Liquid–liquid phase separation underlies the formation of biological condensates. Physically, such systems are microemulsions that in general have a propensity to fuse and coalesce; however, many condensates persist as independent droplets in the test tube and inside cells. This stability is crucial for their function, but the physicochemical mechanisms that control the emulsion stability of condensates remain poorly understood. Here, by combining single-condensate zeta potential measurements, optical microscopy, tweezer experiments, and multiscale molecular modeling, we investigate how the nanoscale forces that sustain condensates impact their stability against fusion. By comparing peptide–RNA (PR25:PolyU) and proteinaceous (FUS) condensates, we show that a higher condensate surface charge correlates with a lower fusion propensity. Moreover, measurements of single condensate zeta potentials reveal that such systems can constitute classically stable emulsions. Taken together, these results highlight the role of passive stabilization mechanisms in protecting biomolecular condensates against coalescence.

Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

Oligomers comprised of misfolded proteins are implicated as neurotoxins in the pathogenesis of protein misfolding conditions such as Parkinson's and Alzheimer's diseases. Structural, biophysical, and biochemical characterization of these nanoscale protein assemblies is key to understanding their pathology and the design of therapeutic interventions, yet it is challenging due to their heterogeneous, transient nature and low relative abundance in complex mixtures. Here, we demonstrate separation of heterogeneous populations of oligomeric α-synuclein, a protein central to the pathology of Parkinson's disease, in solution using microfluidic free-flow electrophoresis. We characterize nanoscale structural heterogeneity of transient oligomers on a time scale of seconds, at least 2 orders of magnitude faster than conventional techniques. Furthermore, we utilize our platform to analyze oligomer ζ-potential and probe the immunochemistry of wild-type αsynuclein oligomers. Our findings contribute to an improved characterization of α-synuclein oligomers and demonstrate the application of microchip electrophoresis for the free-solution analysis of biological nanoparticle analytes.

The sensitive detection of proteins is a key objective in many areas of biomolecular science, ranging from biophysics to diagnostics. However, sensing in complex biological fluids is hindered by nonspecific interactions with off-target species. Here, we describe and demonstrate an assay that utilizes both the chemical and physical properties of the target species to achieve high selectivity in a manner not possible by chemical complementarity alone, in complex media. We achieve this objective through a combinatorial strategy, by simultaneously exploiting free-flow electrophoresis for target selection, on the basis of electrophoretic mobility, and conventional affinity-based selection. In addition, we demonstrate amplification of the resultant signal by a catalytic DNA nanocircuit. This approach brings together the inherent solution-phase advantages of microfluidic sample handling with isothermal, enzyme-free signal amplification. With this method, no surface immobilization or washing steps are required, and our assay is well suited to monoepitopic targets, presenting advantages over conventional ELISA techniques.

Free flow electrophoresis is a versatile technique for the continuous separation of mixtures with both preparative and analytical applications. Microscale versions of free flow electrophoresis are particularly attractive strategies because of their fast separation times, ability to work with small sample volumes, and large surface area to volume ratios facilitating rapid heat transfer, thus minimizing the detrimental effects of Joule heating even at high voltages. The resolution of microscale free flow electrophoresis, however, is limited by the broadening of the analyte beam in the microfluidic channel, an effect that becomes especially pronounced when the analyte is deflected significantly away from its original position. Here, we describe and demonstrate how restricting spatially the sample injection and collection to the regions where the gradients in the velocity distribution of the carrier medium are the smallest allows this broadening effect to be substantially suppressed and hence the resolution of microscale free flow electrophoresis devices to be increased. To demonstrate this concept, we fabricated microfluidic free flow electrophoresis devices with spatially restricted injection nozzles implemented through the use of multilayer soft-photolithography and further integrated quartz based observation areas for fluorescent detection and imaging. With these devices, we demonstrated a 5-fold reduction in the extent of beam broadening relative to conventional free flow electrophoresis approaches with nonrestricted sample introduction. The manifold enhancement in the achievable resolution of microscale free flow electrophoresis devices opens up the possibility of rapid separation and analysis of complex mixtures.

The detection of proteins is of central importance to biomolecular analysis and diagnostics. Typical immunosensing assays rely on surface-capture of target molecules, but this constraint can limit specificity, sensitivity, and the ability to obtain information beyond simple concentration measurements. Here we present a surface-free, single-molecule microfluidic sensing platform for direct digital protein biomarker detection in solution, termed digital immunosensor assay (DigitISA). DigitISA is based on microchip electrophoretic separation combined with single-molecule detection and enables absolute number/concentration quantification of proteins in a single, solution-phase step. Applying DigitISA to a range of targets including amyloid aggregates, exosomes, and biomolecular condensates, we demonstrate that the assay provides information beyond stoichiometric interactions, and enables characterization of immunochemistry, binding affinity, and protein biomarker abundance. Taken together, our results suggest a experimental paradigm for the sensing of protein biomarkers, which enables analyses of targets that are challenging to address using conventional immunosensing approaches.

Direct combination of liquid chromatography with multidimensional post-column analysis through microfluidics for individual quantification of biophysical parameters.

Intracellular phase separation of proteins into biomolecular condensates is increasingly recognised as an important phenomenon for cellular compartmentalisation and regulation of biological function. 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, here, we established an in silico strategy for understanding on a global level the associations between protein sequence and condensate formation, and used this information to construct machine learning classifiers for predicting liquid-liquid phase separation (LLPS) from protein sequence. Our analysis highlighted that LLPS-prone sequences are more disordered, hydrophobic and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database, and have their disordered regions enriched in polar, aromatic and charged residues. Using these determining features together with neural network based word2vec sequence embeddings, we developed machine learning classifiers for predicting protein condensate formation. Our model, trained to distinguish LLPS-prone sequences from structured proteins, achieved high accuracy (93%; 25-fold cross-validation) and identified condensate forming sequences from external independent test data at 97% sensitivity. Moreover, in combination with a classifier that had developed a nuanced insight into the features governing protein phase behaviour by learning to distinguish between sequences of varying LLPS propensity, the sensitivity was supplemented with high specificity (approximated ROC-AUC of 0.85). These results provide a platform rooted in molecular principles for understanding protein phase behaviour. The predictor is accessible from https://deephase.ch.cam.ac.uk/ .