arXiv subjects

Tuomas P. J. Knowles

Publications and source records attributed to Tuomas P. J. Knowles.

At least 19 recordsLinked to original sources

arXiv2026

Tracer-free Contactless Acoustic Microrheometry Quantifies Viscoelastic Spectrum of Phase-separated Condensates

The rheology of phase-separated condensates plays a central role in applications spanning advanced materials design and cellular processes, yet quantitative characterization of their viscoelasticity remains challenging due to the limitations of existing microrheological methods that require tracer particles or mechanical contact. Here, we establish tracer-free and contactless acoustic microrheometry as a versatile platform for quantifying the frequency-dependent complex shear modulus of single microscale condensates over 0.01-10 Hz. Using spatiotemporally controlled acoustic radiation force generated within a micro-acoustic resonator, this method deforms condensates for creep-recovery and oscillatory viscoelastic measurements. Quantitative validation using dextran condensates in a polyethylene-glycol continuous phase successfully captures their size- and frequency-dependent mechanical responses, while application to nucleic-acid condensates reveals salt-dependent internal viscoelastic changes at single-condensate resolution. By enabling quantitative dissection of condensate mechanics without invasive probes, acoustic microrheometry provides a broadly applicable framework for investigating phase-separated condensates across materials science, soft matter physics, biology, and beyond.

cond-mat.soft↗

arXiv2026

A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease

A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we here develop a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease.

q-bio.QM↗

arXiv2025

Kinetics of seeded protein aggregation: theory and application

``Seeding'' is the addition of preformed fibrils to a solution of monomeric protein to accelerate its aggregation into new fibrils. It is a versatile and widely-used tool for scientists studying protein aggregation kinetics, as it enables the isolation and separate study of discrete reaction steps contributing to protein aggregation, specifically elongation and secondary nucleation. However, the seeding levels required to achieve dominating effects on each of these steps separately have been established largely by trial-and-error, due in part to the lack of availability of integrated rate laws valid for moderate to high seeding levels and generally applicable to all common underlying reaction mechanisms. Here, we improve on a recently developed mathematical method based on Lie symmetries for solving differential equations, and with it derive such an integrated rate law. We subsequently develop simple expressions for the amounts of seed required to isolate each step. We rationalize the empirical observation that fibril seeds must often be broken up into small pieces to successfully isolate elongation. We also derive expressions for average fibril lengths at different times in the aggregation reaction, and explore different methods to break up fibrils. This paper will provide an invaluable reference for future experimental and theoretical studies in which seeding techniques are employed, and should enable more sophisticated analyses than have been performed to date.

physics.chem-ph↗

arXiv2025

Multi-state Protein Design with DynamicMPNN

Structural biology has long been dominated by the one sequence, one structure, one function paradigm, yet many critical biological processes - from enzyme catalysis to membrane transport - depend on proteins that adopt multiple conformational states. Existing multi-state design approaches rely on post-hoc aggregation of single-state predictions, achieving poor experimental success rates compared to single-state design. We introduce DynamicMPNN, an inverse folding model explicitly trained to generate sequences compatible with multiple conformations through joint learning across conformational ensembles. Trained on 46,033 conformational pairs covering 75% of CATH superfamilies and evaluated using Alphafold 3, DynamicMPNN outperforms ProteinMPNN by up to 25% on decoy-normalized RMSD and by 12% on sequence recovery across our challenging multi-state protein benchmark.

