Protein Phase Behavior Research Articles

An intrinsically disordered pathological prion variant Y145Stop converts into self-seeding amyloids via liquid–liquid phase separation

Biomolecular condensation via liquid-liquid phase separation of intrinsically disordered proteins/regions (IDPs/IDRs) along with other biomolecules is proposed to control critical cellular functions, whereas aberrant phase transitions are associated with a range of neurodegenerative diseases. Here, we show that a disease-associated stop codon mutation of the prion protein (PrP) at tyrosine 145 (Y145Stop), resulting in a truncated, highly disordered, N-terminal IDR, spontaneously phase-separates into dynamic liquid-like droplets. Phase separation of this highly positively charged N-terminal segment is promoted by the electrostatic screening and a multitude of weak, transient, multivalent, intermolecular interactions. Single-droplet Raman measurements, in conjunction with an array of bioinformatic, spectroscopic, microscopic, and mutagenesis studies, revealed a highly mobile internal organization within the liquid-like condensates. The phase behavior of Y145Stop is modulated by RNA. Lower RNA:protein ratios promote condensation at a low micromolar protein concentration under physiological conditions. At higher concentrations of RNA, phase separation is abolished. Upon aging, these highly dynamic liquid-like droplets gradually transform into ordered, β-rich, amyloid-like aggregates. These aggregates formed via phase transitions display an autocatalytic self-templating characteristic involving the recruitment and binding-induced conformational conversion of monomeric Y145Stop into amyloid fibrils. In contrast to this intrinsically disordered truncated variant, the wild-type full-length PrP exhibits a much lower propensity for both condensation and maturation into amyloids, hinting at a possible protective role of the C-terminal domain. Such an interplay of molecular factors in modulating the protein phase behavior might have much broader implications in cell physiology and disease.

Learning the molecular grammar of protein condensates from sequence determinants and embeddings

Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed, with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, we established an in silico strategy for understanding on a global level the associations between protein sequence and phase behavior and further constructed machine-learning models for predicting protein liquid-liquid phase separation (LLPS). Our analysis highlighted that LLPS-prone proteins are more disordered, less hydrophobic, and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database and that they show a fine balance in their relative content of polar and hydrophobic residues. To further learn in a hypothesis-free manner the sequence features underpinning LLPS, we trained a neural network-based language model and found that a classifier constructed on such embeddings learned the underlying principles of phase behavior at a comparable accuracy to a classifier that used knowledge-based features. By combining knowledge-based features with unsupervised embeddings, we generated an integrated model that distinguished LLPS-prone sequences both from structured proteins and from unstructured proteins with a lower LLPS propensity and further identified such sequences from the human proteome at a high accuracy. These results provide a platform rooted in molecular principles for understanding protein phase behavior. The predictor, termed DeePhase, is accessible from https://deephase.ch.cam.ac.uk/.

Membrane Protein Structures in Lipid Bilayers; Small-Angle Neutron Scattering With Contrast-Matched Bicontinuous Cubic Phases.

This perspective describes advances in determining membrane protein structures in lipid bilayers using small-angle neutron scattering (SANS). Differentially labeled detergents with a homogeneous scattering length density facilitate contrast matching of detergent micelles; this has previously been used successfully to obtain the structures of membrane proteins. However, detergent micelles do not mimic the lipid bilayer environment of the cell membrane in vivo. Deuterated vesicles can be used to obtain the radius of gyration of membrane proteins, but protein-protein interference effects within the vesicles severely limits this method such that the protein structure cannot be modeled. We show herein that different membrane protein conformations can be distinguished within the lipid bilayer of the bicontinuous cubic phase using contrast-matching. Time-resolved studies performed using SANS illustrate the complex phase behavior in lyotropic liquid crystalline systems and emphasize the importance of this development. We believe that studying membrane protein structures and phase behavior in contrast-matched lipid bilayers will advance both biological and pharmaceutical applications of membrane-associated proteins, biosensors and food science.

