Nadia Aicha Erkamp

Antibodies have transformed the therapeutic landscape since the turn of the century, representing a potent class of biologic drugs capable of treating or mitigating a range of maladies from infecti...

Characterizing the mechanical properties of single colloids is a central problem in soft matter physics. It also plays a key role in cell biology through biopolymer condensates, which function as membraneless compartments. Such systems can also malfunction, leading to the onset of a number of diseases, including many neurodegenerative diseases; the functional and pathological condensates are commonly differentiated by their mechanical signature. Comprehensive understanding of mechanical properties of biopolymer condensates has, however, remained challenging. In this study, we demonstrate that acoustic trapping can be used to probe the bulk modulus of biopolymer condensates in a contactless manner. We find that acoustic fields exert an acoustic radiation force on condensates, leading to their migration to a trapping point where acoustic potential energy is minimized. Furthermore, our results show that the positional fluctuation of a condensate undergoing Brownian motion in an acoustic potential well can be used as a probe for its bulk modulus. Using this framework, we infer changes in the bulk modulus of polyadenylic acid condensates in response to variations in ionic strength. Our results show that acoustic trapping opens up a path to characterize the bulk modulus of soft colloids in a noninvasive manner with applications in biology, materials science, and beyond.

Enzymatic cascade systems enhance the efficiency of biocatalysis by mimicking natural metabolic pathways. However, conventional platforms face challenges such as ineffective enzyme distribution, uncontrolled stoichiometry, limited substrate accessibility, and a microenvironment that is suboptimal for enzyme performance. Inspired by cellular biomolecular condensates, we here present a design based on oppositely charged single enzyme nanogels (SENs) that phase separate into denser microdroplet coacervates, which thereby serve both as an immobilization scaffold and functional unit. These coacervates, the first fabricated exclusively from oppositely charged nanogels, form under broad environmental conditions and enable specific microenvironments by adjusting SEN ratios to match the requirements of coupled enzymes. Selective substrate enrichment and efficient diffusion of intermediates enhance cascade productivity. The applicability of the platform is demonstrated with pharmaceutically relevant enzymes, highlighting its potential for biosynthetic applications. This work establishes a new paradigm that integrates protein‐polymer engineering with phase separation chemistry to overcome key limitations in cascade enzyme catalysis.

The control of the assembly, size, and surface–volume ratio of biomolecular condensates is crucial to their function in living cells. These features are determined by nucleation from a supersaturated solution, but quantitative measurements of nucleation rates and their comparison across biomolecular systems have been challenging to obtain. Using microfluidics, we measure the nucleation rates of dense liquid phases within dilute solutions and the rates of the reverse process, in which dilute voids form inside condensates. We find that both processes are controlled by a single physical parameter, the interfacial tension between the dense and dilute phases, demonstrating that nucleation in both directions proceeds through essentially a single‐step mechanism. Furthermore, analysis of interfacial tension data establishes rapid nucleation as a general feature of condensates, even if the free‐energy gain per molecule is smaller than the thermal energy. This behavior sets condensates apart from solid crystals or aggregates and highlights the unique physical principles underlying their distinct biological functions.

Understanding the effects of formulation excipients on protein solubility is a key part of physical chemistry and pharmaceutical sciences. While excipients are routinely employed to reduce the self-association of biologic drugs, their mechanisms of action remain poorly understood and are often assumed to be broadly nonspecific. Using a high-throughput combinatorial droplet microfluidic platform, we systematically survey and quantify how common pharmaceutical excipients affect the solubility of a diverse panel of therapeutic monoclonal antibodies (mAbs). We show that, while excipients are generally solubilizing, their effects vary substantially across different mAbs, with excipient-specific solubilization scores spanning dynamic ranges of approximately 7-fold to >200-fold across the antibody panel. Histidine, arginine and sodium chloride, in particular, engage in interactions characterized by unique molecular specificity, whereas sucrose effects are largely governed by nonspecific, solvent-mediated interactions. Correlating excipient performance with dynamical mAb molecular features from solvated full-length homology models allows us to dissect and quantify the relative contributions of molecular features governing excipient-mediated solubilization. We envision this new physicochemical understanding lays the groundwork for rational excipient selection and bespoke formulation design, with direct implications for accelerating protein therapeutic development for preclinical scenarios.

Self-driving laboratories accelerate materials discovery by autonomously designing and executing experiments through closed-loop integration of robotics and artificial intelligence. Active learning with Gaussian processes has enabled efficient phase diagram mapping, reducing required measurements by approximately 80\% compared to conventional grid sampling. However, current approaches treat each system independently, discarding accumulated knowledge despite systematic similarities across related materials families. Here we introduce PhaseTransfer, a transfer learning framework that leverages previously characterized phase diagrams to accelerate mapping of new systems. PhaseTransfer combines a target model trained on current data with source models from related diagrams using spatially varying reliability assessments and focusing sampling on regions where transferred knowledge proves unreliable. Validation across synthetic benchmarks and experimental implementation on an autonomous microfluidic platform for biological condensate screening demonstrates in the order of 50\% reduction in sampling requirements compared to conventional active learning. By enabling knowledge reuse across investigations, transfer learning substantially enhances both the efficiency and generality of autonomous materials discovery.

Nature utilizes phase separation to create biomolecular condensates which spatially organize cellular components. In artificial cells, however, mimicking the structure of cellular multiphase systems is challenging. While transient or dynamically induced structures can be created, their lifetime is often very limited. Here, a strategy is presented for stabilizing these structures via photo‐crosslinking. Specifically, methacrylate groups are incorporated in one of the scaffold polymers of this coacervate‐based artificial cell. UV light is applied directly after the formation of the transient structure, allowing for dynamic formation but at the same time extending the structure's lifetime by at least 5–10 times. Interestingly, this strategy is effective on single and multiphase condensates. Additionally, spatio‐temporal control over irradiation can create samples in which condensates and parts of condensates have different structures, achieving programmable asymmetry which is rarely observed in artificial systems. It is expected that creating long‐lived intricately structured condensates will contribute to performing increasingly complex functions and develop more advanced artificial cells.

Phase separation fundamentally governs material properties and cellular function on multiple organizational scales. Conventional approaches to studying this nonlinear phenomenon necessitate resource-intensive experiments. As such, investigations were limited to low-dimensional space. We present PhaseXplorer, a platform that combines microfluidics, microscopy, and machine learning to efficiently study phase separation systems. PhaseXplorer autonomously designs, generates, and analyzes samples in a closed-loop active learning workflow until an accurate phase diagram is obtained. Using an acquisition function that balances exploration and exploitation, all the phase boundaries are located with minimal sampling. A convolutional neural network executes real-time image recognition to identify microfluidic droplets and phase separation within them in less than 1 ms per droplet. PhaseXplorer standardizes analysis across experiments and does not require calibration nor extensive postexperiment analysis. We demonstrate PhaseXplorer's capabilities using a poly rA model system by creating a four-dimensional phase diagram 100 times faster than traditional methods while simultaneously consuming 10,000 times less material.

Accurate predictions of protein self-organization and phase stability are important in disease prevention, drug formulation, as well as material and food science. Crucial for accurately capturing this behaviour in a model are depletion and electrostatic interactions. Here, we present an analytical mean-field model which uses these two interactions to quantitatively predict the phase stability of protein-polymer mixtures. We find our predictions have high accuracy by comparing with 18 separate n>10000 experimentally determined phase diagrams.