cs.LG↗

arXiv2025

High-throughput Screening of the Mechanical Properties of Peptide Assemblies

Peptides are recognized for their varied self-assembly behaviors, forming a wide array of structures and geometries, such as spheres, fibers, and hydrogels, each presenting a unique set of material properties. The functionalities of these materials hold exceptional interest for applications in biology, medicine, photonics, nanotechnology and the food industry. In specific, the ability to exploit peptides as viable and sustainable mechanical materials requires sequence design that enables superior performance, notably a high Young's modulus. As the peptide sequence space is vast, however, even a slight increase in sequence length leads to an exponential increase in the number of potential peptide sequences to be characterized. Here, we combine coarse-grained molecular dynamics simulations, atomic force microscopy experiments and machine learning models to correlate the sequence length and composition with the mechanical properties of self-assembled peptides. We calculate the Young's modulus for all possible amino acid sequences of di- and tripeptides using high-throughput coarse-grained methods, and validate these calculations through in-situ mechanical characterization. For pentapeptides, we select and calculate properties for a subset of sequences to train a machine learning model, which allows us to predict the modulus for other sequences. The combined workflow not only identifies promising peptide candidates with exceptional mechanical performances, but also extends current understanding of the sequence-to-function relationships for peptide materials, for specific applications.

q-bio.BM↗

arXiv2024

Learning the rules of peptide self-assembly through data mining with large language models

Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the impacts of both internal chemical composition and external environmental stimuli on the self-assembly behaviour of these systems. However, there is yet to be a systematic study that gathers this rich literature data and collectively examines these experimental factors to provide a global picture of the fundamental rules that govern protein self-assembly behavior. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining facilitated by a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the collected data, ML models are trained and evaluated, demonstrating excellent accuracy (>80\%) and efficiency in peptide assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. We find that this workflow can substantially improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly. In doing so, novel structures can be accessed for a range of applications including sensing, catalysis and biomaterials.

cond-mat.soft↗

arXiv2024

Competing addition processes give distinct growth regimes in the assembly of 1D filaments

We present a model to describe the concentration-dependent growth of protein filaments. Our model contains two states, a low entropy/high affinity ordered state and a high entropy/low affinity disordered state. Consistent with experiments, our model shows a diffusion-limited linear growth regime at low concentration, followed by a concentration independent plateau at intermediate concentrations, and rapid disordered precipitation at the highest concentrations. We show that growth in the linear and plateau regions is the result of two processes that compete amid the rapid binding and unbinding of non-specific states. The first process is the addition of ordered molecules during the periods where the end of the filament is free of incorrectly bound molecules. The second process is the capture of defects, which occurs when consecutive ordered additions occur on top of incorrectly bound molecules. We show that a key molecular property is the probability that a diffusive collision results in a correctly bound state. Small values of this probability suppress the defect capture growth mode, resulting in a plateau in the growth rate when incorrectly bound molecules become common enough to poison ordered growth. We show that conditions that non-specifically suppress or enhance intermolecular interactions, such as the addition of depletants or osmolytes, have opposite effects on the growth rate in the linear and plateau regimes. In the linear regime stronger interactions promote growth by reducing dissolution events, but in the plateau regime stronger interactions inhibit growth by stabilizing incorrectly bound molecules.

cond-mat.soft↗

arXiv2021

Deformable and robust core-shell protein microcapsules templated by liquid-liquid phase separated microdroplets

Microcapsules are a key class of microscale materials with applications in areas ranging from personal care to biomedicine, and with increasing potential to act as extracellular matrix (ECM) models of hollow organs or tissues. Such capsules are conventionally generated from non-ECM materials including synthetic polymers. Here, we fabricated robust microcapsules with controllable shell thickness from physically- and enzymatically-crosslinked gelatin and achieved a core-shell architecture by exploiting a liquid-liquid phase separated aqueous dispersed phase system in a one-step microfluidic process. Microfluidic mechanical testing revealed that the mechanical robustness of thicker-shell capsules could be controlled through modulation of the shell thickness. Furthermore, the microcapsules demonstrated environmentally-responsive deformation, including buckling by osmosis and external mechanical forces. A sequential release of cargo species was obtained through the degradation of the capsules. Stability measurements showed the capsules were stable at 37 °C for more than two weeks. Finally, all-aqueous liquid-liquid phase separated and multiphase liquid-liquid phase separated systems were generated with the gel-sol transition of microgel precursors. These smart capsules are promising models of hollow biostructures, microscale drug carriers, and building blocks or compartments for active soft materials and robots.

