Protein Crystallization Experiments Research Articles

Assembly Requirements for the Construction of Large-Scale Binary Protein Structures.

The precise assembly of multiple biomacromolecules into well-defined structures and materials is of great importance for various biomedical and nanobiotechnological applications. In this study, we investigate the assembly requirements for two-component materials using charged protein nanocages as building blocks. To achieve this, we designed several variants of ferritin nanocages to determine the surface characteristics necessary for the formation of large-scale binary three-dimensional (3D) assemblies. These nanocage variants were employed in protein crystallization experiments and macromolecular crystallography analyses, complemented by computational methods. Through the screening of nanocage variant combinations at various ionic strengths, we identified three essential features for successful assembly: (1) the presence of a favored crystal contact region, (2) the presence of a charged patch not involved in crystal contacts, and (3) sufficient distinctiveness between the nanocages. Surprisingly, the absence of noncrystal contact mediating patches had a detrimental effect on the assemblies, highlighting their unexpected importance. Intriguingly, we observed the formation of not only binary structures but also both negatively and positively charged unitary structures under previously exclusively binary conditions. Overall, our findings will inform future design strategies by providing some design rules, showcasing the utility of supercharging symmetric building blocks in facilitating the assembly of biomacromolecules into large-scale binary 3D assemblies.

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks

The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry.

Racemic crystal structures of A-DNA duplexes.

The ease with which racemic mixtures crystallize compared with the equivalent chiral systems is routinely taken advantage of to produce crystals of small molecules. However, biological macromolecules such as DNA and proteins are naturally chiral, and thus the limited range of chiral space groups available hampers the crystallization of such molecules. Inspiring work over the past 15 years has shown that racemic mixtures of proteins, which were made possible by impressive advances in protein chemical synthesis, can indeed improve the success rate of protein crystallization experiments. More recently, the racemic crystallization approach was extended to include nucleic acids as a possible aid in the determination of enantiopure DNA crystal structures. Here, findings are reported that suggest that the benefits may extend beyond this. Two racemic crystal structures of the DNA sequence d(CCCGGG) are described which were found to fold into A-form DNA. This form differs from the Z-form DNA conformation adopted by the chiral equivalent in the solid state, suggesting that the use of racemates may also favour the emergence of new conformations. Importantly, the racemic mixture forms interactions in the solid state that differ from the chiral equivalent (including the formation of racemic pseudo-helices), suggesting that the use of racemic DNA mixtures could provide new possibilities for the design of precise self-assembled nanomaterials and nanostructures.

Semi-empirical model to estimate ideal conditions for the growth of large protein crystals.

A large high-quality crystal is required to specify the positions of H atoms in neutron structural analysis. Consequently, several methods have been proposed for obtaining such large crystals, and theoretical considerations for growing them have been presented. However, further investigation is required to obtain a numerical model that can provide quantitative experimental conditions for obtaining a single large crystal. In the case of protein crystallization experiments, the amount of sample is often limited. Therefore, it is more realistic to make a rough estimation from a small number of experiments. This paper proposes a method of estimating the optimum experimental conditions for the growth of large protein crystals by performing a small number of experiments using a micro-batch method and reporting a numerical model based on nucleation theory and a linear approximation of the crystal-growth rate. Specifically, micro-batch experiments are performed to provide the empirical parameters for the model and to help to estimate the conditions for the growth of a crystal of a predetermined size using a certain sample concentration and volume. This method is offered as a step on the path towards efficiently and rationally producing large crystals that can be subjected to neutron diffraction without depending on luck or on performing many experiments. It is expected to contribute to drug design and the elucidation of protein molecular functions and mechanisms by obtaining positional information on H atoms in the protein molecule, which is an advantage of neutron diffraction.

Finding the generalized molecular principles of protein thermal stability.

