Spatiotemporal Organization of Catalysts Driven by Enhanced Diffusion

Single molecule-driven nanomotors reveal the dynamic-disordered chemomechanical transduction of active enzymes

Enzymes facilitate the conversion of chemical energy into mechanical work during biochemical reactions, thereby regulating the dynamic metabolic activity of living systems. However, directly observing the energy release facilitated by fluctuating individual enzymes remains a challenge, leading to a contentious debate regarding the underlying reasons for this phenomenon. Here, we aim to overcome this challenge by developing an oscillating nanomotor powered by a single-molecule enzyme, which allows real-time tracking of energy transduction in enzymatic reactions. Through analysis of the shifts in free energy profiles within the nanomotors, our results unveil not only the heterogeneous energy release patterns of individual enzyme molecules but also the dynamic disorder of a particular enzyme in energy release over extended monitoring periods. By exploring six distinct types of single-molecule enzymatic reactions, we provide the direct evidence supporting the argument that the reaction enthalpy governs the enzymatic energy release. This approach has implications for understanding the mechanism of enzymatic catalysis and developing highly efficient nanomotors.

Enzymatic Reaction Enhanced Diffusion Accelerates Cargo Transport through Micro/Nano-Channels

Energy conversion and transfer of enzyme-catalyzed reactions at molecular level are an interesting and challenging scientific topic that helps understanding biological processes in nature. In this study, it is demonstrated that enzyme-catalyzed reactions can enhance diffusion of surrounding molecules and thus accelerate cargo transport within 1D micro/nanochannels. Specifically, urease is immobilized on the inner walls of silica micro/nano-tubes to construct bio-catalytically active micro/nanochannels. The catalytic reaction inside the channels drives a variety of cargoes, including small dye molecules, polymers, and rigid nanoparticles (e.g., quantum dots, QDs), to pass through these micro/nanochannels much faster than they will by free diffusion. The enhanced diffusion of molecular species inside the channels is validated by direct observation of the Brownian motion of tracer particles, and further confirmed by significantly enhanced Raman intensity of reporter molecules. Finite element and Brownian dynamics simulations provide a theoretical understanding of these experimental observations. Furthermore, the effect of the channels' size on the diffusion enhancement is examined. The acceleration effect of the cargo transport through these enzymatically active micro/nanochannels can be turned on or off via chemical activators or inhibitors. This study provides valuable insights on the design of biomimetic channels capable of controlled and efficient transmembrane transport.

DNA Nanomachine-Driven Heterogeneous Quadratic Amplification for Sensitive and Programmable miRNA Profiling

Inspired by the molecular crowding effect in biological systems, a novel heterogeneous quadratic amplification molecular circuit (HEQAC) was developed for sensitive bimodal miRNA profiling (HEQAC-BMP) by combining an MNAzyme-based DNA nanomachine with an entropy-driven catalytic hairpin assembly (E-CHA) autocatalytic circuit. Utilizing ferromagnetic nanomaterials as the substrate for DNA nanomachines, a biomimetic heterogeneous interface was established; thus, a localized molecular crowding system was created that can elevate the local reaction concentration and accelerate the molecular recognition process for a significant threshold signal. Simultaneously, the threshold signal undergoes further amplification by E-CHA and is transformed into a chemical signal, enabling a colorimetric-fluorescence bimodal signal readout. The HEQAC-BMP enables miRNA detection from 10 aM to 10 nM with detection limits of 3.7 aM (colorimetry) and 4.8 aM (fluorometry), respectively. Moreover, the design principle and strategy of HEQAC-BMP can be customized to address other critical viruses or diseases with life-threatening and socioeconomic impacts, enhancing healthcare outcomes for individuals.