cond-mat.soft↗

arXiv2020

Hamiltonian Dynamics of Saturated Elongation in Amyloid Fiber Formation

Elongation is a fundament process in amyloid fiber growth, which is normally characterized by a linear relationship between the fiber elongation rate and the monomer concentration. However, in high concentration regions, a sub-linear dependence was often observed, which could be explained by a universal saturation mechanism. In this paper, we modeled the saturated elongation process through a Michaelis-Menten like mechanism, which is constituted by two sub-steps -- unspecific association and dissociation of a monomer with the fibril end, and subsequent conformational change of the associated monomer to fit itself to the fibrillar structure. Typical saturation concentrations were found to be $7-70μM$ for A$β$40, $α$-synuclein and etc. Furthermore, by using a novel Hamiltonian formulation, analytical solutions valid for both weak and strong saturated conditions were constructed and applied to the fibrillation kinetics of $α$-synuclein and silk fibroin.

q-bio.QM↗

arXiv2019

Top-down proteomics on a microfluidic platform

Protein identification and profiling is critical for the advancement of cell and molecular biology as well as medical diagnostics. Although mass spectrometry and protein microarrays are commonly used for protein identification, both methods require extensive experimental steps and long data analysis times. Here we present a microfluidic top down proteomics platform giving multidimensional read outs of the essential amino acids of proteins. We obtain hydrodynamic radius and fluorescence signals relating to the content of tryptophans, tyrosines and lysines of proteins using a combination of diffusional sizing of proteins, label-free detection and on-chip labelling of proteins with a latent fluorophore in the solution phase. We thereby achieve identification of proteins on a single microfluidic chip by separating and mapping proteins in multidimensional space based on their characteristic physical parameters. Our results have significant implications in the development of easy and rapid platforms to use for native protein identification in clinical and laboratory settings.

physics.bio-ph↗

arXiv2018

Reaction rate theory for supramolecular kinetics: application to protein aggregation

Probing the reaction mechanisms of supramolecular processes in soft- and biological matter, such as protein aggregation, is inherently challenging. These processes emerge from the simultaneous action of multiple molecular mechanisms, each of which is associated with the rearrangement of a large number of weak bonds, resulting in a complex free energy landscape with many kinetic barriers. Reaction rate measurements of supramolecular processes at different temperatures can offer unprecedented insights into the underlying molecular mechanisms and their thermodynamic properties. However, to be able to interpret such measurements in terms of the underlying microscopic mechanisms, a key challenge is to establish which properties of the complex free energy landscapes are probed by the reaction rate. Here, we present a reaction rate theory for supramolecular kinetics based on Kramers rate theory for diffusive reactions over multiple kinetic barriers, and apply the results to protein aggregation. Using this framework and Monte Carlo simulations, we show that reaction rates for protein aggregation are of the Arrhenius-Eyring type and that the associated activation energies probe only one relevant barrier along the respective free energy landscapes. We apply this advancement to interpret, both in experiments and in coarse-grained computer simulations, reaction rate measurements of amyloid aggregation kinetics in terms of the underlying molecular mechanisms and associated thermodynamic signatures. Our results establish a general platform for probing the mechanisms and energetics of supramolecular phenomena in soft- and biological matter using the framework of chemical kinetics.

