Microscopic mechanisms of cooperative communications within single nanocatalysts.

Catalysis is a method of accelerating chemical reactions that is critically important for fundamental research as well as for industrial applications. It has been recently discovered that catalytic reactions on metal nanoparticles exhibit cooperative effects. The mechanism of these observations, however, remains not well understood. In this work, we present a theoretical investigation on possible microscopic origin of cooperative communications in nanocatalysts. In our approach, the main role is played by positively charged holes on metal surfaces. A corresponding discrete-state stochastic model for the dynamics of holes is developed and explicitly solved. It is shown that the observed spatial correlation lengths are given by the average distances migrated by the holes before they disappear, while the temporal memory is determined by their lifetimes. Our theoretical approach is able to explain the universality of cooperative communications as well as the effect of external electric fields. Theoretical predictions are in agreement with experimental observations. The proposed theoretical framework quantitatively clarifies some important aspects of the microscopic mechanisms of heterogeneous catalysis.

Theoretical insights into the full description of DNA target search by subdiffusing proteins.

DNA binding proteins (DBPs) diffuse in the cytoplasm to recognise and bind with their respective target sites on the DNA to initiate several biologically important processes. The first passage time distributions (FPTDs) of DBPs are useful in quantifying the timescales of the most-probable search paths in addition to the mean value of the distribution which, strikingly, are decades of order apart in time. However, extremely crowded in vivo conditions or the viscoelasticity of the cellular medium among other factors causes biomolecules to exhibit anomalous diffusion which is usually overlooked in most theoretical studies. We have obtained approximate analytical expressions of a general FPTD and the two characteristic timescales that are valid for any single subdiffusing protein searching for its target in vivo. Our results can be applied to single-particle tracking experiments of target search.

Macromolecular Crowding Facilitates ssDNA Capture within Biological Nanopores: Role of Size Variation and Solution Heterogeneity.

Genetic sequencing is a vital process that requires the transport of charged nucleic acids through transmembrane nanopores. Single-molecule studies show that macromolecular bulk crowding facilitates the capture of these polymers, leading to a high throughput of nanopore sensors. Motivated by these observations, a minimal discrete-state stochastic framework was developed to describe the role of poly(ethylene glycol) (PEG) crowders in varying concentrations in the transport of ssDNA through α-hemolysin nanopores. This theory suggested that the cooperative partitioning of polycationic PEGs controls the capture of ssDNA due to underlying electrostatic interactions. Herein, we investigate the impact of the size variation of PEGs on the capture event. Even though larger crowders attract ssDNA strongly to enhance its capture, our results show that considerable cooperative partitioning of PEGs is also required to achieve high interevent frequency. The exact analytical results are supported by existing single-molecule studies. Since real cellular conditions are heterogeneous, its influence on the ssDNA capture rate is studied by introducing a binary mixture of crowders. Our results indicate that the "polymer-pushing-polymer" concept possibly affects the capture rate depending on the mixture composition. These new findings provide valuable insights into the microscopic mechanism of the capture process, which eventually allows for accurate genome sequencing in crowded solutions.

Microscopic Mechanism of Macromolecular Crowder-Assisted DNA Capture and Translocation through Biological Nanopores.

Biological nanopore sensors are widely used for genetic sequencing as nucleic acids and other molecules translocate through them across membranes. Recent studies have shown that the transport of these polymers through nanopores is strongly influenced by macromolecular bulk crowders. By using poly(ethylene glycol) (PEG) molecules as crowders, experiments have shown an increase in the capture rates and translocation times of polymers through an α-hemolysin (αHL) nanopore, which provides high-throughput signals and accurate sensing. A clear molecular-level understanding of how the presence of PEGs offers such desirable outcomes in nanopore sensing is still missing. In this work, we present a new theoretical approach to probe the effect of PEG crowders on DNA capture and translocation through the αHL nanopore. We develop an exactly solvable discrete-state stochastic model based on the cooperative partitioning of individual polycationic PEGs within the cavity of the αHL nanopore. It is argued that the apparent electrostatic interactions between the DNA and PEGs control all of the dynamic processes. Our analytical predictions find excellent agreements with existing experiments, thereby strongly supporting our theory.