Learning continuous potentials from smFRET

Potential energy landscapes are useful models in describing events such as protein folding and binding. While single-molecule fluorescence resonance energy transfer (smFRET) experiments encode information on continuous potentials for the system probed, including rarely visited barriers between putative potential minima, this information is rarely decoded from the data. This is because existing analysis methods often model smFRET output assuming, from the onset, that the system probed evolves in a discretized state space to be analyzed within a hidden Markov model (HMM) paradigm. By contrast, here, we infer continuous potentials from smFRET data without discretely approximating the state space. We do so by operating within a Bayesian nonparametric paradigm by placing priors on the family of all possible potential curves. As our inference accounts for a number of required experimental features raising computational cost (such as incorporating discrete photon shot noise), the framework leverages a structured-kernel-interpolation Gaussian process prior to help curtail computational cost. We show that our structured-kernel-interpolation priors for potential energy reconstruction from smFRET analysis accurately infers the potential energy landscape from a smFRET binding experiment. We then illustrate advantages of structured-kernel-interpolation priors for potential energy reconstruction from smFRET over standard HMM approaches by providing information, such as barrier heights and friction coefficients, that is otherwise inaccessible to HMMs.

Computational Proposal for Tracking Multiple Molecules in a Multifocus Confocal Setup

Tracking single molecules continues to provide new insights into the fundamental rules governing biological function. Despite continued technical advances in fluorescent and non-fluorescent labeling as well as data analysis, direct observations of trajectories and interactions of multiple molecules in dense environments remain aspirational goals. While confocal methods provide a means to deduce dynamical parameters with high temporal resolution, such as diffusion coefficients, they do so at the expense of spatial resolution. Indeed, on account of a confocal volume's symmetry, typically only distances from the center of the confocal spot can be deduced. Motivated by the need for true three dimensional high speed tracking in densely labeled environments, we propose a computational tool for tracking many fluorescent molecules traversing multiple, closely spaced, confocal measurement volumes providing independent observations. Various realizations of this multiple confocal volumes strategy have previously been used for long term, large area, tracking of one fluorescent molecule in three dimensions. What is more, we achieve tracking by directly using single photon arrival times to inform our likelihood and exploit Hamiltonian Monte Carlo to efficiently sample trajectories from our posterior within a Bayesian nonparametric paradigm. A nonparametric paradigm here is warranted as the number of molecules present are, themselves, a priori unknown. Taken together, we provide a computational framework to infer trajectories of multiple molecules at once, below the diffraction limit (the width of a confocal spot), in three dimensions at sub-millisecond or faster time scales.

Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap

While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle’s position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap.

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A feedback trap was used to generate noisy Langevin microbead trajectories

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The potential energy surface is recovered using a Bayesian formulation

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The formulation uses a structured-kernel-interpolation Gaussian process (SKI-GP) to tractably approximate Gaussian process regression for larger datasets

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Thanks to our adaptation of SKI-GP, we have broadened the use of Gaussian processes for natural science applications

A simple theory for molecular chemotaxis driven by specific binding interactions

Recent experiments have suggested that enzymes and other small molecules chemotax toward their substrates. However, the physical forces driving this chemotaxis are currently debated. In this work, we consider a simple thermodynamic theory for molecular chemotaxis that is based on the McMillan-Mayer theory of dilute solutions and Schellman's theory for macromolecular binding. Even in the absence of direct interactions, the chemical binding equilibrium introduces a coupling term into the relevant free energy, which then reduces the chemical potential of both enzymes and their substrates. Assuming a local thermodynamic equilibrium, this binding contribution to the chemical potential generates an effective thermodynamic force that promotes chemotaxis by driving each solute toward its binding partner. Our numerical simulations demonstrate that, although small, this thermodynamic force is qualitatively consistent with several experimental studies. Thus, our study may provide additional insight into the role of the thermodynamic binding free energy for molecular chemotaxis.