Are there any generalized molecular principles of thermal adaptation? Here, integrating the concepts of structural bioinformatics, sequence analysis, and classical knot theory, we develop a robust computational framework that seeks for mechanisms of thermal adaptation by comparing orthologous mesophilic-thermophilic and mesophilic-hyperthermophilic proteins of remarkable structural and topological similarities, and still leads us to context-independent results. A comprehensive analysis of 4741 high-resolution, non-redundant X-ray crystallographic structures collected from 11 hyperthermophilic, 32 thermophilic and 53 mesophilic prokaryotes unravels at least five "nearly universal" signatures of thermal adaptation, irrespective of the enormous sequence, structure, and functional diversity of the proteins compared. A careful investigation further extracts a set of amino acid changes that can potentially enhance protein thermal stability, and remarkably, these mutations are overrepresented in protein crystallization experiments, in disorder-to-order transitions and in engineered thermostable variants of existing mesophilic proteins. These results could be helpful to find a precise, global picture of thermal adaptation.

Multi-Step Concanavalin A Phase Separation and Early-Stage Nucleation Monitored Via Dynamic and Depolarized Light Scattering

Protein phase separation and protein liquid cluster formation have been observed and analysed in protein crystallization experiments and, in recent years, have been reported more frequently, especially in studies related to membraneless organelles and protein cluster formation in cells. A detailed understanding about the phase separation process preceding liquid dense cluster formation will elucidate what has, so far, been poorly understood—despite intracellular crowding and phase separation being very common processes—and will also provide more insights into the early events of in vitro protein crystallization. In this context, the phase separation and crystallization kinetics of concanavalin A were analysed in detail, which applies simultaneous dynamic light scattering and depolarized dynamic light scattering to obtain insights into metastable intermediate states between the soluble phase and the crystalline form. A multi-step mechanism was identified for ConA phase separation, according to the resultant ACF decay, acquired after an increase in the concentration of the crowding agent until a metastable ConA gel intermediate between the soluble and final crystalline phases was observed. The obtained results also revealed that ConA is trapped in a macromolecular network due to short-range intermolecular protein interactions and is unable to transform back into a non-ergodic solution.

Inexpensive robotic system for standard and fluorescent imaging of protein crystals.

Protein-crystallization imaging and classification is a labor-intensive process typically performed either by humans or by instruments that currently cost well over $100 000. This cost puts the use of crystallization-trial imaging outside the reach of most academic laboratories, and also start-up biotechnology firms, where resources are scarce. An imaging system has been designed and prototyped which automatically captures images from multi-well protein-crystallization experiments using both standard and fluorescent imaging techniques at a cost 28 times lower than current market rates. The machine uses a Panowin F1 3D printer as a base and controls it using G-code commands sent from a Python script running on a desktop computer. A graphical user interface (GUI) was developed to enable users to control the machine and facilitate image capture, classification and editing. A 488 nm laser diode and a 525 nm filter were incorporated to allow in situ fluorescent imaging of proteins trace-labeled with a fluorophore, Alexa Fluor 488. The instrument was primarily designed using a 3D printer and augmented using commercially available parts, and this publication aims to serve as a guide for comparable in-laboratory robotics projects.

Recent Advance in High Quality Protein Crystal Growth Experiment on the International Space Station by JAXA

Recent Advance in High Quality Protein Crystal Growth Experiment on the International Space Station by JAXA

Thermal and solutal Marangoni stagnation point Casson fluid flow over a stretching sheet in the presence of radiation, Soret and Dofour effect with chemical reaction

AbstractThe Marangoni flow is involved with microgravity and earth gravity, which causes undesirable effects in crystal growth experiments. Crystal growth experiments were designed in such a manner so as to appraise MIR (space station), which is one of the best platforms for protein crystallization and radiation experiments. In this article, a model is proposed with a stagnation point and a Casson fluid flow at the interface of the plate in the presence of Marangoni convection influenced by a magnetic field and chemical reaction. Furthermore, it is considered that both temperature and concentration surface tension vary linearly with the interface. It is important to choose similarity transformations for implementing nonlinear differential equations into linear ordinary differential equations. We solved the system of differential equations using fourth order Range‐Kutta method with suitable shooting techniques, and the results are displayed through graphs. A comparison is made with the earlier existing literature, and it shows a very good agreement. The results and a detailed discussion of velocity, temperature, and concentration have been shown graphically. The favorable and unfavorable buoyancy force to Marangoni flow, the features of temperature and concentration field, have been investigated.