Protein Supersaturation Drives Innate Immunity Signaling

Multiple signaling proteins of the human innate immune system execute cell fate decisions by assembling into large macromolecular complexes, known as signalosomes, when stimulated by pathogen- or danger-associated molecules. Recent observations that some signalosome assemblies can perpetuate themselves in prion-like fashion in vivo suggest that their activation -- and the corresponding cell fate decisions -- may be thermodynamically pre-determined. In other words, signalosome assembly is inevitable, but postponed in the absence of stimulation by structurally-encoded nucleation barriers to self-assembly. We explored this hypothesis using DAmFRET, a newly developed flow cytometric assay for prion-like protein phase behavior, to identify nucleation barriers that govern self-assembly by homotypic interaction domains of the death fold, TIR, and RHIM families from over one-hundred human innate immune signaling proteins. We discovered thirty-six proteins with robust prion-like activity, including the major signalosome scaffolding proteins, or “adaptors”. We further demonstrate for a subset of the corresponding full length proteins that nucleation barriers also control the endogenous proteins’ activities in human THP-1 monocytes, and that hyper- and hypo-activating disease-causing mutations respectively decrease or increase the barriers. We provide a logic for prion-like activity in the architecture of innate immune signaling pathways, and conclude that adaptor protein supersaturation functions to store potential energy and then release it upon nucleation for rapid and effectively irreversible switch-like control over innate immunity signaling.

Repurposing a microfluidic formulation device for automated DNA construction.

Microfluidic applications have expanded greatly over the past decade. For the most part, however, each microfluidics platform is developed with a specific task in mind, rather than as a general-purpose device with a wide-range of functionality. Here, we show how a microfluidic system, originally developed to investigate protein phase behavior, can be modified and repurposed for another application, namely DNA construction. We added new programable controllers to direct the flow of reagents across the chip. We designed the assembly of a combinatorial Golden Gate DNA library using TeselaGen DESIGN software and used the repurposed microfluidics platform to assemble the designed library from off-chip prepared DNA assembly pieces. Further experiments verified the sequences and function of the on-chip assembled DNA constructs.

Basin Mapping Method for Extracting Comparative Assessments of Protein Phase Behavior from In Vivo Measurements

To uncover sequence-encoded driving forces for and the mechanisms of protein-specific phase transitions, we require knowledge of the underlying phase diagram, the degree of supersaturation, and the distance from critical point. Recent technological developments [1] based on Distributed Amphifluoric FRET (DAmFRET) allow the quantitative tracking of phase transitions as a function of protein expression in yeast. These in vivo experiments afford unprecedented throughput while enabling quantitative comparisons of protein-specific phase behavior. For a given protein, the DAmFRET dataset provides 2-dimensional histograms of AmFRET values and expression levels. Here, we adapt advances from energy landscape theory, specifically the inherent structure formalism of Stillinger and coworkers [2], to uncover protein-specific free energy functionals that underlie the phase behavior observed in DAmFRET measurements. We have developed and deployed a hybrid steepest-descent basin mapping method that identifies and quantifies major expression level dependent basins using the 2-dimensional histograms extracted from DAmFRET measurements. This approach identifies; (i) basins, which are envelopes in the 2-dimensional histograms; (ii) the relative weights of basins, and (iii) the centers of basins as defined by the steepest descent mapping. Results from the basin mapping procedure are used to infer protein-specific free energy functionals and these free energy functionals are used to reconstruct phase diagrams. Our analysis enables quantitative comparisons of phase transitions encoded by different protein sequences in a model cellular system. 1. T. Khan et al., (2018), Molecular Cell, 71: 155-168. 2. F. H. Stillinger, Energy Landscapes, Inherent Structures, and Condensed Matter Phenomena, Princeton University Press, 2016.

Label-Free Analysis of Protein Aggregation and Phase Behavior.