Nanoscale Catalyst Chemotaxis Can Drive the Assembly of Functional Pathways

Recent experiments demonstrate molecular chemotaxis or altered diffusion rates of enzymes in the presence of their own substrates. We show here an important implication, namely, that if a nanoscale catalyst A produces a small-molecule ligand product L which is the substrate of another catalyst B, the two catalysts will attract each other. We explore this nonequilibrium producer recruitment force (ProRec) in a reaction-diffusion model. The predicted cat-cat association will be the strongest when A is a fast producer of L and B is a tight binder to it. ProRec is a force that could drive a mechanism (the catpath mechanism) by which catalysts could become spatially localized into functional pathways, such as in the biochemical networks in cells, which can achieve complex goals.

Interconnectivity between Surface Reactivity and Self-Assembly of Kemp Elimination Catalyzing Nanorods

Understanding the fundamental facts behind dynamicity of catalytic processes has been a longstanding quest across disciplines. Herein, we report self-assembly of catalytically active gold nanorods that can be regulated by tuning its reactivity towards a proton transfer reaction at different pH. Unlike substrate-induced templating and co-operativity, the enhanced aggregation rate is due to alteration of catalytic surface charge only during reactivity as negatively charged transition state of reactant (5-nitrobenzisoxazole) is formed on positively charged nanorod while undergoing a concerted E2-pathway. Herein, enhanced diffusivity during catalytic processes might also act as an additional contributing factor. Furthermore, we have also shown that nanosized hydrophobic cavities of clustered nanorods can also efficiently accelerate the rate of an aromatic nucleophilic substitution reaction, which also demonstrates a catalytic phenomenon that can lead to cascading of other reactions where substrates and products of the starting reactions are not directly involved.

Controlled Diffusion of Photoswitchable Receptors by Binding Anti-electrostatic Hydrogen-Bonded Phosphate Oligomers

Dihydrogen phosphate anions are found to spontaneously associate into anti-electrostatic oligomers via hydrogen bonding interactions at millimolar concentrations in DMSO. Diffusion NMR measurements supported formation of these oligomers, which can be bound by photoswitchable anion receptors to form large bridged assemblies of approximately three times the volume of the unbound receptor. Photoisomerization of the oligomer-bound receptor causes a decrease in diffusion coefficient of up to 16%, corresponding to a 70% increase in effective volume. This new approach to external control of diffusion opens prospects in controlling molecular transport using light.

Enhanced Diffusion and Chemotaxis of Enzymes

Many enzymes appear to diffuse faster in the presence of substrate and to drift either up or down a concentration gradient of their substrate. Observations of these phenomena, termed enhanced enzyme diffusion (EED) and enzyme chemotaxis, respectively, lead to a novel view of enzymes as active matter. Enzyme chemotaxis and EED may be important in biology and could have practical applications in biotechnology and nanotechnology. They are also of considerable biophysical interest; indeed, their physical mechanisms are still quite uncertain. This review provides an analytic summary of experimental studies of these phenomena and of the mechanisms that have been proposed to explain them and offers a perspective on future directions for the field.

Advances in biological conversion technologies: new opportunities for reaction engineering

Reaction engineering needs to embrace biological conversion technologies, on the road to identify more sustainable routes for chemical manufacture.

Catalytic enzymes are active matter

Catalysis and mobility of reactants in fluid are normally thought to be decoupled. Violating this classical paradigm, this paper presents the catalyst laws of motion. Comparing experimental data to the theory presented here, we conclude that part of the free energy released by chemical reaction is channeled into driving catalysts to execute wormlike trajectories by piconewton forces performing work of a few k_BT against fluid viscosity, where the rotational diffusion rate dictates the trajectory persistence length. This active motion agitates the fluid medium and produces antichemotaxis, the migration of catalyst down the gradient of the reactant concentration. Alternative explanations of enhanced catalyst mobility are examined critically.

