Electrochemical gradients are essential to the functioning of cells and form across membranes using active transporters. Here we show in contrast that condensed biomolecular systems-often termed condensates-sustain pH gradients without any external energy input. By studying individual condensates on the micrometre scale using a microdroplet platform, we reveal dense-phase pH shifts towards conditions of minimal electrostatic repulsion. We demonstrate that protein condensates can drive substantial alkaline and acidic gradients, which are compositionally tunable and can extend to complex architectures sustaining multiple unique pH conditions simultaneously. Through in silico characterization of human proteomic condensate networks, we further highlight potential wide-ranging electrochemical properties emerging from condensation in nature, while correlating intracellular condensate pH gradients with complex biomolecular composition. Together, the emergent nature of condensation shapes distinct pH microenvironments, thereby creating a regulatory mechanism to modulate biochemical activity in living and artificial systems.

Disease-modifying therapeutics in the α-synucleinopathies multiple system atrophy (MSA) and Parkinson’s Disease (PD) are in early phases of clinical testing. Involving patients’ preferences including therapy-associated risk willingness in initial stages of therapy development has been increasingly pursued in regulatory approval processes. In our study with 49 MSA and 38 PD patients, therapy-associated risk willingness was quantified using validated standard gamble scenarios for varying severities of potential drug or surgical side effects. Demonstrating a non-gaussian distribution, risk willingness varied markedly within, and between groups. MSA patients accepted a median 1% risk [interquartile range: 0.001–25%] of sudden death for a 99% [interquartile range: 99.999–75%] chance of cure, while PD patients reported a median 0.055% risk [interquartile range: 0.001–5%]. Contrary to our hypothesis, a considerable proportion of MSA patients, despite their substantially impaired quality of life, were not willing to accept increased therapy-associated risks. Satisfaction with life situation, emotional, and nonmotor disease burden were associated with MSA patients’ risk willingness in contrast to PD patients, for whom age, and disease duration were associated factors. An individual approach towards MSA and PD patients is crucial as direct inference from disease (stage) to therapy-associated risk willingness is not feasible. Such studies may be considered by regulatory agencies in their approval processes assisting with the weighting of safety aspects in a patient-centric manner. A systematic quantitative assessment of patients’ risk willingness and associated features may assist physicians in conducting individual consultations with patients who have MSA or PD by facilitating communication of risks and benefits of a treatment option.

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm’s capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

Electrochemical gradients are essential to the functioning of cells and are typically formed across membranes using active transporters and require energy input to maintain them. Here, we show by contrast that biomolecular condensates are able to sustain significant pH gradients without any external energy input. We explore the thermodynamic driving forces that establish this gradient using a microfluidics-based droplet platform that allows us to sample in a continuous manner both the stability and composition of the condensates across a wide pH range. These results reveal that condensed biomolecular systems adjust the pH of the dense phase towards the isoelectric point (pI) of the component polypeptide chains. We demonstrate, on the basis of two representative systems, FUS and PGL3, that condensates can create both alkaline and acidic gradients with a magnitude exceeding one pH unit. Investigations of multicomponent protein/nucleic acid systems further show that heterotypic interactions can modulate condensate pH gradients. We further investigate using a bioinformatics approach the diversity of electrochemical properties of complex condensates by studying a large set of human condensate networks, showing that these span a wide range of mixture pIs and pH-response behaviours. In summary, our results reveal that protein condensation may present a fundamental physico-chemical mechanism for the effective segregation and optimisation of functional processes through changes in the emergent electrochemical microenvironment.### Competing Interest StatementT.P.J.K. is the C.T.O. of Transition Bio Inc., G.I. and N.L.Z. are employees of Novo Nordisk and S.A and A.A.H. are members of the scientific advisory board of Dewpoint Therapeutics Inc. The work reported here was not influenced by these affiliations.

Solubility is a property of central importance for the use of proteins in research in molecular and cell biology and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are material intensive and time consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of solubility for large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the development of one such method to include in the predictions the effect of the pH on solubility. We illustrate the resulting pH-dependent predictions on a variety of antibodies and other proteins to demonstrate that these predictions achieve an accuracy comparable with that of experimental methods. We make this method publicly available at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolph, as the version 3.0 of CamSol.

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.

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Parkinson's disease is associated with the aberrant aggregation of α-synuclein. Although the causes of this process are still unclear, post-translational modifications of α-synuclein are likely to play a modulatory role. Since α-synuclein is constitutively N-terminally acetylated, we investigated how this post-translational modification alters the aggregation behavior of this protein. By applying a three-pronged aggregation kinetics approach, we observed that N-terminal acetylation results in a reduced rate of lipid-induced aggregation and slows down both elongation and fibril-catalyzed aggregate proliferation. An analysis of the amyloid fibrils produced by the aggregation process revealed different morphologies for the acetylated and non-acetylated forms in both lipid-induced aggregation and seed-induced aggregation assays. In addition, we found that fibrils formed by acetylated α-synuclein exhibit a lower β-sheet content. These findings indicate that N-terminal acetylation of α-synuclein alters its lipid-dependent aggregation behavior, reduces its rate of in vitro aggregation, and affects the structural properties of its fibrillar aggregates.

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 very high concentrations, making insufficient solubility a critical bottleneck in drug development pipelines. Here, we report an ultra-high-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 micrograms of protein (> 10-fold improvement) and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation additives, such as salt, polysorbate, histidine, arginine and sucrose. Additionally, we can measure how solubility is affected by different concentrations of multiple additives, find a suitable pH for the formulation, and measure the impact of single mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multi-dimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.

Abstract. The goal of understanding the architecture of neural circuits at the synapse level with a brain-wide perspective has powered the interest in high-speed and large field-of-view volumetric imaging at subcellular resolution. Here, we developed a method combining tissue expansion and light-sheet fluorescence microscopy to allow extended volumetric super resolution high-speed imaging of large mouse brain samples. We demonstrate the capabilities of this method by performing two color fast volumetric super resolution imaging of mouse CA1 and dentate gyrus molecular-, granule cell-, and polymorphic layers. Our method enables an exact evaluation of granule cell and neurite morphology within the context of large cell ensembles spanning several orders of magnitude in resolution. We found that imaging a brain region of 1  mm3 in super resolution using light-sheet fluorescence expansion microscopy is about 17-fold faster than imaging the same region by a current state-of-the-art high-resolution confocal laser scanning microscope.