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with &lt;em&gt;In Situ&lt;/em&gt; Dynamic Light Scattering

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to precisely maneuver the nucleation and crystal growth towards desired sizes of protein crystals. The controlled crystallization is based on sample investigation with in situ Dynamic Light Scattering (DLS) while all visual changes in the droplet are monitored online with the help of a microscope coupled to a CCD camera, thus enabling a full investigation of the protein droplet during all stages of crystallization. The use of in situ DLS measurements throughout the entire experiment allows a precise identification of the highly supersaturated protein solution transitioning to a new phase – the formation of crystal nuclei. By identifying the protein nucleation stage, the crystallization can be optimized from large protein crystals to the production of protein microcrystals. The experimental protocol shows an interactive crystallization approach based on precise automated steps such as precipitant addition, water evaporation for inducing high supersaturation, and sample dilution for slowing induced homogeneous nucleation or reversing phase transitions.

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with &lt;em&gt;In Situ&lt;/em&gt; Dynamic Light Scattering

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to precisely maneuver the nucleation and crystal growth towards desired sizes of protein crystals. The controlled crystallization is based on sample investigation with in situ Dynamic Light Scattering (DLS) while all visual changes in the droplet are monitored online with the help of a microscope coupled to a CCD camera, thus enabling a full investigation of the protein droplet during all stages of crystallization. The use of in situ DLS measurements throughout the entire experiment allows a precise identification of the highly supersaturated protein solution transitioning to a new phase – the formation of crystal nuclei. By identifying the protein nucleation stage, the crystallization can be optimized from large protein crystals to the production of protein microcrystals. The experimental protocol shows an interactive crystallization approach based on precise automated steps such as precipitant addition, water evaporation for inducing high supersaturation, and sample dilution for slowing induced homogeneous nucleation or reversing phase transitions.

Surfaces with Controllable Topography and Chemistry Used as a Template for Protein Crystallization.

Surfaces with controllable topography and chemistry were prepared to act as substrates for protein crystallization, in order to investigate the influence of these surface properties on the protein crystallization outcome. Three different methods were investigated to deposit 1,3,5-tris(10-carboxydecyloxy)benzene (TCDB) on a muscovite mica substrate to find the best route for controlled topography. Of these three, sublimation worked best. Contact angle measurements revealed that the surfaces with short exposure to the TCDB vapor (20 min or less) are hydrophilic, while surfaces exposed for 30 min or longer are hydrophobic. The hydrophilic surfaces are flat with low steps, while the hydrophobic surfaces contain macrosteps. Four model proteins were used for crystallization on the surfaces with controlled topography and chemistry. Hen egg white lysozyme crystals were less numerous on the surface with macrosteps than on smoother surfaces. On the other hand, insulin nucleated faster on the hydrophobic surfaces with macrosteps, and therefore, the crystals were more abundant and smaller. Bovine serum albumin and talin protein crystals were more numerous on all TCDB functionalized surfaces, compared to the reference clean muscovite mica surfaces. Overall, this shows that surface topography and chemistry is an important factor that partly determines the outcome in a protein crystallization experiment.

Protein crystallization under microgravity conditions. Analysis of the results of Russian experiments performed on the International Space Station in 2005−2015

Conditions of mass transport to growing crystals have a considerable effect on the crystal size and quality. The reduction of convective transport can help improve the quality of crystals for X-ray crystallography. One approach to minimizing convective transport is crystallization in a microgravity environment, in particular, in space. The data obtained by our research team in protein crystallization experiments on the International Space Station are surveyed and analyzed.

