Crystallization Propensity Research Articles

Effect of sintering behavior and phase evolution on glass-ceramics entirely derived from ferrochrome slag and fluorite tailings

The recycling of solid waste is widely regarded as the one of the most crucial issues of sustainability. This is particularly evident in the industrial sector, where the production of slags is huge and their handling presents a significant challenge. In this study, solid wastes from the industrial sector, namely ferrochrome slag (Fs) and fluorite tailings (Ft), were employed for the preparation of glass-ceramics by the sintering method. Three glasses were prepared by using the melt-quenching method, with a weight ratio of 60∼70 % Fs and 40∼30 % Ft (designated S30, S35, and S40, where the higher number indicates a greater Fs content). The sintering and crystallization behaviors of these glasses were characterized by hot-stage microscopy (HSM) and differential thermal analysis (DTA). It was observed that S40 glass exhibited a weak sintering capacity in comparison to that of S30 and S35 glass, which can be attributed to the robust crystallization propensity of S40 glass. The coarser glass frits were subjected to sintering experiments at varying temperatures, which demonstrates that the predominant crystalline phase is augite and that Cr2O3 remains present in all sintered samples. Besides, a transition of the Cr2O3 crystal into uvarovite was observed in S40 glass sintered at 1050 °C. The sintering time controlling experiment demonstrated that both wollastonite and the Ca-rich flow formed during sintering are vital for the formation of uvarovite. The formation of uvarovite can enhance the hardness of GCs. All GCs samples sintered at 1050 °C and 1120 °C exhibit comparable mechanical performance.

PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.

Protein dynamics inform protein structure: An interdisciplinary investigation of protein crystallization propensity

Protein dynamics inform protein structure: An interdisciplinary investigation of protein crystallization propensity

Decoding crystallization behavior of aluminoborosilicate glasses: From structural descriptors to Quantitative Structure – Property Relationship (QSPR) based predictive models

Successful decoding of structural descriptors controlling the crystallization in multicomponent functional glasses can pave the way for the transition from the trial-and-error approach and empirical modeling for glass/glass-ceramic composition design toward more rational and scientifically rigorous Quantitative Structure-Property Relationship (QSPR) based models. However, due to the compositional and structural complexity of multicomponent glasses and the longer time and length scales associated with nucleation, the development and validation of QSPR models are still in it's infancy. The work presented in the article is an attempt to leap forward in this pursuit by combining the strengths of experimental and computational materials science to decode the chemo-structural drivers that promote or suppress nucleation and crystal growth in alkali/alkaline-earth aluminoborosilicate glasses leading to the development of a QSPR-based model (powered by MD simulations). The results reveal the following two descriptors that govern the nucleation and crystallization of a particular aluminosilicate phase in the functional glasses: (1) degree of mixing between the SiO4 and AlO4 units, i.e., Si–O–Al linkages, and (2) difference/similarity between the short-to-intermediate range ordering in the glass structure to that of the structure of corresponding crystalline phase. Based on the established composition–structure–crystallization behavior relationships, a cluster analysis based QSPR model has been developed (and tested) to predict the propensity of nepheline (and anorthite) crystallization in the investigated glasses. The model has been tested on several compositions from the present and previous studies and has successfully predicted the crystallization propensity of all glass compositions, even in cases where previous empirical and semi-empirical models were unsuccessful.

Systematic enhancement of protein crystallization efficiency by bulk lysine-to-arginine (KR) substitution.

Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x-ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal-packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine-to-arginine (KR) substitutions should improve crystallization. To test this hypothesis, we developed software that ranks lysine sites in a target protein based on the redundancy-corrected KR substitution frequency in homologs. This software can be run interactively on the worldwide web at https://www.pxengineering.org/. We demonstrate that three unrelated single-domain proteins can tolerate 5-11 KR substitutions with at most minor destabilization, and, for two of these three proteins, the construct with the largest number of KR substitutions exhibits significantly enhanced crystallization propensity. This approach rapidly produced a 1.9 Å crystal structure of a human protein domain refractory to crystallization with its native sequence. Structures from Bulk KR-substituted domains show the engineered arginine residues frequently make hydrogen-bonds across crystal-packing interfaces. We thus demonstrate that Bulk KR substitution represents a rational and efficient method for probabilistic engineering of protein surface properties to improve crystallization.

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks.

Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)2, Im=C3H3N2-) glasses, as well as for the formation of ZIF-zni (Zn(Im)2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses.

Increasing the bulk of the 1TEL-target linker and retaining the 10×His tag in a 1TEL-CMG2-vWa construct improves crystal order and diffraction limits.