Using a microscopic theory to analyze experiments, we demonstrate that enzymes are active matter. Superresolution fluorescence measurements—performed across four orders of magnitude of substrate concentration, with emphasis on the biologically relevant regime around or below the Michaelis–Menten constant—show that catalysis boosts the motion of enzymes to be superdiffusive for a few microseconds, enhancing their effective diffusivity over longer timescales. Occurring at the catalytic turnover rate, these fast ballistic leaps maintain direction over a duration limited by rotational diffusion, driving enzymes to execute wormlike trajectories by piconewton forces performing work of a few k_BT against viscosity. The boosts are more frequent at high substrate concentrations, biasing the trajectories toward substrate-poor regions, thus exhibiting antichemotaxis, demonstrated here experimentally over a wide range of aqueous concentrations. Alternative noncatalytic, passive mechanisms that predict chemotaxis, cross-diffusion, and phoresis, are critically analyzed. We examine the physical interpretation of our findings, speculate on the underlying mechanism, and discuss the avenues they open with biological and technological implications. These findings violate the classical paradigm that chemical reaction and motility are distinct processes, and suggest reaction–motion coupling as a general principle of catalysis.

Enhanced Diffusion and Chemotaxis at the Nanoscale

Enzymes have been recently proposed to have mechanical activity associated with their chemical activity. In a number of recent studies, it has been reported that enzymes undergo enhanced diffusion in the presence of their corresponding substrate when this substrate is uniformly distributed in solution. Moreover, if the concentration of the substrate is nonuniform, enzymes and other small molecules have been reported to show chemotaxis (biased stochastic movement in the direction of the substrate gradient), typically toward higher concentrations of this substrate, with a few exceptions. The underlying physical mechanisms responsible for enhanced diffusion and chemotaxis at the nanoscale, however, are still not well understood. Understanding these processes is important both for fundamental biological research, for example, in the context of spatial organization of enzymes in metabolic pathways (metabolon formation), as well as for engineering applications, such as in the design of new vehicles for targeted drug delivery. In this Account, we will review the available experimental observations of both enhanced diffusion and chemotaxis, and we will discuss critically the different theories that have been proposed to explain the two. We first focus on enhanced diffusion, beginning with an overview of the experimental results. We then discuss the two main types of mechanisms that have been proposed, namely, active mechanisms relying on the catalytic step of the enzymatic reaction and equilibrium mechanisms, which consider the reversible binding and unbinding of the substrate to the enzyme. We put particular emphasis on an equilibrium model recently introduced by us, which describes how the diffusion of dumbbell-like modular enzymes can be enhanced in the presence of substrate thanks to a binding-induced reduction of the internal fluctuations of the enzyme. We then turn to chemotaxis, beginning with an overview of the experimental evidence for the chemotaxis of enzymes and small molecules, followed by a description of a number of shortcomings and pitfalls in the thermodynamic and phenomenological models for chemotaxis introduced in those and other works in the literature. We then discuss a microscopic model for chemotaxis including both noncontact interactions and specific binding between enzyme and substrate recently developed by us, which overcomes many of these shortcomings and is consistent with the experimental observations of chemotaxis. Finally, we show that the results of this model may be used to engineer chemically active macromolecules that are directed in space via patterning of the concentrations of their substrates.