physics.bio-ph↗

arXiv2017

DNA-coated Functional Oil Droplets

Many industrial soft materials often include oil-in-water (O/W) emulsions at the core of their formulations. By using tuneable interface stabilizing agents, such emulsions can self-assemble into complex structures. DNA has been used for decades as a thermoresponsive highly specific binding agent between hard and, recently, soft colloids. Up until now, emulsion droplets functionalized with DNA had relatively low coating densities and were expensive to scale up. Here a general O/W DNA-coating method using functional non-ionic amphiphilic block copolymers, both diblock and triblock, is presented. The hydrophilic polyethylene glycol ends of the surfactants are functionalized with azides, allowing for efficient, dense and controlled coupling of dibenzocyclooctane functionalized DNA to the polymers through a strain-promoted alkyne-azide click reaction. The protocol is readily scalable due to the triblock's commercial availability. Different production methods (ultrasonication, microfluidics and membrane emulsification) are used with different oils (hexadecane and silicone oil) to produce functional droplets in various size ranges (sub-micron, $\sim 20\,\mathrm{μm}$ and $> 50\,\mathrm{μm}$), showcasing the generality of the protocol. Thermoreversible sub-micron emulsion gels, hierarchical "raspberry" droplets and controlled droplet release from a flat DNA-coated surface are demonstrated. The emulsion stability and polydispersity is evaluated using dynamic light scattering and optical microscopy. The generality and simplicity of the method opens up new applications in soft matter and biotechnological research and industrial advances.

cond-mat.soft↗

arXiv2017

Kinetics of spontaneous filament nucleation via oligomers: insights from theory and simulation

Nucleation processes are at the heart of a large number of phenomena, from cloud formation to protein crystallization. A recently emerging area where nucleation is highly relevant is the initiation of filamentous protein self-assembly, a process that has broad implications from medicine to nanotechnology. As such, spontaneous nucleation of protein fibrils has received much attention in recent years with many theoretical and experimental studies focussing on the underlying physical principles. In this paper we make a step forward in this direction and explore the early time behaviour of filamentous protein growth in the context of nucleation theory. We first provide an overview of the thermodynamics and kinetics of spontaneous nucleation of protein filaments in the presence of one relevant degree of freedom, namely the cluster size. In this case, we review how key kinetic observables, such as the reaction order of spontaneous nucleation, are directly related to the physical size of the critical nucleus. We then focus on the increasingly prominent case of filament nucleation that includes a conformational conversion of the nucleating building-block as an additional slow step in the nucleation process. Using computer simulations, we study the concentration dependence of the nucleation rate. We find that, under these circumstances, the reaction order of spontaneous nucleation with respect to the free monomer does no longer relate to the overall physical size of the nucleating aggregate but rather to the portion of the aggregate that actively participates in the conformational conversion. Our results thus provide a novel interpretation of the common kinetic descriptors of protein filament formation, including the reaction order of the nucleation step or the scaling exponent of lag times, and put into perspective current theoretical descriptions of protein aggregation.

cond-mat.soft↗

arXiv2016

Intra-chain organisation of hydrophobic residues controls inter-chain aggregation rates of amphiphilic polymers

Aggregation of amphiphiles through the action of hydrophobic interactions is a common feature in soft condensed matter systems and is of particular importance in the context of biophysics as it underlies both the generation of functional biological machinery as well as the formation of pathological misassembled states of proteins. Here we explore the aggregation behaviour of amphiphilic polymers using lattice Monte-Carlo calculations and show that the distribution of hydrophobic residues within the polymer sequence determines the facility with which dry/wet interfaces can be created and that such interfaces drive the aggregation process.

physics.bio-ph↗

arXiv2016

A general reaction network unifies the aggregation behaviour of the A$β$42 peptide and its variants

The amyloid $β$ peptide (A$β$42), whose aggregation is associated with Alzheimer's disease, is an amphiphatic peptide with a high propensity to self-assemble. A$β$42 has a net negative charge at physiological pH and modulations of intermolecular electrostatic interactions can significantly alter its aggregation behaviour. Variations in sequence and solution conditions lead to varied macroscopic behaviour, often resulting in a number of different mechanistic explanations for the aggregation of these closely related systems. Here we alter the electrostatic interactions governing the fibril aggregation kinetics by varying the ionic strength over an order of magnitude, which allows us to sample the space of different reaction mechanisms, and develop a minimal reaction network that explains the experimental kinetics under all the different conditions. We find that an increase in the ionic strength leads to an increased rate of surface catalysed nucleation over fragmentation and eventually to a saturation of this nucleation process. More generally, this reaction network connects previously separate systems, such as mutants of A$β$42 and the wild type, on a continuous mechanistic landscape, thereby providing a unified picture of the aggregation mechanism of A$β$42 and the means of directly comparing the effects of intrinsic modifications of the peptide to those of simple electrostatic shielding.