TELSAM-fusion crystallization has the potential to become a revolutionary tool for the facile crystallization of proteins. TELSAM fusion can increase the crystallization rate and enable crystallization at low protein concentrations, in some cases with minimal crystal contacts [Nawarathnage et al. (2022), Open Biol. 12, 210271]. Here, requirements for the linker composition between 1TEL and a fused CMG2 vWa domain were investigated. Ala-Ala, Ala-Val, Thr-Val and Thr-Thr linkers were evaluated, comparing metrics for crystallization propensity and crystal order. The effect on crystallization of removing or retaining the purification tag was then tested. It was discovered that increasing the linker bulk and retaining the 10×His purification tag improved the diffraction resolution, likely by decreasing the number of possible vWa-domain orientations in the crystal. Additionally, it was discovered that some vWa-domain binding modes are correlated with scrambling of the 1TEL polymer orientation in crystals and an effective mitigation strategy for this pathology is presented.

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially determined and screened by modeling. As a result, this study created a new pipeline aimed at using protein sequence to predict protein crystallization propensity in the protein material production stage, purification stage and production of crystal stage. The newly created pipeline proposed a new feature selection method, which involves combining Chi-square (${\chi }^{2}$) and recursive feature elimination together with the 12 selected features, followed by a linear discriminant analysisfor dimensionality reduction and finally, a support vector machine algorithm with hyperparameter tuning and 10-fold cross-validation is used to train the model and test the results. This new pipeline has been tested on three different datasets, and the accuracy rates are higher than the existing pipelines. In conclusion, our model provides a new solution to predict multistage protein crystallization propensity which is a big challenge in computational biology.

Computational protein engineering for systematic enhancement of crystallization propensity

Computational protein engineering for systematic enhancement of crystallization propensity

Cocrystal Synthesis through Crystal Structure Prediction.

Crystal structure prediction (CSP) is an invaluable tool in the pharmaceutical industry because it allows to predict all the possible crystalline solid forms of small-molecule active pharmaceutical ingredients. We have used a CSP-based cocrystal prediction method to rank ten potential cocrystal coformers by the energy of the cocrystallization reaction with an antiviral drug candidate, MK-8876, and a triol process intermediate, 2-ethynylglyclerol. For MK-8876, the CSP-based cocrystal prediction was performed retrospectively and successfully predicted the maleic acid cocrystal as the most likely cocrystal to be observed. The triol is known to form two different cocrystals with 1,4-diazabicyclo[2.2.2]octane (DABCO), but a larger solid form landscape was desired. CSP-based cocrystal screening predicted the triol-DABCO cocrystal as rank one, while a triol-l-proline cocrystal was predicted as rank two. Computational finite-temperature corrections enabled determination of relative crystallization propensities of the triol-DABCO cocrystals with different stoichiometries and prediction of the triol-l-proline polymorphs in the free-energy landscape. The triol-l-proline cocrystal was obtained during subsequent targeted cocrystallization experiments and was found to exhibit an improved melting point and deliquescence behavior over the triol-free acid, which could be considered as an alternative solid form in the synthesis of islatravir.

Fundamentals of crystallization at oil-brine interfaces for conformance control and oil spill remediation applications

We have investigated the interfacial properties at a brine-hydrocarbon boundary with the prospect of understanding the crystallization process that takes place when certain electrolytes are present in the brine and when certain surfactants are present in the hydrocarbon phase. Solutions of nine cations and five carboxylic acids were investigated. We found that some combinations of electrolytes and acids than others are solidified at the brine-hydrocarbon interface. These interfacial phenomena were studied with an optical force tensiometer setup with a so-called buoyant droplet configuration. aim to utilize these specific interfacial formed crystals to design a method to form crystal plugs in high-flow sections (e.g., at fractures) of an oil reservoir. Ultimately, our studies target a real reservoir environment and we have included a range of different crudes with varying total acid numbers (TAN) and viscosities to bring the study a little closer to a realistic upstream setting. The electrolyte-surfactant combination that gives rise to the most clear-cut formation of crystals directly at the interface is involving Zn2+ or Cu2+ and dodecanoic acid (C11H23COOH). Several of the systems under study appears to be forming crystals within the hydrocarbon phase; these crystals are more likely a result of the surfactant-associated diffusive transfer of cations into the hydrocarbon phase with subsequent complexation. When it comes to the crudes, the findings are somewhat more complex, the acidity of the oil, not surprisingly, influences the interfacial properties including the crystallization propensity. The relationship between one-dimensional parameters such as TAN and crystallization propensity is not straightforwardly interpreted but it does seem like proper spiking can induce crystallization in most oils and that it is most favorably done in light oils where the diffusivity of the acids is largest. We have made the applicational space wider by also showing that the crystallization/interfacial phenomena can induce confinement of oil on a water surface; an observation that opens applications in oil spill remedies.

Polymorphic nanobody crystals as long-acting intravitreal therapy for wet age-related macular degeneration.