Phase transitions of protein molecules are central to biological function and malfunction. One such transition commonly encountered in nature is the conversion of soluble monomeric states into solid phases, which include crystals and amyloid fibrils, the latter of which are associated with the onset and development of neurodegenerative diseases. Monitoring aggregate formation and protein phase behavior is essential in gaining mechanistic insights into these fundamental processes. Fluorescence techniques have proven invaluable in observing biological molecules; yet, most such approaches rely on the use of an extrinsic fluorophore that binds to the molecule of interest, the installation of which can perturb the molecular systems under study. However, most proteins also possess aromatic amino acids within their peptide sequence and therefore exhibit intrinsic fluorescence. Here, we show that by measuring in space and time tryptophan autofluorescence for three proteins, reconstituted silk fibroin, β-lactoglobulin, and lysozyme, fibrillar self-assembly can be monitored accurately and without the need for extrinsic dyes. When fibrillar protein self-assembly takes place, hydrophobic burial occurs, resulting in the minimization of exposed tryptophan residues to the solvent and consequently leading to an increase in protein autofluorescence. Moreover, by employing a droplet-microfluidic approach to confine protein self-assembly in space, we demonstrate that intrinsic fluorescence can be used to image protein nanofibrils in a label-free manner and that the microstructural analysis obtained from intrinsic fluorescence microscopy correlates well with that from samples treated with extrinsic dyes. Finally, our results show that protein autofluorescence is not limited to the observation of β-sheet-rich structures, but can also be used to distinguish between different types of solid phases including spherulites and crystals, making this approach suitable for overall characterization of protein phase transition phenomena.

Predicting protein-protein interactions using the ePC-SAFT equation-of-state

Within the last decade, thermodynamic models have increasingly grown into the pharmaceutical industry, becoming an essential part in both, process development, as well as formulation development of small-molecule drugs. They are to an increasing degree accepted as a valuable building block to evaluate e.g. small-molecule formulation conditions. In contrast, modeling of highly complex (bio)-molecules (e.g. monoclonal antibodies) remains a challenging task and thermodynamic modeling is only rarely applied for e.g. protein formulations in the biopharmaceutical industry.Within this work, we developed a thermodynamic modeling approach to access protein-protein and protein-excipient interactions (via second osmotic virial coefficients B22 and cross viral coefficients B23) for immunoglobulin G (IgG) and bovine serum albumin (BSA) in the presence of different excipients (NaCl, sucrose, L-histidine, L-arginine) at defined buffer conditions using the ePC-SAFT equation-of-state. The pure component parameters of IgG were determined based on the amino acid sequence and further fitted to experimentally determined static light scattering signals of IgG at buffer conditions. This approach allows to predict protein-protein and protein-excipient interactions of BSA and IgG in the presence of different excipient types and thus give access to protein phase behavior in these complex solutions. Applying this approach further allows to gain a mechanistic understanding on the complex interactions in solution that govern the phase behavior, and thus can aid an optimized formulation development.

A phase diagram-based toolbox to assess the impact of freeze/thaw ramps on the phase behavior of proteins.

The influence of process parameters during freeze/thaw (FT) operations is essential for the preservation of the protein stability/activity during production and storage processes in the biopharmaceutical industry. Process parameters, such as FT ramps, the final storage time and temperature, affect the occurring FT stress onto the target protein in different ways. FT stress includes cold denaturation, freeze concentration, and ice crystal formation which can result in protein aggregation. To visualize the impact of variations in FT ramps, descriptors such as solubility, phase behavior and crystal morphology were evaluated. The phase diagram-based toolbox in combination with an HTS-compatible cryo-device allowed the identification of suitable ramping schemes during FT operations. It could be clearly shown that rapid operations are needed above the glass transition temperature of the target protein to circumvent precipitation during FT cycles. Finally, a stability index is introduced which allows ranking of thesystems investigated.

LLPSDB: a database of proteins undergoing liquid-liquid phase separation in vitro.