How Heterogeneity Affects Cooperative Communications within Single Nanocatalysts.

Catalysis remains one of the most essential methods in chemical research and industry. Recent experiments have discovered an unusual phenomenon of catalytic cooperativity, when a reaction at one active site can stimulate reactions at neighboring sites within single nanoparticles. While theoretical analysis established that the transport of charged holes is responsible for this phenomenon, it does not account for inhomogeneity in the structural and dynamic properties of single nanocatalysts. Here, we investigate the effect of heterogeneity on catalytic communications by extending a discrete-state stochastic framework to random distributions of the transition rates. Our explicit calculations of spatial and temporal properties of heterogeneous systems in comparison with homogeneous systems predict that the strength of cooperativity increases, while the communication lifetimes and distances decrease. Monte Carlo computer simulations support theoretical calculations, and microscopic arguments to explain these observations are also presented. Our theoretical analysis clarifies some important aspects of molecular mechanisms of catalytic processes.

Influence of Nonspecific Interactions between Proteins and In Vivo Cytoplasmic Crowders in Facilitated Diffusion of Proteins: Theoretical Insights.

The binding of proteins to their respective specific sites on the DNA through facilitated diffusion serves as the initial step of various important biological processes. While this search process has been thoroughly investigated via in vitro studies, the cellular environment is complex and may interfere with the protein's search dynamics. The cytosol is heavily crowded, which can potentially modify the search by nonspecifically interacting with the protein that has been mostly overlooked. In this work, we probe the target search dynamics in the presence of explicit crowding agents that have an affinity toward the protein. We theoretically investigate the role of such protein-crowder associations in the target search process using a discrete-state stochastic framework that allows for the analytical description of dynamic properties. It is found that stronger nonspecific associations between the crowder and proteins can accelerate the facilitated diffusion of proteins in comparison with a purely inert, rather weakly interacting cellular environment. This effect depends on how strong these associations are, the spatial positions of the target with respect to the crowders, and the size of the crowded region. Our theoretical results are also tested with Monte Carlo computer simulations. Our predictions are in qualitative agreement with existing experimental observations and computational studies.

Theoretical Tools to Quantify Stochastic Fluctuations in Single-Molecule Catalysis by Enzymes and Nanoparticles.

Single-molecule microscopic techniques allow the counting of successive turnover events and the study of the time-dependent fluctuations of the catalytic activities of individual enzymes and different sites on a single heterogeneous nanocatalyst. It is important to establish theoretical methods to obtain the statistical measurements of such stochastic fluctuations that provide insight into the catalytic mechanism. In this review, we discuss a few theoretical frameworks for evaluating the first passage time distribution functions using a self-consistent pathway approach and chemical master equations, to establish a connection with experimental observables. The measurable probability distribution functions and their moments depend on the molecular details of the reaction and provide a way to quantify the molecular mechanisms of the reaction process. The statistical measurements of these fluctuations should provide insight into the enzymatic mechanism.

Understanding the Reaction Dynamics on Heterogeneous Catalysts Using a Simple Stochastic Approach.

Recent experimental advances on investigating nanoparticle catalysts with multiple active sites provided a large amount of quantitative information on catalytic processes. These observations stimulated significant theoretical efforts, but the underlying molecular mechanisms are still not well-understood. We introduce a simple theoretical method to analyze the reaction dynamics on catalysts with multiple active sites based on a discrete-state stochastic description and obtain a comprehensive description of the dynamics of chemical reactions on such catalysts. We explicitly determine how the dynamics of catalyzed chemical reactions depend on the number of active sites, on the number of intermediate chemical transitions, and on the topology of underlying chemical reactions. It is argued that the theory provides quantitative bounds for realistic dynamic properties of catalytic processes that can be directly applied to analyze the experimental observations. In addition, this theoretical approach clarifies several important aspects of the molecular mechanisms of chemical reactions on catalysts.