Wet age-related macular degeneration (wet AMD) is the most common cause of blindness, and chronic intravitreal injection of anti-vascular endothelial growth factor (VEGF) proteins has been the dominant therapeutic approach. Less intravitreal injection and a prolonged inter-injection interval are the main drivers behind new wet AMD drug innovations. By rationally engineering the surface residues of a model anti-VEGF nanobody, we obtained a series of anti-VEGF nanobodies with identical protein structures and VEGF binding affinities, while drastically different crystallization propensities and crystal lattice structures. Among these nanobody crystals, the P212121 lattice appeared to be denser and released protein slower than the P1 lattice, while nanobody crystals embedding zinc coordination further slowed the protein release rate. The polymorphic protein crystals could be a potentially breakthrough strategy for chronic intravitreal administration of anti-VEGF proteins.

Implications of Drug-Polymer Interactions on Time-Temperature-Transformation: A Tool to Assess the Crystallization Propensity in Amorphous Solid Dispersions.

The critical cooling rate (CRcrit) to prevent drug crystallization during the preparation of nifedipine amorphous solid dispersions (ASDs) was determined through the time-temperature-transformation (TTT) diagram. ASDs were prepared with polyvinylpyrrolidone, hydroxypropylmethyl cellulose acetate succinate, and poly(acrylic acid). ASDs were subjected to isothermal crystallization over a wide temperature range, and the time and temperature dependence of nifedipine crystallization onset time (tC) was determined by differential scanning calorimetry (DSC) and synchrotron X-ray diffractometry. TTT diagrams were generated for ASDs, which provided the CRcrit for the dispersions prepared with each polymer. The observed differences in CRcrit could be explained in terms of differences in the strength of interactions. Stronger drug-polymer interactions led to longer tC and decreased CRcrit. The effect of polymer concentrations (4-20% w/w) was also influenced by the strength of the interaction. The CRcrit of amorphous NIF was ∼17.5 °C/min. Addition of 20% w/w polymer resulted in a CRcrit of ∼0.05, 0.2, and 11 °C/min for the dispersions prepared with PVP, HPMCAS, and PAA, respectively.

Lipid-assisted PEG-b-PLA nanoparticles with ultrahigh SN38 loading capability for efficient cancer therapy.

The topoisomerase I inhibitor, 7-ethyl-10-hydroxycamptothecin (SN38), has demonstrated potent anticancer activity. However, its clinical application is hindered by its low solubility and high crystallization propensity, which further complicates its encapsulation into nanoparticles for systemic delivery. Herein, we explore the utilization of lipid-assisted poly(ethylene glycol)-block-poly(D,L-lactide) (PEG-b-PLA) nanoparticles to achieve ultrahigh loading capability for SN38. Through the introduction of cationic, anionic, or neutral lipids, the SN38 loading efficiency and loading capacity is elevated to >90% and >10% respectively. These lipids efficiently attenuate the intermolecular π-π stacking of SN38, thereby disrupting its crystalline structure. Moreover, we assess the therapeutic activity of SN38-loaded formulations in various tumor models and identify an anionic lipid 1,2-dioleoyl-sn-glycero-3-phospho-(1'-rac-glycerol) sodium salt (DOPG)-assisted formulation that exhibits the highest anticancer activity and has favorable biosafety. Overall, our findings present a simple and robust strategy to achieve ultrahigh loading efficiency of SN38 using commonly employed PEG-b-PLA nanoparticles, opening up a new avenue for the systemic delivery of SN38.

GCmapCrys: Integrating graph attention network with predicted contact map for multi-stage protein crystallization propensity prediction

X-ray crystallography is the major approach for atomic-level protein structure determination. Since not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity is critical to guiding the experimental design and improving the success rate of X-ray crystallography experiments. In this work, we proposed a new deep learning pipeline, GCmapCrys, for multi-stage crystallization propensity prediction through integrating graph attention network with predicted protein contact map. Experimental results on 1548 proteins with known crystallization records demonstrated that GCmapCrys increased the value of Matthew's correlation coefficient by 37.0% in average compared to state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of GCmapCrys lie in the efficiency of the graph attention network with predicted contact map, which effectively associates the residue-interaction knowledge with crystallization pattern. Meanwhile, the designed four sequence-based features can be complementary to further enhance crystallization propensity proprediction.