Liquid-liquid phase separation (LLPS) leads to a conversion of homogeneous solution into a dense phase that often resembles liquid droplets, and a dilute phase. An increasing number of investigations have shown that biomolecular condensates formed by LLPS play important roles in both physiology and pathology. It has been suggested the phase behavior of proteins would be not only determined by sequences, but controlled by micro-environmental conditions. Here, we introduce LLPSDB (http://bio-comp.ucas.ac.cn/llpsdb or http://bio-comp.org.cn/llpsdb), a web-accessible database providing comprehensive, carefully curated collection of proteins involved in LLPS as well as corresponding experimental conditions in vitro from published literatures. The current release of LLPSDB incorporates 1182 entries with 273 independent proteins and 2394 specific conditions. The database provides a variety of data including biomolecular information (protein sequence, protein modification, nucleic acid, etc.), specific phase separation information (experimental conditions, phase behavior description, etc.) and comprehensive annotations. To our knowledge, LLPSDB is the first available database designed for LLPS related proteins specifically. It offers plenty of valuable resources for exploring the relationship between protein sequence and phase behavior, and will enhance the development of phase separation prediction methods, which may further provide more insights into a comprehensive understanding of LLPS in cellular function and related diseases.

Time-Dependent Multi-Light-Source Image Classification Combined With Automated Multidimensional Protein Phase Diagram Construction for Protein Phase Behavior Analysis.

Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious time-consuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images. This study investigates the combined effect of end point and time-dependent image features obtained from cross-polarized and ultraviolet light features, supplementary to visible light, on protein phase diagram image classification. In addition, external validation was performed to evaluate the classification algorithm's applicability to support protein phase diagram scoring. The predicted protein phase behavior classes were subsequently used to automatically construct multidimensional protein phase diagrams to prevent image information loss without complicating the used image classification algorithm. Combining end point and time-dependent features from 3 light sources resulted in a balanced accuracy of 86.4 ± 4.3%, which is comparable to or better than more complex classifiers reported in literature. External validation resulted in a correct formulation classification rate of 91.7%. Subsequent automated construction of the multidimensional protein phase diagrams, using predicted classes, allowed visualization of details such as crystallization rate and protein phase behavior type coexistence.

Correlating multidimensional short-term empirical protein properties to long-term protein physical stability data via empirical phase diagrams

Identification of long-term stable biopharmaceutical formulations is essential for biopharmaceutical product development. Reduction of the number of long-term storage experiments and a well-defined formulation search space requires knowledge-based formulation screenings and a detailed protein phase behavior understanding. To achieve this, short-term analytical techniques can serve as predictors for long-term protein phase behavior. Protein phase behavior studies that investigate this concept commonly display shortcomings such as limited and small datasets, sample adjustments, or simplistic data analysis. To overcome these shortcomings, 150 unique lysozyme solutions were analyzed using six different short-term analytical techniques. Lysozyme’s structural properties, conformational stability, colloidal stability, surface charge, and surface hydrophobicity were obtained directly after formulation preparation. Employing the empirical phase diagram method, this short-term data was correlated to long-term physical stability data obtained during 40 days of storage. Short-term protein properties showed partial correlation to long-term phase behavior. Structural differences, changing surface properties, colloidal stability, and conformation stability as a function of formulation conditions were observed. This study contributes to long-term protein phase behavior research by presenting a systematic, data-dependent, and multidimensional data evaluation workflow to create a comprehensive overview of short-term protein analytics in relation to long-term protein phase behavior.

Modeling the Effects of Ligand Binding on the Phase Behavior of Aggregation-Prone Proteins

Many diseases are associated with aberrant protein aggregation. Recently, there has been resurgent interest in co-opting the framework of phase transitions to understand the distinct concentration thresholds separating different phases of aggregation-prone molecules. This analogy, which was purely conceptual, has now been shown to have quantitative significance. Importantly, instead of searching for elusive “toxic conformations”, dealing with the deleterious consequences of protein aggregation and phase separation can be approached via methods that alter or modulate the sequence-specific phase behavior. One route to accomplishing this is by shifting phase boundaries via ligand binding through a phenomenon known as polyphasic linkage. Disease-causing mutations, such as polyglutamine expansions in the N-terminal region of huntingtin, lead to aggregation and phase separation at lower, physiologically relevant concentration thresholds. Ligand binding can shift these phase boundaries based on the phase that is preferentially bound. Here, we show that a ligand can reduce the driving force for protein aggregation in vitro and may help to explain the reduced cellular aggregation and toxicity observed when overexpressed in the context of Huntington's disease. Through a combined biophysical and computational approach, we further show that multi-valency in binding appears to be important for shifting phase boundaries in therapeutically relevant directions. However, in the context of the cell, a protein such as huntingtin has a large interactome. Each of the interaction partners will have a different effect on the intrinsic phase boundaries and therefore, designing a therapeutic approach that leverages the tenets of polyphasic linkage requires a general framework that considers the competing effects within a network of interactions. Using the huntingtin example, we develop a framework for designing modulators of phase behavior via polyphasic linkage that accounts for competing interactions within an interactome.

