An integrative in silico approach to the structure of Omp33-36 in Acinetobacter baumannii

Omp33-36 in A. baumannii, a bacterium causing serious nosocomial infections, is a virulence factor associated with the pathogen metabolic fitness as well as its adherence and invasion to human epithelial cells. This protein is also involved in interaction of the bacteria with host cells by binding to fibronectin. Moreover, Omp33-36 renders cytotoxicity to A. baumannii in addition to inducing apoptosis and modulation of autophagy. In the present study, an integrated strategy is launched to pierce into the 3D structure of Omp33-36 protein. The signal peptide within the sequence was determined, then, topology as well as secondary and tertiary structures of the protein were predicted. The mature protein assigned as a 14-stranded barrel in which residues 1–19 is removed as signal peptide. The obtained 3D models were evaluated in terms of quality; and then, served as queries to find similar protein structures. The hits were analyzed regarding topology among which 14-stranded were considered. The most qualified model was refined and then its sequence aligned to its counterpart similar structure protein (CymA from Klebsiella oxytoca). The determined structure of Omp33-36 could justify its porin function and carbapenem-resistance associated with the loss of this protein.     

Planar Tetracoordinate Silicon in Organic Molecules As Carbenoid-Type Amphoteric Centers: A Computational Study

Designing and synthesizing a stable compound with a planar tetracoordinate silicon (ptSi) center is a challenging goal for chemists. Here, a series of potential aromatic ptSi compounds composed of four conjugated rings shared by a centrally embedded Si atom are theoretically designed and computationally verified. Both Born–Oppenheimer molecular dynamics (BOMD) simulations and potential energy surface scannings verify the high stability and likely existence of these compounds, particularly Si-16-5555 (SiN₄C₈H₈) with 16 π electrons, under standard ambient temperature and pressure. Notably, the Hückel aromaticity rule, which works well for single rings, is inconsistent with the high stability of Si-16-5555 where the 16 p electrons are spread over four five-membered rings fused together. Bonding analyses show that the strong electron donation from the peripheral 12-membered conjugated ring with 16 π electrons to the vacant central atomic orbital Si 3p_z leads to the stabilization for both the ptSi coordination and planar aromaticity. The partial occupation of Si 3p_z results in the peculiar carbenoid-type behaviors for the amphoteric center. By modulating the electron density on the ring with substituent groups, we can regulate the nucleophilic and electrophilic properties of the central Si.     

Physical implication of two problems in transition prediction of boundary layers based on linear stability theory

Up to now,the most widely used method for transition prediction is the one based on linear stability theory.When it is applied to three-dimensional boundary layers,one has to choose the direction,or path,along which the growth rate of the disturbance is to be integrated.The direction given by using saddle point method in the theory of complex variable function is seen as mathematically most reasonable.However,unlike the saddle point method applied to water waves,here its physical meaning is not so obvious,as the frequency and wave number may be complex.And on some occasions,in advancing the integration of the growth rate of the disturbance,up to a certain location,one may not be able to continue the integration,because the condition for specifying the direction set by the saddle point method can no longer be satisfied on the basis of continuously varying wave number.In this paper,these two problems are discussed,and suggestions for how to do transition prediction under the latter condition are provided.     

Automated RNA tertiary structure prediction from secondary structure and low‐resolution restraints

A novel protocol for all‐atom RNA tertiary structure prediction is presented that uses restrained molecular mechanics and simulated annealing. The restraints are from secondary structure, covariation analysis, coaxial stacking predictions for helices in junctions, and, when available, cross‐linking data. Results are demonstrated on the Alu domain of the mammalian signal recognition particle RNA, the Saccharomyces cerevisiae phenylalanine tRNA, the hammerhead ribozyme, the hepatitis C virus internal ribosomal entry site, and the P4–P6 domain of the Tetrahymena thermophila group I intron. The predicted structure is selected from a pool of decoy structures with a score that maximizes radius of gyration and base–base contacts, which was empirically found to select higher quality decoys. This simple ab initio approach is sufficient to make good predictions of the structure of RNAs compared to current crystal structures using both root mean square deviation and the accuracy of base–base contacts. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour-Liquid equilibria or Liquid-Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid-Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔC_p term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔC_p term can be neglected and will not have a great impact on the results.     

人工神经网络研究硝基苯化合物结构与毒性的关系

基于简单的化学基团描述符,应用人工神经网络研究了硝基苯类化合物对黑呆头鱼的毒性构效关系,并与多元线性回归相比较,结果显示了人工神经网络处理非线性问题的优越性.     

Comparison and application of different empirical correlations for estimating the hydrate safety margin of oil-based drilling fluids containing ethylene glycol

As the oil and gas industries continue to increase their activity in deep water, gas hydrate hazards will become more serious and challenging, both at present and in the future. Accurate predictions of the hydrate-free zone and the suitable addition of salts and/or alcohols in preparing drilling fluids are particularly important both in preventing hydrate problems and decreasing the cost of drilling operations. In this paper, we compared several empirical correlations commonly used to estimate the hydrate inhibition effect of aqueous organic and electrolyte solutions using experiments with ethylene glycol (EG) as a hydrate inhibitor. The results show that the Najibi et al. correlation (for single and mixed thermodynamic inhibitors) and the Ostergaard et al. empirical correlation (for single thermodynamic inhibitors) are suitable for estimating the hydrate safety margin of oil-based drilling fluids (OBDFs) in the presence of thermodynamic hydrate inhibitors. According to the two correlations, the OBDF, composed of 1.6 L vaporizing oil, 2% emulsifying agent, 1% organobentonite, 0.5% SP-1, 1% LP-1, 10% water and 40% EG, can be safely used at a water depth of up to 1900 m. However, for more accurate predictions for drilling fluids, the effects of the solid phase, especially bentonite, on hydrate inhibition need to be considered and included in the application of these two empirical correlations.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

Exploring the Limits of Transition-Metal Fluorination at High Pressures

Fluorination is a proven method for challenging the limits of chemistry, both structurally and electronically. Here we explore computationally how pressures below 300 GPa affect the fluorination of several transition metals. A plethora of new structural phases are predicted along with the possibility for synthesizing four unobserved compounds: TcF₇, CdF₃, OsF₈, and IrF₈. The Ir and Os octaflourides are both predicted to be stable as quasi-molecular phases with an unusual cubic ligand coordination, and both compounds formally correspond to a high oxidation state of +8. Electronic-structure analysis reveals that otherwise unoccupied 6p levels are brought down in energy by the combined effects of pressure and a strong ligand field. The valence expansion of Os and Ir enables ligand-to-metal F 2p→M 6p charge transfer that strengthens M−F bonds and decreases the overall bond polarity. The lower stability of IrF₈, and the instability of PtF₈ and several other compounds below 300 GPa, is explained by the occupation of M−F antibonding orbitals in octafluorides with a metal-valence-electron count exceeding 8.     

固体异质结界面结构预测方法及AutoInterface程序实现

预测材料异质结的界面原子结构对于理解界面对性能的影响至关重要. 目前, 从理论上预测材料界面结构仍具有极大挑战, 主要是缺乏普适有效的理论计算方法. 本文介绍了本课题组在异质结界面结构预测方面取得的最新进展. 结合马氏体相变唯象理论、 图论和随机表面行走算法, 提出了界面结构的一种有效预测方法, 可以实现自动化的计算预测. 通过GaP/TiO₂半导体异质结等展示了该方法的有效性和在催化等领域的应用前景.