Reaction-Path Statistical Mechanics of Enzymatic Kinetics

We introduce a reaction-path statistical mechanics formalism based on the principle of large deviations to quantify the kinetics of single-molecule enzymatic reaction processes under the Michaelis-Menten mechanism, which exemplifies an out-of-equilibrium process in the living system. Our theoretical approach begins with the principle of equal a priori probabilities and defines the reaction path entropy to construct a new nonequilibrium ensemble as a collection of possible chemical reaction paths. As a result, we evaluate a variety of path-based partition functions and free energies using the formalism of statistical mechanics. They allow us to calculate the timescales of a given enzymatic reaction, even in the absence of an explicit boundary condition that is necessary for the equilibrium ensemble. We also consider the large deviation theory under a closed-boundary condition of the fixed observation time to quantify the enzyme-substrate unbinding rates. The result demonstrates the presence of a phase-separation-like, bimodal behavior in unbinding events at a finite timescale, and the behavior vanishes as its rate function converges to a single phase in the long-time limit.Comment: 29 pages, 7 figure

Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection

We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained formally through treating each series as a point in a Hilbert space, placing a kernel at those points, and summing the kernels (a “point approach”), or through using Kernel Density Estimation to approximate the distributions of Fourier mode coefficients to infer a probability density (a “Fourier approach”). We refer to these approaches as Functional Kernel Density Estimation for Anomaly Detection as they both yield functionals that can score a time series for how anomalous it is. Both methods naturally handle missing data and apply to a variety of settings, performing well when compared with an outlyingness score derived from a boxplot method for functional data, with a Principal Component Analysis approach for functional data, and with the Functional Isolation Forest method. We illustrate the use of the proposed methods with aviation safety report data from the International Air Transport Association (IATA)

A commensal-encoded genotoxin drives restriction of Vibrio cholerae colonization and host gut microbiome remodeling.

SignificanceIn a polymicrobial battlefield where different species compete for nutrients and colonization niches, antimicrobial compounds are the sword and shield of commensal microbes in competition with invading pathogens and each other. The identification of an Escherichia coli-produced genotoxin, colibactin, and its specific targeted killing of enteric pathogens and commensals, including Vibrio cholerae and Bacteroides fragilis, sheds light on our understanding of intermicrobial interactions in the mammalian gut. Our findings elucidate the mechanisms through which genotoxins shape microbial communities and provide a platform for probing the larger role of enteric multibacterial interactions regarding infection and disease outcomes

Highly selective PtCo bimetallic nanoparticles on silica for continuous production of hydrogen from aqueous phase reforming of xylose

Hydrogen (H2) is a promising energy vector for mitigating greenhouse gas emissions. Lignocellulosic biomass waste has been introduced as one of the abundant and carbon-neutral H2 sources. Among those, xylose with its short carbon chain has emerged attractive, where H2 can be catalytically released in an aqueous reactor. In this study, a composite catalyst system consisting of silica (SiO2)-supported platinum (Pt)-cobalt (Co) bimetallic nanoparticles was developed for aqueous phase reforming of xylose conducted at 225 °C and 29.3 bar. The PtCo/SiO2 catalyst showed a significantly higher H2 production rate and selectivity than that of Pt/SiO2, whereas Co/SiO2 shows no activity in H2 production. The highest selectivity for useful liquid byproducts was obtained with PtCo/SiO2. Moreover, CO2 emissions throughout the reaction were reduced compared to those of monometallic Pt/SiO2. The PtCo bimetallic nanocatalyst offers an inexpensive, sustainable, and durable solution with high chemical selectivity for scalable reforming of hard-to-ferment pentose sugars

The Distinct Metabolic Phenotype of Lung Squamous Cell Carcinoma Defines Selective Vulnerability to Glycolytic Inhibition

Adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) are the two predominant subtypes of non-small cell lung cancer (NSCLC) and are distinct in their histological, molecular and clinical presentation. However, metabolic signatures specific to individual NSCLC subtypes remain unknown. Here, we perform an integrative analysis of human NSCLC tumour samples, patient-derived xenografts, murine model of NSCLC, NSCLC cell lines and The Cancer Genome Atlas (TCGA) and reveal a markedly elevated expression of the GLUT1 glucose transporter in lung SqCC, which augments glucose uptake and glycolytic flux. We show that a critical reliance on glycolysis renders lung SqCC vulnerable to glycolytic inhibition, while lung ADC exhibits significant glucose independence. Clinically, elevated GLUT1-mediated glycolysis in lung SqCC strongly correlates with high 18F-FDG uptake and poor prognosis. This previously undescribed metabolic heterogeneity of NSCLC subtypes implicates significant potential for the development of diagnostic, prognostic and targeted therapeutic strategies for lung SqCC, a cancer for which existing therapeutic options are clinically insufficient

