Estimation of relative order tensors, and reconstruction of vectors in space using unassigned RDC data and its application

Advances in NMR instrumentation and pulse sequence design have resulted in easier acquisition of Residual Dipolar Coupling (RDC) data. However, computational and theoretical analysis of this type of data has continued to challenge the international community of investigators because of their complexity and rich information content. Contemporary use of RDC data has required a-priori assignment, which significantly increases the overall cost of structural analysis. This article introduces a novel algorithm that utilizes unassigned RDC data acquired from multiple alignment media (nD-RDC, n  3) for simultaneous extraction of the relative order tensor matrices and reconstruction of the interacting vectors in space.Estimation of the relative order tensors and reconstruction of the interacting vectors can be invaluable in a number of endeavors. An example application has been presented where the reconstructed vectors have been used to quantify the fitness of a template protein structure to the unknown protein structure. This work has other important direct applications such as verification of the novelty of an unknown protein and validation of the accuracy of an available protein structure model in drug design. More importantly, the presented work has the potential to bridge the gap between experimental and computational methods of structure determination.     

The definition of the effective interaction energy for astrophysical relevant reactions

Experimental studies of nuclear reactions of astrophysical interest are hampered by the exponential drop of the cross-section with decreasing energy. Generally, the effects of the projectile energy loss in the target cannot be neglected and the reaction yield is proportional to the average value of the cross-section over the interaction energies inside the target. Local cross-section values, instead of averaged, are needed to evaluate stellar reaction rates. To deal with this, several different effective interaction energy definitions have been introduced during the years, leading to potentially discrepant results. Thus, a well-defined procedure for data reduction is required. This work briefly reviews the theoretical ground for the experimental cross-section data reduction and the effective interaction energies definitions up to now introduced. The self-consistent approach introduced by B.W. Filippone et al. is discussed and its application to the data analysis of non-resonant and narrow-resonant reactions is presented. A comparison of the results obtained using the different approaches is also reported.     

Fine-structure energy levels, oscillator strengths and lifetimes in Co XV

Excitation energies from ground state for 97 fine-structure levels as well as oscillator strengths and radiative decay rates for all electric-dipole-allowed and intercombination transitions among the fine-structure levels of the terms belonging to the (1s ²2s ²2p ⁶)3s ²3p, 3s ³ p ², 3s ²3d, 3p ³, 3s3p3d, 3p ²3d, 3s3d ², 3s ²4s, 3s ²4p, 3s ²4d, 3s ²4f, and 3s3p4s configurations of Co XV are calculated, using extensive configuration-interaction (CI) wave functions, obtained with the CIV3 computer code of Hibbert. The important relativistic effects in intermediate coupling are included through the Breit-Pauli approximation via spin-orbit, spin-other-orbit, spin-spin, Darwin and mass correction terms. Small adjustments to the diagonal elements of the Hamiltonian matrices have been made. Our calculated excitation energies, including their ordering, are in excellent agreement with the experimental results and the experimentally compiled energy values of the National Institute for Standards and Technology (NIST) wherever available. The mixing among several fine-structure levels is found to be very strong, with most of the strongly mixed levels belonging to the (1s ²2s ²2p ⁶)3p ²3d and 3s3d ² configurations. The strong mixing among several fine-structure levels makes it very difficult to identify them uniquely. Perhaps, that may be the reason for the lack of both experimental and theoretical results for these levels. We believe that our extensive calculated values can guide experimentalists in identifying the fine-structure levels in their future work. From our radiative decay rates we have also calculated radiative lifetimes of some fine-structure levels. In this calculation we also predict new data for several fine-structure levels where no other theoretical and/or experimental results are available.     

Sensitivity-enhanced IPAP-SOFAST-HMQC for fast-pulsing 2D NMR with reduced radiofrequency load

The SOFAST-HMQC experiment [P. Schanda, B. Brutscher, Very fast two-dimensional NMR spectroscopy for real-time investigation of dynamic events in proteins on the time scale of seconds, J. Am. Chem. Soc. 127 (2005) 8014-8015] allows recording two-dimensional correlation spectra of macromolecules such as proteins in only a few seconds acquisition time. To achieve the highest possible sensitivity, SOFAST-HMQC experiments are preferably performed on high-field NMR spectrometers equipped with cryogenically cooled probes. The duty cycle of over 80% in fast-pulsing SOFAST-HMQC experiments, however, may cause problems when using a cryogenic probe. Here we introduce SE-IPAP-SOFAST-HMQC, a new pulse sequence that provides comparable sensitivity to standard SOFAST-HMQC, while avoiding heteronuclear decoupling during (1)H detection, and thus significantly reducing the radiofrequency load of the probe during the experiment. The experiment is also attractive for fast and sensitive measurement of heteronuclear one-bond spin coupling constants.     

Exploring structure–selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping

We design and analyze compound selectivity sets of antagonists with differential selectivity against seven biogenic amine G-protein coupled receptors. The selectivity sets consist of a total of 267 antagonists and contain a spectrum of in part closely related molecular scaffolds. Each set represents a different selectivity profile. Using these com- pound sets, a systematic computational analysis of structure-selectivity relationships is carried out with different 2D similarity methods including fingerprints, recursive partitioning, clustering, and dynamic compound mapping. Screening calculations are performed in a background database containing nearly four million molecules. Fingerprint searching and compound mapping are found to enrich target-selective antagonists over family-selective ones. Dynamic compound mapping effectively discriminates database compounds from GPCR antagonists and consistently retains target-selective antagonists during the final dimension extension levels. Furthermore, the widely used MACCS key fingerprint displays a strong tendency to distinguish between target- and family-selective GPCR antagonists. Taken together, the results indicate that different types of 2D similarity methods are capable of distinguishing closely related molecules having different selectivity. The reported compound benchmark system is made freely available in order to enable selectivity-oriented analyses using other computational approaches.     

