Targeting nucleic acid phase transitions as a mechanism of action for antimicrobial peptides.

Antimicrobial peptides (AMPs), which combat bacterial infections by disrupting the bacterial cell membrane or interacting with intracellular targets, are naturally produced by a number of different organisms, and are increasingly also explored as therapeutics. However, the mechanisms by which AMPs act on intracellular targets are not well understood. Using machine learning-based sequence analysis, we identified a significant number of AMPs that have a strong tendency to form liquid-like condensates in the presence of nucleic acids through phase separation. We demonstrate that this phase separation propensity is linked to the effectiveness of the AMPs in inhibiting transcription and translation in vitro, as well as their ability to compact nucleic acids and form clusters with bacterial nucleic acids in bacterial cells. These results suggest that the AMP-driven compaction of nucleic acids and modulation of their phase transitions constitute a previously unrecognised mechanism by which AMPs exert their antibacterial effects. The development of antimicrobials that target nucleic acid phase transitions may become an attractive route to finding effective and long-lasting antibiotics.

Theoretical and Data-Driven Approaches for Biomolecular Condensates.

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.

Turning high-throughput structural biology into predictive inhibitor design.

A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein-ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.

Direct digital sensing of protein biomarkers in solution.

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.

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.

Microchip Free-Flow Electrophoresis for Bioanalysis, Sensing, and Purification.

The separation of complex mixtures is ubiquitous throughout molecular biology, and techniques such as gel-based electrophoresis are common laboratory practice. Such methods are not without their drawbacks, however, which include non-specific interactions between analyte and the separation matrix, poor yields in purification and non-continuous analyte throughput. Microfluidic techniques, which exploit physical phenomena unique to the microscale, promise to improve many aspects of traditional laboratory procedures. These methods offer a quantitative, solution-based alternative to traditional gel electrophoresis, with rapid measurement times enabling the analysis of transient or weak biomolecular interactions that would be challenging to observe with traditional methods. Here, we present a protocol for the lithographic fabrication and operation of microfluidic chips capable of free-flow electrophoretic (FFE) fractionation and analysis of biological analytes. We demonstrate the efficacy of our approach through a protein-sensing methodology based on FFE fractionation of DNA-protein mixtures. In addition, the FFE technique described here can be readily adapted to suit a variety of preparative and analytical applications, providing information on the charge, zeta-potential, and interactions of analytes.

Surface Electrostatics Govern the Emulsion Stability of Biomolecular Condensates.

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.

New Frontiers for Machine Learning in Protein Science.

Protein function is fundamentally reliant on inter-molecular interactions that underpin the ability of proteins to form complexes driving biological processes in living cells. Increasingly, such interactions are recognised as being formed between proteins that exist on a broad spectrum of dynamic conformational states and levels of intrinsic disorder. Additionally, the sizes of the structures formed can range from simple binary complexes to large dynamic biomolecular condensates measuring 100 nm or more. Understanding the parameters that govern such interactions, how they form, how they lead to function and what happens when they take place in unintended manners and lead to disease, represent some of the core questions for molecular biosciences. In light of recent advances made in solving the protein folding problem by machine learning methods, we discuss here the challenges and opportunities brought by these new data-driven approaches for the next frontiers of biomolecular science.

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.

Machine learning-aided protein identification from multidimensional signatures.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.

Rapid highly sensitive general protein quantification through on-chip chemiluminescence.

Protein detection and quantification is a routinely performed procedure in research laboratories, predominantly executed either by spectroscopy-based measurements, such as NanoDrop, or by colorimetric assays. The detection limits of such assays, however, are limited to µ M concentrations. To establish an approach that achieves general protein detection at an enhanced sensitivity and without necessitating the requirement for signal amplification steps or a multicomponent detection system, here, we established a chemiluminescence-based protein detection assay. Our assay specifically targeted primary amines in proteins, which permitted characterization of any protein sample and, moreover, its latent nature eliminated the requirement for washing steps providing a simple route to implementation. Additionally, the use of a chemiluminescence-based readout ensured that the assay could be operated in an excitation source-free manner, which did not only permit an enhanced sensitivity due to a reduced background signal but also allowed for the use of a very simple optical setup comprising only an objective and a detection element. Using this assay, we demonstrated quantitative protein detection over a concentration range of five orders of magnitude and down to a high sensitivity of 10 pg mL - 1 , corresponding to pM concentrations. The capability of the platform presented here to achieve a high detection sensitivity without the requirement for a multistep operation or a multicomponent optical system sets the basis for a simple yet universal and sensitive protein detection strategy.

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/.

Rapid Structural, Kinetic, and Immunochemical Analysis of Alpha-Synuclein Oligomers in Solution.

