Biology subjects

Saar, K. L.

Publications and source records attributed to Saar, K. L..

13 recordsLinked to original sources

bioRxiv2025

Phosphorylation enables allosteric control of a viral condensate

In many viruses, intrinsically disordered proteins (IDPs) drive the formation of replicative organelles essential for viral production. In species A rotaviruses, the disordered protein NSP5 forms condensates in cells via liquid-liquid phase separation (LLPS). Yet the sequence diversity of NSP5 raises the question of whether condensate formation is conserved across all strains and if distinct variants employ alternative mechanisms for nucleating phase separation. Using a machine learning approach, we demonstrate that NSP5 variants differ significantly in their propensity to phase-separate. We engineered a variant incorporating amino acid signatures from strains with low LLPS tendency, which failed to phase separate in vitro yet supported the formation of replicative condensates in recombinant viruses in cells. Low-tendency LLPS strains require phosphorylation of NSP5 to nucleate phase separation, whereas high-tendency strains do not, suggesting distinct nucleation mechanisms. Furthermore, hydrogen-deuterium exchange mass spectrometry revealed a phosphorylation-driven allosteric switch between binding sites on the high-propensity variant. These findings establish that phosphorylation plays a context-dependent role in the formation of replicative organelles across diverse rotaviruses.

biophysics↗

bioRxiv2024

Biomolecular condensates sustain pH gradients at equilibrium driven by charge neutralisation

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 sustain significant pH gradients without any external energy input. By studying individual condensates on the micron scale using a microdroplet platform, we reveal dense phase pH shifts towards conditions of minimal electrostatic repulsion. We demonstrate that by doing so protein condensates can drive substantial alkaline and acidic gradients which are compositionally tuneable and can extend to complex architectures sustaining multiple unique pH conditions simultaneously. Through in silico characterisation 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 unique regulatory mechanism to modulate biochemical activity in living systems.

biophysics↗

bioRxiv2023

Single-molecule digital sizing of proteins in solution

Proteins constitute the molecular machinery of life and exert their biological function by interacting with other proteins, as well as by assembling into biomolecular complexes and higher order structures. Characterizing the sizes, interactions, and assembly states of proteins is thus key for understanding the normal functional behavior of proteins and for elucidating aberrant processes and interactions that can lead to dysfunction and disease. However, the physical characterization of proteins has remained a challenging problem due to the inherent compositional heterogeneity of protein mixtures as well as the polydisperse nature of protein complexes. Here, we address this challenge by demonstrating measurements of molecular diffusivity of single proteins and protein assemblies in microchannels using single-molecule fluorescence detection. The approach, termed single-molecule microfluidic diffusional sizing (smMDS), allows individual molecules to be counted directly, that is, in a digital manner, to enable calibration-free single-molecule diffusional-sizing-based monitoring of protein hydrodynamic radii even within heterogenous multicomponent mixtures. Applying smMDS to a variety of protein systems, we show that the high sensitivity provided by smMDS enables ultrasensitive sizing of proteins down to the femtomolar concentration range. We further demonstrate the applicability of the approach towards affinity profiling of protein interactions at the single-molecule level and illustrate the potential of smMDS in resolving different assembly states of high- and low-molecular weight protein oligomers. Furthermore, we highlight the digital nature of the detection process by sizing multiple protein species within complex aggregation mixtures. Finally, we apply the approach to characterize nanoscale clusters of a phase separating protein system. Taken together, smMDS constitutes a versatile approach for digital, in-solution characterization of the sizes, interactions, and assembly states of proteins. We anticipate that smMDS will facilitate the discovery of new biomolecular mechanisms of proteins and will find broad applicability in the analysis of protein complexes in the biological, biophysical, and biomedical sciences, and beyond.

biophysics↗

bioRxiv2023

Alpha-synuclein oligomers displace monomeric alpha-synuclein from lipid membranes

Parkinsons disease (PD) is an increasingly prevalent and currently incurable neurodegenerative disorder linked to the accumulation of -synuclein (S) protein aggregates in the nervous system. While S binding to membranes in its monomeric state is correlated to its physiological role, S oligomerisation and subsequent aberrant interactions with lipid bilayers have emerged as key steps in PD-associated neurotoxicity. However, little is known of the mechanisms that govern the interactions of oligomeric S (OS) with lipid membranes and the factors that modulate such interactions. This is in large part due to experimental challenges underlying studies of OS-membrane interactions due to their dynamic and transient nature. Here, we address this challenge by using a suite of microfluidics-based assays that enable in-solution quantification of OS-membrane interactions. We find that OS bind more strongly to highly curved, rather than flat, lipid membranes. By comparing the membrane-binding properties of OS and monomeric S (MS), we further demonstrate that OS bind to membranes with up to 150-fold higher affinity than their monomeric counterparts. Moreover, OS compete with and displace bound MS from the membrane surface, suggesting that disruption to the functional binding of MS to membranes may provide an additional toxicity mechanism in PD. These findings present a unique binding mechanism of oligomers to model membranes, which can potentially be targeted to inhibit the progression of PD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/533646v2_ufig1.gif" ALT="Figure 1"> View larger version (10K): org.highwire.dtl.DTLVardef@4ab089org.highwire.dtl.DTLVardef@18c0709org.highwire.dtl.DTLVardef@220077org.highwire.dtl.DTLVardef@4c85b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Modulating Nucleic Acid Phase Transitions as a Mechanism of Action for Cell-Penetrating Antimicrobial Peptides