Reaction-path statistical mechanics of enzymatic kinetics

We introduce a reaction-path statistical mechanics formalism based on the principle of large deviations to quantify the kinetics of single-molecule enzymatic reaction processes under the Michaelis-Menten mechanism, which exemplifies an out-of-equilibrium process in the living system. Our theoretical approach begins with the principle of equal a priori probabilities and defines the reaction path entropy to construct a new nonequilibrium ensemble as a collection of possible chemical reaction paths. As a result, we evaluate a variety of path-based partition functions and free energies by using the formalism of statistical mechanics. They allow us to calculate the timescales of a given enzymatic reaction, even in the absence of an explicit boundary condition that is necessary for the equilibrium ensemble. We also consider the large deviation theory under a closed-boundary condition of the fixed observation time to quantify the enzyme-substrate unbinding rates. The result demonstrates the presence of a phase-separation-like, bimodal behavior in unbinding events at a finite timescale, and the behavior vanishes as its rate function converges to a single phase in the long-time limit.& nbsp;& nbsp;Published under an exclusive license by AIP Publishing.N

Deep Learning Model for Predicting Solvation Free Energies in Generic Organic Solvents

Prediction of aqueous solubilities or hydration free energies is an extensively studied area in machine learning applications on chemistry since water is the sole solvent in the living system. However, for non-aqueous solutions, few machine learning studies have been undertaken so far despite the fact that the solvation mechanism plays an important role in various chemical reactions. Here, we introduce a novel, machine-learning based quantitative structure-property prediction method which predicts solvation free energies for various organic solute and solvent systems. A novelty of our method involves two separate solvent and solute encoder networks that can quantify structural features of given compounds via word embedding and recurrent layers, with the attention mechanism which extracts important substructures from outputs of recurrent neural networks. As a result, the predictor network calculates solvation free energy of a given mixture using features from encoders. With results obtained from extensive calculations on 2495 solute-solvent mixtures, we demonstrate that our methodology outperforms both ab initio and MD solvation model in terms of estimation error for solvation energy

Computational investigation of dynamical heterogeneity in ionic liquids based on the restricted primitive model

© 2022 Korean Chemical Society, Seoul & Wiley-VCH GmbHThe locally heterogeneous dynamics of the glass-forming liquids is an unusual and distinct phenomenon that is not observed in a typical liquid system above the melting temperature. Room-temperature ionic liquids (RTILs) are usually good glass formers, exhibiting various interesting features of glassy materials such as nonexponential and non-Arrenhius relaxations and transport decoupling behaviors. In this study, we propose a minimal model that represents the general structural features of RTILs to study their dynamics by extending the restricted primitive model. We mainly focus on the heterogeneous dynamics of the RTIL as a glass former and investigate how the charge symmetry of each ion affects the structural relaxation, diffusive behavior, and the dynamic heterogeneity lifetime of the system.N

Delfos: deep learning model for prediction of solvation free energies in generic organic solvents

Prediction of aqueous solubilities or hydration free energies is an extensively studied area in machine learning applications in chemistry since water is the sole solvent in the living system. However, for nonaqueous solutions, few machine learning studies have been undertaken so far despite the fact that the solvation mechanism plays an important role in various chemical reactions. Here, we introduce Delfos (deep learning model for solvation free energies in generic organic solvents), which is a novel, machine-learning-based QSPR method which predicts solvation free energies for various organic solute and solvent systems. A novelty of Delfos involves two separate solvent and solute encoder networks that can quantify structural features of given compounds via word embedding and recurrent layers, augmented with the attention mechanism which extracts important substructures from outputs of recurrent neural networks. As a result, the predictor network calculates the solvation free energy of a given solvent-solute pair using features from encoders. With the results obtained from extensive calculations using 2495 solute-solvent pairs, we demonstrate that Delfos not only has great potential in showing accuracy comparable to that of the state-of-the-art computational chemistry methods, but also offers information about which substructures play a dominant role in the solvation process.Y