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 α-synuclein 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.

Multidimensional protein characterisation using microfluidic post-column analysis.

The biological function of proteins is dictated by the formation of supra-molecular complexes that act as the basic machinery of the cell. As such, measuring the properties of protein species in heterogeneous mixtures is of key importance for understanding the molecular basis of biological function. Here, we describe the combination of analytical microfluidic tools with liquid chromatography for multidimensional characterisation of biomolecules in complex mixtures in the solution phase. Following chromatographic separation, a small fraction of the flow-through is distributed to multiple microfluidic devices for analysis. The microfluidic device developed here allows the simultaneous determination of the hydrodynamic radius, electrophoretic mobility, effective molecular charge and isoelectric point of isolated protein species. We demonstrate the operation principle of this approach with a mixture of three unlabelled model proteins varying in size and charge. We further extend the analytical potential of the presented approach by analysing a mixture of interacting streptavidin with biotinylated BSA and fluorophores, which form a mixture of stable complexes with diverse biophysical properties and stoichiometries. The presented microfluidic device positioned in-line with liquid chromatography presents an advanced tool for characterising multidimensional physical properties of proteins in biological samples to further understand the assembly/disassembly mechanism of proteins and the nature of complex mixtures.

Rapid two-dimensional characterisation of proteins in solution.

Microfluidic platforms provide an excellent basis for working with heterogeneous samples and separating biomolecular components at high throughput, with high recovery rates and by using only very small sample volumes. To date, several micron scale platforms with preparative capabilities have been demonstrated. Here we describe and demonstrate a microfluidic device that brings preparative and analytical operations together onto a single chip and thereby allows the acquisition of multidimensional information. We achieve this objective by using a free-flow electrophoretic separation approach that directs fractions of sample into an on-chip analysis unit, where the fractions are characterised through a microfluidic diffusional sizing process. This combined approach therefore allows simultaneously quantifying the sizes and the charges of components in heterogenous mixtures. We illustrate the power of the platform by describing the size distribution of a mixture comprising components which are close in size and cannot be identified as individual components using state-of-the-art solution sizing techniques on their own. Furthermore, we show that the platform can be used for two-dimensional fingerprinting of heterogeneous protein mixtures within tens of seconds, opening up a possibility to obtain multiparameter data on biomolecular systems on a minute timescale.

Analysis of αB-crystallin polydispersity in solution through native microfluidic electrophoresis.

In recent years, significant advancements have been made in the understanding of the population distributions and dynamic oligomeric states of the molecular chaperone αB-crystallin and its core domain variants. In this work, we provide solution-phase evidence of the polydispersity of αB-crystallin using microfluidic methods, used for separating the oligomeric species present in solution according to their different electrophoretic mobilities on-chip in a matter of seconds. We in particular demonstrate that microfluidic high-field electrophoresis and diffusion can detect the oligomerisation of these highly dynamic molecular chaperones and characterise the dominant oligomeric species present. We thereby provide a robust microfluidic method for characterising the individual species within complex protein mixtures of biological relevance.

Quaternization of Vinyl/Alkynyl Pyridine Enables Ultrafast Cysteine-Selective Protein Modification and Charge Modulation.

Quaternized vinyl- and alkynyl-pyridine reagents were shown to react in an ultrafast and selective manner with several cysteine-tagged proteins at near-stoichiometric quantities. We have demonstrated that this method can effectively create a homogenous antibody-drug conjugate that features a precise drug-to-antibody ratio of 2, which was stable in human plasma and retained its specificity towards Her2+ cells. Finally, the developed warhead introduces a +1 charge to the overall net charge of the protein, which enabled us to show that the electrophoretic mobility of the protein may be tuned through the simple attachment of a quaternized vinyl pyridinium reagent at the cysteine residues. We anticipate the generalized use of quaternized vinyl- and alkynyl-pyridine reagents not only for bioconjugation, but also as warheads for covalent inhibition and as tools to profile cysteine reactivity.

Combining Affinity Selection and Specific Ion Mobility for Microchip Protein Sensing.

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.

Enhancing the Resolution of Micro Free Flow Electrophoresis through Spatially Controlled Sample Injection.

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.

On-chip measurements of protein unfolding from direct observations of micron-scale diffusion.

Investigations of protein folding, unfolding and stability are critical for the understanding of the molecular basis of biological structure and function. We describe here a microfluidic approach to probe the unfolding of unlabelled protein molecules in microliter volumes. We achieve this objective through the use of a microfluidic platform, which allows the changes in molecular diffusivity upon folding and unfolding to be detected directly. We illustrate this approach by monitoring the unfolding of bovine serum albumin in solution as a function of pH. These results show the viability of probing protein stability on chip in small volumes.