Soft-Matter Approaches for Controlling Food Protein Interactions and Assembly.

Animal- and plant-based proteins are present in a wide variety of raw and processed foods. They play an important role in determining the final structure of food matrices. Food proteins are diverse in terms of their biological origin, molecular structure, and supramolecular assembly. This diversity has led to segmented experimental studies that typically focus on one or two proteins but hinder a more general understanding of food protein structuring as a whole. In this review, we propose a unified view of how soft-matter physics can be used to control food protein assembly. We discuss physical models from polymer and colloidal science that best describe and predict the phase behavior of proteins. We explore the occurrence of phase transitions along two axes: increasing protein concentration and increasing molecular attraction. This review provides new perspectives on the link between the interactions, phase transitions, and assembly of proteins that can help in designing new food products and innovative food processing operations.

Protein Phase Separation as a Stress Survival Strategy.

Cells under stress must adjust their physiology, metabolism, and architecture to adapt to the new conditions. Most importantly, they must down-regulate general gene expression, but at the same time induce synthesis of stress-protective factors, such as molecular chaperones. Here, we investigate how the process of phase separation is used by cells to ensure adaptation to stress. We summarize recent findings and propose that the solubility of important translation factors is specifically affected by changes in physical-chemical parameters such temperature or pH and modulated by intrinsically disordered prion-like domains. These stress-triggered changes in protein solubility induce phase separation into condensates that regulate the activity of the translation factors and promote cellular fitness. Prion-like domains play important roles in this process as environmentally regulated stress sensors and modifier sequences that determine protein solubility and phase behavior. We propose that protein phase separation is an evolutionary conserved feature of proteins that cells harness to regulate adaptive stress responses and ensure survival in extreme environmental conditions.

Water on hydrophobic surfaces: mechanistic modeling of polyethylene glycol-induced protein precipitation

For the purification of biopharmaceutical proteins, liquid chromatography is still the gold standard. Especially with increasing product titers, drawbacks like slow volumetric throughput and high resin costs lead to an intensifying need for alternative technologies. Selective preparative protein precipitation is one promising alternative technique. Although the capability has been proven, there has been no precipitation process realized for large-scale monoclonal antibody (mAb) production yet. One reason might be that the mechanism behind protein phase behavior is not completely understood and the precipitation process development is still empirical. Mechanistic modeling can be a means for faster, material-saving process development and a better process understanding at the same time. In preparative chromatography, mechanistic modeling was successfully shown for a variety of applications. Lately, a new isotherm for hydrophobic interaction chromatography (HIC) under consideration of water molecules as participants was proposed, enabling an accurate description of HIC. In this work, based on similarities between protein precipitation and HIC, a new precipitation model was derived. In the proposed model, the formation of protein–protein interfaces is thought to be driven by hydrophobic effects, involving a reorganization of the well-ordered water structure on the hydrophobic surfaces of the protein–protein complex. To demonstrate model capability, high-throughput precipitation experiments with pure or prior to the experiments purified proteins lysozyme, myoglobin, bovine serum albumin, and one mAb were conducted at various pH values. Polyethylene glycol (PEG) 6000 was used as precipitant. The precipitant concentration as well as the initial protein concentration was varied systematically. For all investigated proteins, the initial protein concentrations were varied between 1.5 mg/mL and 12 mg/mL. The calibrated models were successfully validated with experimental data. This mechanistic description of protein precipitation process offers mathematical explanation of the precipitation behavior of proteins at PEG concentration, protein concentration, protein size, and pH.

Self-Coacervation of a Silk-Like Protein and Its Use As an Adhesive for Cellulosic Materials.