Antimicrobial peptides (AMPs) are a vital part of the immune system that helps combat bacterial infections by disrupting the bacterial cell membrane or interacting with intracellular targets. However, the mechanisms by which AMPs act on intracellular targets are not well understood. Using machine learning-based sequence analysis, we have discovered that a significant number of AMPs have a strong tendency to form liquid-like condensates in the presence of nucleic acids, a process known as phase separation. We have demonstrated 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. We propose 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. These findings open up exciting possibilities for the development of antimicrobials that target nucleic acid phase transitions.

microbiology↗

bioRxiv2021

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 critically 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 cation-{pi} and electrostatic interactions. Our work sheds light on HMGA1 phase separation as an emergent biophysical factor in regulating chromatin structure.

biophysics↗

bioRxiv2021

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 underutilised advance is the increase in structural biology throughput, which has progressed from an artisanal endeavour to a monthly throughput of up to 100 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 the binding activity. This allows us to design a one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a non-covalent and non-peptidomimetic 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.

biophysics↗

bioRxiv2020

Rotavirus Replication Factories Are Complex Ribonucleoprotein Condensates

RNA viruses induce 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. We demonstrate that rotavirus proteins NSP5 and NSP2 undergo phase separation in vitro and form RNA-rich condensates in vivo that can be reversibly dissolved by aliphatic diols. During infection, these RNA-protein condensates became less dynamic and impervious to aliphatic diols, indicating a transition from a liquid to solid state. Some aspects of assembly of rotavirus replication factories mirror the formation of cytoplasmic ribonucleoprotein granules, while the selective enrichment of viral transcripts appears to be a unique feature of these condensates. Such complex RNA-protein condensates that underlie replication of RNA viruses represent an attractive target for developing novel therapeutic approaches.

biophysics↗

bioRxiv2020

Machine learning aided top-down protein identification on a microfluidic platform

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 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 novel method for protein identification in a microfluidic format.

biophysics↗

bioRxiv2020

Machine learning models for predicting protein condensate formation from sequence determinants and embeddings

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/. Significance StatementThe tendency of many cellular proteins to form protein-rich biomolecular condensates underlies the formation of subcellular compartments and has been linked to various physiological functions. Understanding the molecular basis of this fundamental process and predicting protein phase behaviour have therefore become important objectives. To develop a global understanding of how protein sequence determines its phase behaviour, here, we constructed bespoke datasets of proteins of varying phase separation propensity and identified explicit biophysical and sequence-specific features common to phase separating proteins. Moreover, by combining this insight with neural network based sequence embeddings, we trained machine learning classifiers that identified phase separating sequences with high accuracy, including from independent external test data. The predictor is available from https://deephase.ch.cam.ac.uk/.

biophysics↗

bioRxiv2020

Direct digital sensing of proteins through single-molecule optofluidics

The detection of proteins is of central importance to biomolecular analysis and diagnostics, yet fundamental limitations due to the surface-based nature of most sensing approaches persist, and limited improvements have been designed to integrate multimodal information beyond concentration measurements. Here we present a single-molecule microfluidic sensing platform for 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-concentrations 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. Together, DigitISA constitutes a new experimental paradigm for the digital sensing of protein biomarkers, and enables analyses of targets that would otherwise be hard or impossible to address by conventional immuno-sensing techniques.

biophysics↗

bioRxiv2020

Single particle zeta-potential measurements reveal the role of electrostatics in protein condensate stability

Liquid-liquid phase separation underlies the formation of biological condensates. Physically, such systems are microemulsions which have a general propensity to fuse and coalesce; however, many condensates persist as independent droplets inside cells. This stability is crucial for their functioning, 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 modelling, we investigate how the forces that sustain condensates impact their stability against fusion. By comparing PR25:PolyU and FUS condensates, we show that a higher condensate surface charge correlates with a lower fusion propensity, and that this behavior can be inferred from their zeta potentials. We reveal that overall stabilization against fusion stems from a combination of repulsive forces between condensates and the effects that surface electrostatics have on lowering surface tension, thus shedding light on the molecular determinants of condensate coalescence.

biophysics↗

bioRxiv2020

Rapid Fractionation and Characterisation of Alpha-Synuclein Oligomers in Solution

Oligomeric intermediates are implicated as neurotoxins in the pathogenesis of protein mis-folding diseases. Structural, biophysical and biochemical characterisation of these species is challenging due to their heterogeneous and transient nature, and their typically low abun-dance. Here, we show that microfluidic free-flow electrophoresis is capable of separating heterogeneous oligomer mixtures on a timescale of seconds, at least two orders of magnitude faster than conventional techniques. This enables analysis of oligomer structural heterogeneity, zeta-potential and immunochemistry with minimal sample perturbation under physiologically-relevant conditions.

biophysics↗