Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling.

Small-angle X-ray scattering (SAXS) is a characterization technique that allows for the study of colloidal interactions by fitting the structure factor of the SAXS profile with a selected model and closure relation. However, the applicability of this approach is constrained by the limited number of existing models that can be fitted analytically, as well as the narrow operating range for which the models are valid. In this work, we demonstrate a proof of concept for using an artificial neural network (ANN) trained on SAXS curves obtained from Monte Carlo (MC) simulations to predict values of the effective macroion valency (Z eff) and the Debye length (κ-1) for a given SAXS profile. This ANN, which was trained on 200,000 simulated SAXS curves, was able to predict values of Z eff and κ-1 for a test set containing 25,000 simulated SAXS curves, where most predicted values had errors smaller than 20%. Subsequently, an ANN was used as a surrogate model in a Markov chain Monte Carlo sampling algorithm to obtain maximum a posteriori estimates of Z eff and κ-1, as well as the associated confidence intervals and correlations between Z eff and κ-1 for an experimentally obtained SAXS profile.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform.

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 µg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

The Chromatin Regulator HMGA1a Undergoes Phase Separation in the Nucleus.

The protein high mobility group A1 (HMGA1) is an important regulator of chromatin organization and function. However, the mechanisms by which it exerts its biological function are not fully understood. Here, we report that the HMGA isoform, HMGA1a, nucleates into foci that display liquid-like properties in the nucleus, and that the protein readily undergoes phase separation to form liquid condensates in vitro. By bringing together machine-leaning modelling, cellular and biophysical experiments and multiscale simulations, we demonstrate that phase separation of HMGA1a is promoted by protein-DNA interactions, and has the potential to be modulated by post-transcriptional effects such as phosphorylation. We further show that the intrinsically disordered C-terminal tail of HMGA1a significantly contributes to its phase separation through electrostatic interactions via AT hooks 2 and 3. Our work sheds light on HMGA1 phase separation as an emergent biophysical factor in regulating chromatin structure.

Biomolecular condensate phase diagrams with a combinatorial microdroplet platform.

The assembly of biomolecules into condensates is a fundamental process underlying the organisation of the intracellular space and the regulation of many cellular functions. Mapping and characterising phase behaviour of biomolecules is essential to understand the mechanisms of condensate assembly, and to develop therapeutic strategies targeting biomolecular condensate systems. A central concept for characterising phase-separating systems is the phase diagram. Phase diagrams are typically built from numerous individual measurements sampling different parts of the parameter space. However, even when performed in microwell plate format, this process is slow, low throughput and requires significant sample consumption. To address this challenge, we present here a combinatorial droplet microfluidic platform, termed PhaseScan, for rapid and high-resolution acquisition of multidimensional biomolecular phase diagrams. Using this platform, we characterise the phase behaviour of a wide range of systems under a variety of conditions and demonstrate that this approach allows the quantitative characterisation of the effect of small molecules on biomolecular phase transitions.

Microfluidics for multiscale studies of biomolecular condensates.

Membraneless organelles formed through condensation of biomolecules in living cells have become the focus of sustained efforts to elucidate their mechanisms of formation and function. These condensates perform a range of vital functions in cells and are closely connected to key processes in functional and aberrant biology. Since these systems occupy a size scale intermediate between single proteins and conventional protein complexes on the one hand, and cellular length scales on the other hand, they have proved challenging to probe using conventional approaches from either protein science or cell biology. Additionally, condensate can form, solidify and perform functions on various time-scales. From a physical point of view, biomolecular condensates are colloidal soft matter systems, and microfluidic approaches, which originated in soft condensed matter research, have successfully been used to study biomolecular condensates. This review explores how microfluidics have aided condensate research into the thermodynamics, kinetics and other properties of condensates, by offering high-throughput and novel experimental setups.

Recent Advances in Microgels: From Biomolecules to Functionality.

The emerging applications of hydrogel materials at different length scales, in areas ranging from sustainability to health, have driven the progress in the design and manufacturing of microgels. Microgels can provide miniaturized, monodisperse, and regulatable compartments, which can be spatially separated or interconnected. These microscopic materials provide novel opportunities for generating biomimetic cell culture environments and are thus key to the advances of modern biomedical research. The evolution of the physical and chemical properties has, furthermore, highlighted the potentials of microgels in the context of materials science and bioengineering. This review describes the recent research progress in the fabrication, characterization, and applications of microgels generated from biomolecular building blocks. A key enabling technology allowing the tailoring of the properties of microgels is their synthesis through microfluidic technologies, and this paper highlights recent advances in these areas and their impact on expanding the physicochemical parameter space accessible using microgels. This review finally discusses the emerging roles that microgels play in liquid-liquid phase separation, micromechanics, biosensors, and regenerative medicine.

Liquid-liquid phase separation underpins the formation of replication factories in rotaviruses.