LA FASE ESPONJA COMO SISTEMA BIOMIMÉTICO PARA CRISTALIZAR PROTEÍNAS DE MEMBRANA

En este trabajo se describe de manera general la importancia de la cristalización de proteínas para realizar experimentos de difracción de rayos X que permitan dilucidar la estructura terciaria de dichas biomoléculas. En particular, se expone que la cristalización de proteínas de membrana requiere métodos especiales de preparación de la matriz de cristalización puesto que se debe “mimetizar” el ambiente hidrofóbico de la proteína en la membrana. De esta manera, el trabajo se centra en algunas propiedades de la fase líquida de membranas denominada “fase esponja”, cuya característica principal es una estructura membranar compleja conectada en tres dimensiones. Debido a su microestructura, la fase esponja es transparente e isotrópica, además de presentar baja viscosidad en las membranas. Estas características convierten a la fase esponja en un medio “biomimético” potencialmente útil para cristalizar proteínas de membrana.

The collection of MicroED data for macromolecular crystallography.

The formation of large, well-ordered crystals for crystallographic experiments remains a crucial bottleneck to the structural understanding of many important biological systems. To help alleviate this problem in crystallography, we have developed the MicroED method for the collection of electron diffraction data from 3D microcrystals and nanocrystals of radiation-sensitive biological material. In this approach, liquid solutions containing protein microcrystals are deposited on carbon-coated electron microscopy grids and are vitrified by plunging them into liquid ethane. MicroED data are collected for each selected crystal using cryo-electron microscopy, in which the crystal is diffracted using very few electrons as the stage is continuously rotated. This protocol gives advice on how to identify microcrystals by light microscopy or by negative-stain electron microscopy in samples obtained from standard protein crystallization experiments. The protocol also includes information about custom-designed equipment for controlling crystal rotation and software for recording experimental parameters in diffraction image metadata. Identifying microcrystals, preparing samples and setting up the microscope for diffraction data collection take approximately half an hour for each step. Screening microcrystals for quality diffraction takes roughly an hour, and the collection of a single data set is ∼10 min in duration. Complete data sets and resulting high-resolution structures can be obtained from a single crystal or by merging data from multiple crystals.

Optimizing Associative Experimental Design for Protein Crystallization Screening.

The goal of protein crystallization screening is the determination of the main factors of importance to crystallizing the protein under investigation. One of the major issues about determining these factors is that screening is often expanded to many hundreds or thousands of conditions to maximize combinatorial chemical space coverage for maximizing the chances of a successful (crystalline) outcome. In this paper, we propose an experimental design method called "Associative Experimental Design (AED)" and an optimization method includes eliminating prohibited combinations and prioritizing reagents based on AED analysis of results from protein crystallization experiments. AED generates candidate cocktails based on these initial screening results. These results are analyzed to determine those screening factors in chemical space that are most likely to lead to higher scoring outcomes, crystals. We have tested AED on three proteins derived from the hyperthermophile Thermococcus thioreducens, and we applied an optimization method to these proteins. Our AED method generated novel cocktails (count provided in parentheses) leading to crystals for three proteins as follows: Nucleoside diphosphate kinase (4), HAD superfamily hydrolase (2), Nucleoside kinase (1). After getting promising results, we have tested our optimization method on four different proteins. The AED method with optimization yielded 4, 3, and 20 crystalline conditions for holo Human Transferrin, archaeal exosome protein, and Nucleoside diphosphate kinase, respectively.

CrystPro: Spatiotemporal Analysis of Protein Crystallization Images.

Thousands of experiments corresponding to different combinations of conditions are set up to determine the relevant conditions for successful protein crystallization. In recent years, high throughput robotic set-ups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallization progress. Images are collected multiple times during the course of an experiment. Huge number of collected images make manual review of images tedious and discouraging. In this paper, utilizing trace fluorescence labeling, we describe an automated system called CrystPro for monitoring the protein crystal growth in crystallization trial images by analyzing the time sequence images. Given the sets of image sequences, the objective is to develop an efficient and reliable system to detect crystal growth changes such as new crystal formation and increase of crystal size. CrystPro consists of three major steps- identification of crystallization trials proper for spatio-temporal analysis, spatio-temporal analysis of identified trials, and crystal growth analysis. We evaluated the performance of our system on 3 crystallization image datasets (PCP-ILopt-11, PCP-ILopt-12, and PCP-ILopt-13) and compared our results with expert scores. Our results indicate a) 98.3% accuracy and .896 sensitivity on identification of trials for spatio-temporal analysis, b) 77.4% accuracy and .986 sensitivity of identifying crystal pairs with new crystal formation, and c) 85.8% accuracy and 0.667 sensitivity on crystal size increase detection. The results show that our method is reliable and efficient for tracking growth of crystals and determining useful image sequences for further review by the crystallographers.

