Conformational Ensemble of Disordered Proteins Probed by Solvent Paramagnetic Relaxation Enhancement (sPRE)

Characterization of the conformational ensemble of disordered proteins is highly important for understanding protein folding and aggregation mechanisms, but remains a computational and experimental challenge owing to the dynamic nature of these proteins. New observables that can provide unique insights into transient residual structures in disordered proteins are needed. Here using denatured ubiquitin as a model system, NMR solvent paramagnetic relaxation enhancement (sPRE) measurements provide an accurate and highly sensitive probe for detecting low populations of residual structure in a disordered protein. Furthermore, a new ensemble calculation approach based on sPRE restraints in conjunction with residual dipolar couplings (RDCs) and small‐angle X‐ray scattering (SAXS) is used to define the conformational ensemble of disordered proteins at atomic resolution. The approach presented should be applicable to a wide range of dynamic macromolecules.     

基于组合预测模型的广东省第三产业产值预测

首先分析了影响广东省第三产业发展的主要因素,指出由于上述因素相互制约、相互影响,导致第三产业的发展呈现出高度的非线性特征,并使得单一的预测模型在预测效果和泛化能力方面难以胜任.在此基础上,提出了基于神经网络集成的组合预测模型,对广东省第三产业的发展进行预测,阐述了算法的基本原理和数据处理流程,实证分析表明:基于神经网络集成的组合预测模型要比单一预测模型的预测精度高.     

On the dispute between Boltzmann and Gibbs entropy

The validity of the concept of negative temperature has been recently challenged by arguing that the Boltzmann entropy (that allows negative temperatures) is inconsistent from a mathematical and statistical point of view, whereas the Gibbs entropy (that does not admit negative temperatures) provides the correct definition for the microcanonical entropy. Here we prove that the Boltzmann entropy is thermodynamically and mathematically consistent. Analytical results on two systems supporting negative temperatures illustrate the scenario we propose. In addition we numerically study a lattice system to show that negative temperature equilibrium states are accessible and obey standard statistical mechanics prediction.     

DFT-based Machine Learning for Ensemble Effect of Pd@Au Electrocatalysts on CO2 Reduction Reaction

The ensemble effect due to variation of Pd content in Pd−Au alloys have been widely investigated for several important reactions, including CO₂ reduction reaction (CO₂RR), however, identifying the stable Pd arrangements on the alloyed surface and picking out the active sites are still challenging. Here we use a density functional theory (DFT) based machine-learning (ML) approach to efficiently find the low-energy configurations of Pd−Au(111) surface alloys and the potentially active sites for CO₂RR, fully covering the Pd content from 0 to 100 %. The ML model is actively learning process to improve the predicting accuracy for the configuration formation energy and to find the stable Pd−Au(111) alloyed surfaces, respectively. The local surface properties of adsorption sites are classified into two classes by the K-means clustering approach, which are closely related to the Pd content on Au surface. The classification is reflected in the variation of adsorption energy of CO and H: In the low Pd content range (0–60 %) the adsorption energies over the surface alloys can be tuned significantly, and in the medium Pd content (37-68 %), the catalytic activity of surface alloys for CO₂RR can be increased by increase the Pd content and attributed to the meta-stable active site over the surface. Thus, the active site-dependent reaction mechanism is elucidated based on the ensemble effect, which provides new physical insights to understand the surface-related properties of catalysts.     

Statistical Equivalence of Quantum Chemical Methods for Energy Distribution in Water Clusters

Methods of quantum chemistry are instrumental in understanding molecular structures and properties. However, the results demonstrate significant variability, which is difficult to predict and rationalize. The fundamental question is whether some molecular systems exhibit properties invariant with respect to the computational method. The idea explored here is that collective properties of statistical ensembles should be more robust than characteristics of individual molecules and their arbitrary sets. This effect is demonstrated for the complete set of hydrogen-bond topologies of the dodecahedral water cluster (H₂O)₂₀. Non-Gaussian energy distributions produced by various methods have the same functional form despite strong differences in mean values and standard deviations. The conclusion is tested on methods of different complexity and origin employing a number of criteria. A linear mapping between the energies produced by different methods is discussed. The significance of the results is in establishing a collective equivalence property of quantum chemical methods.     