Liquid–liquid phase separation of biomacromolecules plays a critical role in many of their functions, both as cellular components and in structural assembly. Phase separation is also a key mechanism in the assembly of engineered recombinant proteins for the general aim to build new materials with unique structures and properties. Here the phase separation process of an engineered protein with a block-architecture was studied. As a central block, we used a modified spider silk sequence, predicted to be unstructured. In each terminus, folded globular blocks were used. We studied the kinetics and mechanisms of phase formation and analyzed the evolving structures and their viscoelastic properties. Individual droplets were studied with a micropipette technique, showing both how properties vary between individual drops and explaining overall bulk rheological properties. A very low surface energy allowed easy deformation of droplets and led to efficient infiltration into cellulosic fiber networks. Based on these findings, we demonstrated an efficient use of the phase-separated material as an adhesive for cellulose. We also conclude that the condensed state is metastable, showing an ensemble of properties in individual droplets and that an understanding of protein phase behavior will lead to developing a wider use of proteins as structural polymers.

Protein crystallization in a droplet-based microfluidic device: Hydrodynamic analysis and study of the phase behaviour

This work reports a cheap and easy-to-use droplet-based microfluidic platform for the study of protein crystallization, offering the possibility to characterize the protein phase behaviour, and the effect of volumetric and interfacial phenomena on the crystallization mechanism. We conducted a parametric study supported by comparison with literature data, to quantify the influence of the droplet volume on the thermodynamic (solubility data) and kinetic (metastability data) parameters, using lysozyme as a model protein. Experiments were performed in a tubular microreactor at low Capillary numbers (4.1 × 10−5–2.3 × 10−4), resulting in a broad range of droplet sizes. The droplet formation in a flow-focusing geometry was also numerically studied using CFD and a correlation for the droplet size was developed. Subsequently, the lysozyme phase behaviour and the possible mechanisms associated with the nucleation process were evaluated. While crystallization in small volume droplets is usually characterized by a low nucleation probability and correspondingly low number of crystals, we did not observe this in our experiments. A potential explanation for this is the complex and stochastic mechanism of nucleation, including the competition between monomers and oligomers in solution.

A User’s Guide for Phase Separation Assays with Purified Proteins

The formation of membrane-less organelles and compartments by protein phase separation is an important way in which cells organize their cytoplasm and nucleoplasm. In vitro phase separation assays with purified proteins have become the standard way to investigate proteins that form membrane-less compartments. By now, various proteins have been purified and tested for their ability to phase separate and form liquid condensates in vitro. However, phase-separating proteins are often aggregation-prone and difficult to purify and handle. As a consequence, the results from phase separation assays often differ between labs and are not easily reproduced. Thus, there is an urgent need for high-quality proteins, standardized procedures, and generally agreed-upon practices for protein purification and conducting phase separation assays. This paper provides protocols for protein purification and guides the user through the practicalities of in vitro protein phase separation assays, including best-practice approaches and pitfalls to avoid. We believe that this compendium of protocols and practices will provide a useful resource for scientists studying the phase behavior of proteins.

Application of Empirical Phase Diagrams for Multidimensional Data Visualization of High-Throughput Microbatch Crystallization Experiments

Protein phase diagrams are a tool to investigate the cause and consequence of solution conditions on protein phase behavior. The effects are scored according to aggregation morphologies such as crystals or amorphous precipitates. Solution conditions affect morphologic features, such as crystal size, as well as kinetic features, such as crystal growth time. Commonly used data visualization techniques include individual line graphs or phase diagrams based on symbols. These techniques have limitations in terms of handling large data sets, comprehensiveness or completeness. To eliminate these limitations, morphologic and kinetic features obtained from crystallization images generated with high throughput microbatch experiments have been visualized with radar charts in combination with the empirical phase diagram method. Morphologic features (crystal size, shape, and number, as well as precipitate size) and kinetic features (crystal and precipitate onset and growth time) are extracted for 768 solutions with varying chicken egg white lysozyme concentration, salt type, ionic strength, and pH. Image-based aggregation morphology and kinetic features were compiled into a single and easily interpretable figure, thereby showing that the empirical phase diagram method can support high-throughput crystallization experiments in its data amount as well as its data complexity.