Detecting Crystallinity in Amorphous Solid Dispersions Using Dissolution Testing: Considerations on Properties of Drug Substance, Drug Product, and Selection of Dissolution Media

Dissolution testing has long been used to monitor product quality. Its role in quality control of amorphous solid dispersion (ASD) formulations is relatively new. In the presence of the crystalline phase, the dissolution of ASDs is determined by the dynamics between the dissolution rate of the amorphous solids and the rate of crystal growth. The detection of crystalline phase by dissolution test has not been well understood in the context of drug properties, formulation characteristics and dissolution test variables. This study systematically evaluated the impact of key parameters such as intrinsic crystallization tendency of the API, drug loading, extent of dissolution sink conditions and level of crystallinity on the ASD dissolution behavior. The results indicated diverse dissolution behaviors due to the differences in the intrinsic crystallization propensity of the drug, the drug loading, the ASD polymers and the dissolution sink index. Each of the complex dissolution profiles were interpreted based on visual observations during dissolution, the appropriate sink index based on the amorphous solubility, and the competition between drug dissolution versus crystallization. The findings of this study provide insights towards the various considerations that should be taken into account towards rationally developing a discriminatory dissolution method.

An Artificial Gut/Absorption Simulator: Simultaneous Evaluation of Desupersaturation and Absorption from Ketoconazole Supersaturated Solutions

For supersaturating formulations of BCS-II compounds, which by definition have high intestinal permeability, a closed USP apparatus does not provide the necessary absorptive conditions during dissolution. To address this, an artificial gut simulator (AGS) has been constructed consisting of a 2.5 mL donor compartment in which a hollow fiber-based absorption module is suspended. Drug from donor diffuses across the hollow fiber membrane to be absorbed by the continuously flowing intraluminal receiver fluid. The membrane surface area and intraluminal fluid flow rate are tuned to obtain the physiologically observed absorption rate constant for a weakly basic, poorly water-soluble model compound, ketoconazole (KTZ). Supersaturated solutions of KTZ were generated in the donor in pH 6.5 phosphate buffer by the pH-shift method in the absence (closed system, control) and presence (open system, biorelevant) of an optimally or suboptimally tuned absorption module. Drug concentrations in the donor and intraluminal fluids were determined by in-line UV spectroscopy. The presence of an absorptive sink reduced the supersaturated solution's crystallization propensity, more so in the case of the optimally tuned AGS. This study demonstrates the significance of simulating absorption of drug at a physiological rate during dissolution studies, especially to predict the performance of formulations of BCS-II drugs.

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.

Influence of the Nature of Aliphatic Hydrophobic Physical Crosslinks on Water Crystallization in Copolymer Hydrogels.

The local environment within a hydrogel influences the properties of water, including the propensity for ice crystallization. Water-swollen amphiphilic copolymers produce tunable nanoscale environments, which are defined by hydrophobic associations, for the water molecules. Here, the antifreeze properties for equilibrium-swollen amphiphilic copolymers with a common hydrophilic component, hydroxyethyl acrylate (HEA), but associated through crystalline (octadecyl acrylate, ODA) or rubbery (ethylhexyl acrylate, EHA) hydrophobic segments, are examined. Differences in the efficacy of the associations can be clearly enunciated from compositional solubility limits for the copolymers in water (<2.6 mol % ODA vs ≤14 mol % EHA), and these differences can be attributed to the strength of the association. The equilibrium-swollen HEA-ODA copolymers are viscoelastic solids, while the swollen HEA-EHA copolymers are viscoelastic liquids. Cooling these swollen copolymers to nearly 200 K induces some crystallization of the water, where the fraction of water frozen depends on the details of the nanostructure. Decreasing the mean free path of water by increasing the ODA composition from 10 to 25 mol % leads to fractionally more unfrozen water (66-87%). The swollen HEA-EHA copolymers only marginally inhibit ice (<13%) except with 45 mol % EHA, where nearly 60% of the water remains amorphous on cooling to 200 K. In general, the addition of the EHA leads to less effective ice inhibition than analogous covalently crosslinked HEA hydrogels (19.9 ± 1.8%). These results illustrate that fluidity of confining surfaces can provide pathways for critical nuclei to form and crystal growth to proceed.

Fragile‐to‐Strong Transition in Phase‐Change Material Ge3Sb6Te5

AbstractChalcogenide phase‐change materials combine a remarkable set of properties that makes them promising candidates for future non‐volatile memory applications. Binary data storage exploits the high contrast in electrical and optical properties between the covalent amorphous and metavalent crystalline phase. Here the authors perform an analysis of the liquid phase kinetics of the phase‐change material Ge3Sb6Te5, which is the key to ultrafast switching speeds. By employing four experimental techniques, the viscosity is measured over sixteen orders of magnitude despite its propensity for fast crystallization. These measurements reveal that the liquid undergoes a transition in viscosity–temperature dependence associated with a liquid–liquid phase transition. The system exhibits a shallow viscosity change with temperature near the glass transition which stabilizes the memory cells in the amorphous state and which limits the severity of relaxation processes. Meanwhile, when heated during the writing process, the fragility increases to more than double, causing the viscosity to drop rapidly enabling a nanosecond crystallization speed. This change in viscosity–temperature dependence is highly unusual among glass forming liquids and is reminiscent of the behavior of water. This viscosity transition is also key to the technological success of phase‐change materials for computer memory applications.