Improvement in molecule exchange efficiency in Gibbs ensemble Monte Carlo: development and implementation of the continuous fractional component move

The continuous fractional component Monte Carlo (CFC MC) move (J Chem Theory Comput, 2007, 3, 1451) is extended to the Gibbs ensemble. The algorithm is validated against conventional simulations for the Lennard Jones fluid and a flexible water model. The method is also used to compute the vapor-liquid coexistence densities of a model for SO(2). The CFC molecule exchange move relies on the gradual insertion and deletion of molecules in conjunction with a self-adapting bias potential. As a result, the method does not require the formation of spontaneous voids in the dense fluid phase to be successful, leading to molecule exchange acceptance probabilities that are nearly independent of temperature. For example, over 1% of the vapor-liquid molecule exchange moves are successful for water at 280 K, whereas advanced rotational and configurational bias methods have success rates of less than 0.03%. The CFC move can be combined with other Monte Carlo moves to enable efficient simulation of dense strongly associating fluids that are to this point infeasible to model with standard methods.     

RNACluster: An integrated tool for RNA secondary structure comparison and clustering

RNA structure comparison is a fundamental problem in structural biology, structural chemistry, and bioinformatics. It can be used for analysis of RNA energy landscapes, conformational switches, and facilitating RNA structure prediction. The purpose of our integrated tool RNACluster is twofold: to provide a platform for computing and comparison of different distances between RNA secondary structures, and to perform cluster identification to derive useful information of RNA structure ensembles, using a minimum spanning tree (MST) based clustering algorithm. RNACluster employs a cluster identification approach based on a MST representation of the RNA ensemble data and currently supports six distance measures between RNA secondary structures. RNACluster provides a user-friendly graphical interface to allow a user to compare different structural distances, analyze the structure ensembles, and visualize predicted structural clusters.     

用于近红外光谱分析的化学计量学方法研究与应用进展

分析模型的建立是近红外光谱分析的核心技术之一,本文综述了近些年在近红外光谱分析方法中出现的一些新算法和模型建立策略,如基于核函数的非线性校正方法、集成(或共识)的建模策略、多维分辨和校正方法、基于局部样本的建模策略以及二维相关光谱等,并给出了一些方法的具体算法。     

Au nanoparticle decorated silicate network for the amperometric sensing of isoniazid

Au nanoparticle (nAu) based electrochemical platform for the amperometric sensing of isoniazid at sub-nanomolar level is developed. The sol-gel derived 3-dimensional silicate network pre-assembled on a conducting substrate is chemically decorated with nAu of 70-100 nm by seed-mediated growth approach. The Au nanoseeds are first chemisorbed onto the thiol functional groups of the silicate network and their size was enlarged by hydroxylamine seeding. The nanoparticles efficiently catalyze the oxidation of isoniazid at less positive potential. Large decrease in the overpotential and significant enhancement in the anodic peak current with respect to the polycrystalline Au electrode are observed. The nanoparticle based platform is highly sensitive (4.03 ± 0.01 nA/nM) and it linearly responds to isoniazid up to the concentration of 1 mM. It could detect as low as 0.1 nM (S/N = 5) of isoniazid at the potential of 10 mV in aqueous solution without any redox mediator. The catalytic response of the sensing platform depends on the amount of nanoparticles loaded onto the silicate network. Very interestingly, the sensing platform could simultaneously detect isoniazid and hydrazine in their coexistence without compromising the sensitivity. Well separated individual voltammetric response is obtained for both analytes. The sensing platform is highly stable and it can be repeatedly used for 7 days.     

受限狭缝中CO_2-CH_4体系的汽液相平衡性质

受限条件下CO2-CH4体系的相平衡性质对化工工艺过程的设计具有非常重要的意义.采用Gibbs系综Monte Carlo模拟,对220K下CO2-CH4体系在主体相和受限狭缝中的相平衡性质进行了系统地研究.通过主体相模拟与实验结果比较,验证了流体分子势能参数的合理性;通过改变狭缝壁面原子的能量参数,研究了受限环境对CO2-CH4体系汽液相平衡性质的影响.与主体相相比,在硬壁狭缝中,CO2-CH4体系的露点压力增加,泡点压力降低,压力-组成相图变窄,且体系更容易达到超临界状态;在吸引狭缝中,随壁面原子能量参数的增大,CO2-CH4体系的压力-组成相图上移,临界点处CH4的摩尔分数减小,相图变窄.在体系汽液相总组成相同情况下,硬壁狭缝内体系的汽液相密度均比主体相中小;随壁面原子能量参数增大,气相密度变大、液相密度在CH4的摩尔分数较小时变大而当CH4的摩尔分数达到一定值后反而减小.在体系汽液相总组成相同时,受限环境下的汽化热比主体相的汽化热小且随壁面吸引势的增强越来越小;在主体相和硬壁狭缝中体系的汽化热随CH4含量的增加单调减小,而当壁面势能参数较大时汽化热随CH4含量增加先增大后减小.