Powering Motion with Enzymes

Enzymes are ubiquitous in living systems. Apart from traditional motor proteins, the function of enzymes was assumed to be confined to the promotion of biochemical reactions. Recent work shows that free swimming enzymes, when catalyzing reactions, generate enough mechanical force to cause their own movement, typically observed as substrate-concentration-dependent enhanced diffusion. Preliminary indication is that the impulsive force generated per turnover is comparable to the force produced by motor proteins and is within the range to activate biological adhesion molecules responsible for mechanosensation by cells, making force generation by enzymatic catalysis a novel mechanobiology-relevant event. Furthermore, when exposed to a gradient in substrate concentration, enzymes move up the gradient: an example of chemotaxis at the molecular level. The driving force for molecular chemotaxis appears to be the lowering of chemical potential due to thermodynamically favorable enzyme-substrate interactions and we suggest that chemotaxis promotes enzymatic catalysis by directing the motion of the catalyst and substrates toward each other. Enzymes that are part of a reaction cascade have been shown to assemble through sequential chemotaxis; each enzyme follows its own specific substrate gradient, which in turn is produced by the preceding enzymatic reaction. Thus, sequential chemotaxis in catalytic cascades allows time-dependent, self-assembly of specific catalyst particles. This is an example of how information can arise from chemical gradients, and it is tempting to suggest that similar mechanisms underlie the organization of living systems. On a practical level, chemotaxis can be used to separate out active catalysts from their less active or inactive counterparts in the presence of their respective substrates and should, therefore, find wide applicability. When attached to bigger particles, enzyme ensembles act as "engines", imparting motility to the particles and moving them directionally in a substrate gradient. The impulsive force generated by enzyme catalysis can also be transmitted to the surrounding fluid and molecular and colloidal tracers, resulting in convective fluid pumping and enhanced tracer diffusion. Enzyme-powered pumps that transport fluid directionally can be fabricated by anchoring enzymes onto a solid support and supplying the substrate. Thus, enzyme pumps constitute a novel platform that combines sensing and microfluidic pumping into a single self-powered microdevice. Taken in its entirety, force generation by active enzymes has potential applications ranging from nanomachinery, nanoscale assembly, cargo transport, drug delivery, micro- and nanofluidics, and chemical/biochemical sensing. We also hypothesize that, in vivo, enzymes may be responsible for the stochastic motion of the cytoplasm, the organization of metabolons and signaling complexes, and the convective transport of fluid in cells. A detailed understanding of how enzymes convert chemical energy to directional mechanical force can lead us to the basic principles of fabrication, development, and monitoring of biological and biomimetic molecular machines.

A Theory of Enzyme Chemotaxis: From Experiments to Modeling

Enzymes show two distinct transport behaviors in the presence of their substrates in solution. First, their diffusivity enhances with an increasing substrate concentration. In addition, enzymes perform directional motion toward regions with a high substrate concentration, termed as chemotaxis. While a variety of enzymes has been shown to undergo chemotaxis, there remains a lack of quantitative understanding of the phenomenon. Here, we derive a general expression for the active movement of an enzyme in a concentration gradient of its substrate. The proposed model takes into account both the substrate-binding and catalytic turnover step, as well as the enhanced diffusion of the enzyme. We have experimentally measured the chemotaxis of a fast and a slow enzyme: urease under catalytic conditions and hexokinase for both full catalysis and for simple noncatalytic substrate binding. There is good agreement between the proposed model and the experiments. The model is general, has no adjustable parameters, and only requires three experimentally defined constants to quantify chemotaxis: enzyme-substrate binding affinity ( K_d), Michaelis-Menten constant ( K_M), and level of diffusion enhancement in the associated substrate (α).

Phoresis and Enhanced Diffusion Compete in Enzyme Chemotaxis

Chemotaxis of enzymes in response to gradients in the concentration of their substrate has been widely reported in recent experiments, but a basic understanding of the process is still lacking. Here, we develop a microscopic theory for chemotaxis that is valid for enzymes and other small molecules. Our theory includes both nonspecific interactions between enzyme and substrate as well as complex formation through specific binding between the enzyme and the substrate. We find that two distinct mechanisms contribute to enzyme chemotaxis: a diffusiophoretic mechanism due to the nonspecific interactions and a new type of mechanism due to binding-induced changes in the diffusion coefficient of the enzyme. The latter chemotactic mechanism points toward lower substrate concentration if the substrate enhances enzyme diffusion and toward higher substrate concentration if the substrate inhibits enzyme diffusion. For a typical enzyme, attractive phoresis and binding-induced enhanced diffusion will compete against each other. We find that phoresis dominates above a critical substrate concentration, whereas binding-induced enhanced diffusion dominates for low substrate concentration. Our results resolve an apparent contradiction regarding the direction of urease chemotaxis observed in experiments and, in general, clarify the relation between the enhanced diffusion and the chemotaxis of enzymes. Finally, we show that the competition between the two distinct chemotactic mechanisms may be used to engineer nanomachines that move toward or away from regions with a specific substrate concentration.