Feature vector clustering molecular pairs in computer simulations

A clustering framework is introduced to analyze the microscopic structural organization of molecular pairs in liquids and solutions. A molecular pair is represented by a representative vector (RV). To obtain RV, intermolecular atom distances in the pair are extracted from simulation trajectory as components of the key feature vector (KFV). A specific scheme is then suggested to transform KFV to RV by removing the influence of permutational molecular symmetry on the KFV as the predicted clusters should be independent of possible permutations of identical atoms in the pair. After RVs of pairs are obtained, a clustering analysis technique is finally used to classify all the RVs of molecular pairs into the clusters. The framework is applied to analyze trajectory from molecular dynamics simulations of an ionic liquid (trihexyltetradecylphosphonium bis(oxalato)borate ([P_6,6,6,14][BOB])). The molecular pairs are successfully categorized into physically meaningful clusters, and their effectiveness is evaluated by computing the product moment correlation coefficient (PMCC). (Willett, Winterman, and Bawden, J. Chem. Inf. Comput. Sci. 1986, 26, 109–118; Downs, Willett, and Fisanick, J. Chem. Inf. Comput. Sci. 1994, 34, 1094–1102) It is observed that representative configurations of two clusters are related to two energy local minimum structures optimized by density functional theory (DFT) calculation, respectively. Several widely used clustering analysis techniques of both nonhierarchical (k-means) and hierarchical clustering algorithms are also evaluated and compared with each other. The proposed KFV technique efficiently reveals local molecular pair structures in the simulated complex liquid. It is a method, which is highly useful for liquids and solutions in particular with strong intermolecular interactions. © 2019 Wiley Periodicals, Inc.     

基于QSAR模型的有机磷化合物毒性预测

随着有机磷化合物(OPs)的广泛应用,其在越来越多的环境介质中被检测出来。大多数OPs具有毒性,但人们缺乏快速且有效的预测手段来对毒性进行评估。本文将结合E-Dragon软件计算的分子描述符,采用不同的QSAR模型对36个OPs的毒性进行预测。文中采用后退法作为描述符筛选方法,以均方根误差(RMSE)作为评价标准,共找到14个对线性核函数支持向量机(SVM)模型贡献较大的描述符;在最终得到的SVM模型交叉验证结果中,计算值与实际值的相关系数为0. 913,均方根误差为0. 388;外部测试验证结果中,平均相对误差为9. 10%。此外,采用多元线性回归(MLR)、人工神经网络(ANN)以及偏最小二乘回归(PLS)模型对OPs的毒性进行预测,交叉验证结果显示,三个模型的计算值与实际值的相关系数分别为0. 878、0. 686与0. 620,没有SVM模型的预测能力好。因此采用线性核函数的SVM模型对OPs进行毒性预测是一个行之有效的方法。     

GPR40 受体苯丙酸类激动剂三维定量构效关系研究

苯丙酸类化合物是G蛋白偶联受体40(GPR40)潜在的生物活性药物。本文基于比较分子力场分析法(Co MFA)和比较分子相似性指数分析法(CoMSIA),分别建立了40个已知活性的GPR40受体苯丙酸类激动剂的三维定量构效关系(3D-QSAR)模型,研究该类激动剂与生物活性之间的关系。CoMFA和CoMSIA模型的交叉验证系数(q~2)分别为0. 527和0. 500,拟合验证系数(r~2)分别为0. 901和0. 860,两个3D-QSAR模型预测值与实验值基本一致,表明模型具有良好的可信度和预测能力。根据两个3D-QSAR模型提供的立体场、静电场、疏水场、氢键供体场和氢键受体场所提供的信息提出优化该类抑制剂结构的药物设计思路,为指导设计更高活性的GPR40激动剂以及GRR40新分子激动活性的预测提供理论依据。     

Recent advances in photofunctional polymorphs of molecular materials

Recently, molecule-based luminescent materials have been drawing extensive attention due to their desirable properties and promising applications in the fields of sensors, lighting display, and cell imaging. Crystalline polymorph is an intriguing phenomenon that the presence of multiple packing and aggregate architectures of the same molecular system. The studies on polymorphs for molecule-based fluorophores provide the opportunities to adjust the mode of molecular packing and photophysical properties, which will help to illustrate the structure-property relationship. In this review, we focus on the recent progress in various feasible methods of molecule-based crystalline polymorphism growth and their adjustable photofunctional properties, which will open up possibilities of variant optical applications. Firstly, several effective ways to prepare and screen polymorphs are sorted out. And then, we discuss the discrepant properties and multifunctional applications (such as sensors, laser, and OFET, etc.). Finally, the development trends and future prospects of these polymorphs are also briefly introduced.     

“对比实验+现象驱动的知识挖掘”在高分子材料合成创新实验教学中的尝试与运用

高分子材料合成实验是高分子专业的核心实验课程之一,如何建立实验课程与理论课程的有效衔接,加深学生对所学知识点的深入理解和活学活用,是本课程的主要目标。为此,笔者和教学团队提出：在同一个实验中改变某个实验变量,在教学班中引入对比实验,通过这些改变引起的不同实验现象和由此产生的材料性能的迥然差异,引导学生从现象出发,联系理论课堂知识进行深入分析。这种尝试有效改变了以往实验教学中所有教学班千篇一律的操作和几乎完全相同的实验现象与结果,有效激发了学生的主动性,提高了其分析问题、解决问题的能力。2年的实验教学反馈表明,“对比实验的引入”和“现象驱动的知识挖掘”帮助学生有效巩固了理论课堂的知识点,做到了活学活用。