover it is shown that the supersaturation evolution and the crystal size distribution of the seeded experiments can be governed by the seed quantity and the evaporation rate. The findings are expected to pave the way for the application of seeding in future protein crystallization processes.