An S‐Oxygenated [NiFe] Complex Modelling Sulfenate Intermediates of an O2‐Tolerant Hydrogenase

To understand the molecular details of O₂‐tolerant hydrogen cycling by a soluble NAD⁺‐reducing [NiFe] hydrogenase, we herein present the first bioinspired heterobimetallic S‐oxygenated [NiFe] complex as a structural and vibrational spectroscopic model for the oxygen‐inhibited [NiFe] active site. This compound and its non‐S‐oxygenated congener were fully characterized, and their electronic structures were elucidated in a combined experimental and theoretical study with emphasis on the bridging sulfenato moiety. Based on the vibrational spectroscopic properties of these complexes, we also propose novel strategies for exploring S‐oxygenated intermediates in hydrogenases and similar enzymes.     

The molecular electrostatic potential of some simple molecules

The calculation of the molecular electrostatic potential from simplified models of the electron density is considered. Results are shown for water, hydrogen fluoride and ammonia. Little loss of accuracy is evident when the density is represented by a linear sum of well-chosen Gaussians. When these are further simplified into sets of point charges the inner parts of the molecule are poorly represented. More elaborate point moments make the representation worse. On the other hand a mixed representation with point charges and one diffuse Gaussian gives all the essential features of the potential of these molecules.     

The approximation of electron densities

This paper discusses the approximate representation of the electron density produced by an ab initio calculation. A linear combination of Gaussians is fitted to the density by minimizing a functional which is the consequent error in field-energy. The practical implementation of the procedure, following a Gaussian 80 calculation, is described and some of the complications are analysed.     

NAD(P)H辅酶模型物结构与反应性关系的研究──3-位酰基的电子效应

合成了一系列3酰胺基氮取代的NAD(P)H模型物,测定了其与5硝基异喹啉正离子的二级反应速率常数,并与模型物的氧化还原电势进行了比较.实验结果表明,模型物3位酰基氧一方面可离域二氢吡啶环上N的电子;另一方面负电性的3位酰基氧在反应过渡态中又可引起分子内和分子间的两种静电作用;3位酰基的电子效应对模型物动力学反应性的影响是这两种效应综合作用的结果.     

Simple correction for perturbations of peak amplitudes in statistical models of overlap

Summary The application of statistical models of overlap (SMOs) to saturated separations is made possible by theory that addresses variable peak amplitudes. These amplitudes cause peak widths to vary, and this variation can be modeled by a random variable whose effect on the probability of overlap is expressed by a convolution integral. Modified probabilities of overlap are derived for both homogeneous and nonhomogeneous one-dimensional separations, and the new probabilities are compared to results determined from published computer simulations. The new theory can describe overlap at saturations that are 3 to 4 times larger than before. Previously reported experimental chromatograms are reinterpreted to show the capabilities of theory. The theoretical extension is an important step towards making SMOs into practical tools for screening analytical separations.     

Effect of combining rules for cubic equations of state on the prediction of double retrograde vaporization

In this work, the phenomenon of double retrograde vaporization (DRV) is simulated using the Peng–Robinson equation of state with the classical mixing rules and several combining rules for the cross-energy and cross-co-volume parameters. The binary interaction parameters are set equal to zero in all cases, i.e., the calculations are entirely predictive. An interesting conclusion is that the predictions using the classical combining rules (geometric mean rule for a_ij and arithmetic mean rule for b_ij) provide the best agreement with the experimental data for all the systems tested: methane + n-butane, methane + n-pentane, ethane + limonene, and ethane + linalool. Another interesting observation is that several combining rules for b_ij, other than the arithmetic mean rule, predict the existence of three phases in equilibrium in a very narrow temperature range close to the critical temperature of methane in the methane + n-pentane system, even though, literature data indicates that n-hexane is the first n-alkane to present partial liquid phase immiscibility with methane.     

Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems?

Molecular dynamics (MD) simulation has become a powerful tool to investigate the structurefunction relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets containing millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape (geometric clustering) and kinetics (kinetic clustering). In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, agglomerative algorithms (single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage), center-based algorithms (K-Means, K-Medoids, K-Centers, and APM), density-based algorithms (neighbor-based, DBSCAN, density-peaks, and Robust-DB), and spectral-based algorithms (PCCA and PCCA+). In particular, differences between geometric and kinetic clustering metrics will be discussed along with the performances of different clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algorithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.     

Revised mechanism of carboxylation of ribulose‐1,5‐biphosphate by rubisco from large scale quantum chemical calculations

Here, we describe a computational approach for studying enzymes that catalyze complex multi‐step reactions and apply it to Ribulose 1,5‐bisphosphate carboxylase–oxygenase (Rubisco), the enzyme that fixes atmospheric carbon dioxide within photosynthesis. In the 5‐step carboxylase reaction, the substrate Ribulose‐1,5‐bisphosphate (RuBP) first binds Rubisco and undergoes enolization before binding the second substrate, CO₂. Hydration of the RuBP.CO₂ complex is followed by C C bond scission and stereospecific protonation. However, details of the roles and protonation states of active‐site residues, and sources of protons and water, remain highly speculative. Large‐scale computations on active‐site models provide a means to better understand this complex chemical mechanism. The computational protocol comprises a combination of hybrid semi‐empirical quantum mechanics and molecular mechanics within constrained molecular dynamics simulations, together with constrained gradient minimization calculations using density functional theory. Alternative pathways for hydration of the RuBP.CO₂ complex and associated active‐site protonation networks and proton and water sources were investigated. The main findings from analysis of the resulting energetics advocate major revision to existing mechanisms such that: hydration takes place anti to the CO₂; both hydration and C C bond scission require early protonation of CO₂ in the RuBP.CO₂ complex; C C bond scission and stereospecific protonation reactions are concerted and, effectively, there is only one stable intermediate, the C3‐gemdiolate complex. Our main conclusions for interpreting enzyme kinetic results are that the gemdiolate may represent the elusive Michaelis–Menten‐like complex corresponding to the empirical K_m (=K_c) with turnover to product via bond scission concerted with stereospecific protonation consistent with the observed catalytic rate. © 2018 Wiley Periodicals, Inc.     

A simple,fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part I: Estimation of reference column geometry and thermodynamic parameters

The transfer of retention times based on thermodynamic models between columns can aid in separation optimization and compound identification in gas chromatography. Although earlier investigations have been reported, this problem remains unsuccessfully addressed. One barrier is poor predictive accuracy when moving from a reference column or system to a new target column or system. This is attributed to challenges associated with the accurate determination of the effective geometric parameters of the columns. To overcome this, we designed least squares‐based models that account for geometric parameters of the columns and thermodynamic parameters of compounds as they partition between mobile and stationary phases. Quasi‐Newton‐based algorithms were then used to perform the numerical optimization. In this first of three parts, the model used to determine the geometric parameters of the reference column and the thermodynamic parameters of compounds subjected to separation is introduced. As will be shown, the overall approach significantly improves the predictive accuracy and transferability of thermodynamic data (and retention times) between columns of the same stationary phase chemistry. The data required for the determination of the thermodynamic parameters and retention time prediction are obtained from fast and simple experiments. The proposed model and optimization algorithms were tested and validated using simulated and experimental data.