RNA viruses induce the formation of subcellular organelles that provide microenvironments conducive to their replication. Here we show that replication factories of rotaviruses represent protein-RNA condensates that are formed via liquid-liquid phase separation of the viroplasm-forming proteins NSP5 and rotavirus RNA chaperone NSP2. Upon mixing, these proteins readily form condensates at physiologically relevant low micromolar concentrations achieved in the cytoplasm of virus-infected cells. Early infection stage condensates could be reversibly dissolved by 1,6-hexanediol, as well as propylene glycol that released rotavirus transcripts from these condensates. During the early stages of infection, propylene glycol treatments reduced viral replication and phosphorylation of the condensate-forming protein NSP5. During late infection, these condensates exhibited altered material properties and became resistant to propylene glycol, coinciding with hyperphosphorylation of NSP5. Some aspects of the assembly of cytoplasmic rotavirus replication factories mirror the formation of other ribonucleoprotein granules. Such viral RNA-rich condensates that support replication of multi-segmented genomes represent an attractive target for developing novel therapeutic approaches.

Liquid-Liquid Phase-Separated Systems from Reversible Gel-Sol Transition of Protein Microgels.

Liquid-liquid phase-separated biomolecular systems are increasingly recognized as key components in the intracellular milieu where they provide spatial organization to the cytoplasm and the nucleoplasm. The widespread use of phase-separated systems by nature has given rise to the inspiration of engineering such functional systems in the laboratory. In particular, reversible gelation of liquid-liquid phase-separated systems could confer functional advantages to the generation of new soft materials. Such gelation processes of biomolecular condensates have been extensively studied due to their links with disease. However, the inverse process, the gel-sol transition, has been less explored. Here, a thermoresponsive gel-sol transition of an extracellular protein in microgel form is explored, resulting in an all-aqueous liquid-liquid phase-separated system with high homogeneity. During this gel-sol transition, elongated gelatin microgels are demonstrated to be converted to a spherical geometry due to interfacial tension becoming the dominant energetic contribution as elasticity diminishes. The phase-separated system is further explored with respect to the diffusion of small particles for drug-release scenarios. Together, this all-aqueous system opens up a route toward size-tunable and monodisperse synthetic biomolecular condensates and controlled liquid-liquid interfaces, offering possibilities for applications in bioengineering and biomedicine.

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/.

Comparative characterisation of non-monodisperse gold nanoparticle populations by X-ray scattering and electron microscopy.

Accurate nanoparticle size determination is essential across various research domains, with many functionalities in nanoscience and biomedical research being size-dependent. Although electron microscopy is capable of resolving a single particle down to the sub-nm scale, the reliable representation of entire populations is plagued by challenges in providing statistical significance, suboptimal preparation procedures and operator bias. While alternative techniques exist that provide ensemble information in solution, their implementation is generally challenging for non-monodisperse populations. Herein, we explore the use of small-angle X-ray scattering in combination with form-free Monte Carlo fitting of scattering profiles as an alternative to conventional electron microscopy imaging in providing access to any type of core size distribution. We report on a cross-method comparison for quasi-monodisperse, polydisperse and bimodal gold nanoparticles of 2-7 nm in diameter and discuss advantages and limitations of both techniques.

Application of the Spatial Distribution Function to Colloidal Ordering.

2D colloidal assembly is a vital process in the fabrication of nanostructured devices and receives widespread attention in fundamental research. Characterizing the ordering is crucial to develop an understanding of the driving forces behind the assembly and to optimize processing conditions. Image analysis offers a direct evaluation pathway, typically via the radial distribution function or the 2D fast Fourier transform. Both methods have inherent limitations; the former provides no angular dependence, while the latter is challenged when confronted with imperfection on the mean size, spacing, and coverage of the building blocks. Here, we introduce the 2D spatial distribution function (SDF) as an alternative pathway to evaluate colloidal ordering. We benchmark the method in case studies of prominent examples and provide a tool kit for implementation, including an ImageJ plugin and the standalone software CORDERLY. Application and interpretation are straightforward and particularly powerful to analyze and compare colloidal assemblies with limited order.

Probing the interaction of nanoparticles with small molecules in real time via quartz crystal microbalance monitoring.

Despite extensive advances in the field of molecular recognition, the real-time monitoring of small molecule binding to nanoparticles (NP) remains a challenge. To this end, we report on a versatile approach, based on quartz crystal microbalance with dissipation monitoring, for the stepwise in situ quantification of gold nanoparticle (AuNPs) immobilisation and subsequent uptake and release of binding partners. AuNPs stabilised by thiol-bound ligand shells of prescribed chemical composition were densely immobilised onto gold surfaces via dithiol linkers. The boronate ester formation between salicylic acid derivatives in solution and boronic acids in the AuNP ligand shell was then studied in real time, revealing a drastic effect of both ligand architecture and Lewis base concentration on the interaction strength. The binding kinetics were analysed with frequency response modelling for a thorough comparison of binding parameters including relaxation time as well as association rate constant. The results directly mirror those from previously reported in-depth studies using nuclear magnetic resonance spectroscopy. By achieving quantitative characterisation of selective binding of analytes with molecular weight below 300 Da, this new method enables rapid, low cost, rational screening of AuNP candidates for molecular recognition.