Dynamically optimizing experiment schedules of a laboratory robot system with simulated annealing.

A scheduler has been developed for an integrated laboratory robot system that operates in an always-on mode. The integrated system is designed for imaging plates containing protein crystallization experiments, and it allows crystallographers to enter plates at any time and request that they be imaged at multiple time points in the future. The scheduler must rearrange tasks within the time it takes to image one plate, trading off the quality of the schedule for the speed of the computation. For this reason, the scheduler was based on a simulated annealing algorithm with an objective function that makes use of a linear programming solver. To optimize the scheduler, extensive computational simulations were performed involving a difficult but representative scheduling problem. The simulations explore multiple configurations of the simulated annealing algorithm, including both geometric and adaptive annealing schedules, 3 neighborhood functions, and 20 neighborhood diameters. An optimal configuration was found that produced the best results in less than 60 seconds, well within the window necessary to dynamically reschedule imaging tasks as new plates are entered into the system.

Celebrating crystallography from K to 12 at CIMF, Montreal

If crystallography is well known to many scientists, this is not true for the general public. Aims of the international year of crystallography include the desire to increase public awareness, to stimulate the intellectual curiosity of students and to promote crystallographic education. At the College International Marie de France, we decided to tackle these challenges and celebrate IYCR2014 with students from kindergarten to grade 10 and by an outreach with the Universite de Montreal (UdeM). We will illustrate how, beyond appreciating the mesmerizing ordered crystal growth from a saturated solution, studying and performing crystallization activities has a proven track record for fostering academic and practical skills. If crystallography starts by the Bragg's iconic law, then most undergraduate students will not appreciate it. However, crystals do have an intrinsic aesthetic appeal that can serve as a starting point to stimulate students' interest. Instead of teaching crystallography as a discipline, could we not teach key scientific concepts through it? We devised three distinct approaches addressing skills and notions appropriate to students in kindergarten, primary or grade 10 school levels. Younger pupils can make descriptions, comparisons and learn about symmetry and growth based upon the observation of snowflakes or crystal growth from solution, encouraging them to formulate basic concepts regarding solubility and changes in states of matter. Growing crystals allows primary students to practice mathematical skills such as measuring angles, masses, volumes and describing shapes. This sets the stage for problem solving using the scientific method. Older students take part in workshops ranging from diffraction patterns generated by lasers to simulating growth with cellular automata. They also undertake protein crystallization experiments in partnership with UdeM thereby offering unique access to crystallographic facilities and insight into scientific research.

Nanoliter-scale protein crystallization and screening with a microfluidic droplet robot.

Large-scale screening of hundreds or even thousands of crystallization conditions while with low sample consumption is in urgent need, in current structural biology research. Here we describe a fully-automated droplet robot for nanoliter-scale crystallization screening that combines the advantages of both automated robotics technique for protein crystallization screening and the droplet-based microfluidic technique. A semi-contact dispensing method was developed to achieve flexible, programmable and reliable liquid-handling operations for nanoliter-scale protein crystallization experiments. We applied the droplet robot in large-scale screening of crystallization conditions of five soluble proteins and one membrane protein with 35–96 different crystallization conditions, study of volume effects on protein crystallization, and determination of phase diagrams of two proteins. The volume for each droplet reactor is only ca. 4–8 nL. The protein consumption significantly reduces 50–500 fold compared with current crystallization stations.