q-bio.MN↗

arXiv2016

Oligomers of heat-shock proteins: Structures that don't imply function

Most proteins must remain soluble in the cytosol in order to perform their biological functions. To protect against undesired protein aggregation, living cells maintain a population of molecular chaperones that ensure the solubility of the proteome. Here we report simulations of a lattice model of interacting proteins to understand how low concentrations of passive molecular chaperones, such as small heat-shock proteins, suppress thermodynamic instabilities in protein solutions. Given fixed concentrations of chaperones and client proteins, the solubility of the proteome can be increased by tuning the chaperone--client binding strength. Surprisingly, we find that the binding strength that optimizes solubility while preventing irreversible chaperone binding also promotes the formation of weakly bound chaperone oligomers, although the presence of these oligomers does not significantly affect the thermodynamic stability of the solution. Such oligomers are commonly observed in experiments on small heat-shock proteins, but their connection to the biological function of these chaperones has remained unclear. Our simulations suggest that this clustering may not have any essential biological function, but rather emerges as a natural side-effect of optimizing the thermodynamic stability of the proteome.

physics.bio-ph↗

arXiv2015

Particle-based simulations of steady-state mass transport at high Péclet numbers

Conventional approaches for simulating steady-state distributions of particles under diffusive and advective transport at high Péclet numbers involve solving the diffusion and advection equations in at least two dimensions. Here, we present an alternative computational strategy by combining a particle-based rather than a field-based approach with the initialisation of particles in proportion to their flux. This method allows accurate prediction of the steady state and is applicable even at high Péclet numbers where traditional particle-based Monte-Carlo methods starting from randomly initialised particle distributions fail. We demonstrate that generating a flux of particles according to a predetermined density and velocity distribution at a single fixed time and initial location allows for accurate simulation of mass transport under flow. Specifically, upon initialisation in proportion to their flux, these particles are propagated individually and detected by summing up their Monte-Carlo trajectories in predefined detection regions. We demonstrate quantitative agreement of the predicted concentration profiles with the results of experiments performed with fluorescent particles in microfluidic channels under continuous flow. This approach is computationally advantageous and readily allows non-trivial initial distributions to be considered. In particular, this method is highly suitable for simulating advective and diffusive transport in microfluidic devices.

physics.flu-dyn↗

arXiv2014

Crucial role of non-specific interactions in amyloid nucleation

Protein oligomers have been implicated as toxic agents in a wide range of amyloid-related diseases. Yet it has remained unsolved whether the oligomers are a necessary step in the formation of amyloid fibrils, or just a dangerous by-product. Analogously, it has not been resolved if the amyloid nucleation process is a classical one-step nucleation process, or a two-step process involving pre-nucleation clusters. We use coarse-grained computer simulations to study the effect of non-specific attractions between peptides on the primary nucleation process underlying amyloid fibrillization. We find that for peptides that do not attract, the classical one-step nucleation mechanism is possible, but only at non-physiologically high peptide concentrations. At low peptide concentrations, which mimic the physiologically relevant regime, attractive inter-peptide interactions are essential for fibril formation. Nucleation then inevitably takes place through a two-step mechanism involving prefibrillar oligomers. We show that oligomers not only help peptides meet each other, but create an environment that facilitates the conversion of monomers into the $β$-sheet rich form characteristic of fibrils. Nucleation typically does not proceed via the most prevalent oligomers, but via an oligomer size that is only observed in rare fluctuations, which is why such aggregates might be hard to capture experimentally. Finally, we find that the nucleation of amyloid fibrils cannot be described by classical nucleation theory: in the two-step mechanism the critical nucleus size increases both with an increase in concentration and in the inter-peptide interactions, in direct contrast with predictions from classical nucleation theory.

q-bio.BM↗