Ligand- and Structure-Based Approaches of Escherichia coli FabI Inhibition by Triclosan Derivatives: From Chemical Similarity to Protein Dynamics Influence

Enoyl-acyl carrier protein reductase (FabI) is the limiting step to complete the elongation cycle in type II fatty acid synthase (FAS) systems and is a relevant target for antibacterial drugs. E. coli FabI has been employed as a model to develop new inhibitors against FAS, especially triclosan and diphenyl ether derivatives. Chemical similarity models (CSM) were used to understand which features were relevant for FabI inhibition. Exhaustive screening of different CSM parameter combinations featured chemical groups, such as the hydroxy group, as relevant to distinguish between active/decoy compounds. Those chemical features can interact with the catalytic Tyr156. Further molecular dynamics simulation of FabI revealed the ionization state as a relevant for ligand stability. Also, our models point the balance between potency and the occupancy of the hydrophobic pocket. This work discusses the strengths and weak points of each technique, highlighting the importance of complementarity among approaches to elucidate EcFabI inhibitor's binding mode and offers insights for future drug discovery.     

High-Precision Automatic Identification of Fentanyl-Related Drugs by Terahertz Spectroscopy with Molecular Dynamics Simulation and Spectral Similarity Mapping

Fentanyl is a potent opioid analgesic with high bioavailability. It is the leading cause of drug addiction and overdose death. To better control the abuse of fentanyl and its derivatives, it is crucial to develop rapid and sensitive detection methods. However, fentanyl-related substrates undergo similar molecular structures resulting in similar properties, which are difficult to be identified by conventional spectroscopic methods. In this work, a method for the automatic identification of 8 fentanyl-related substances with similar spectral characteristics was developed using terahertz (THz) spectroscopy coupled with density functional theory (DFT) and spectral similarity mapping (SSM). To characterize the THz fingerprints of these fentanyl-related samples more accurately, the method of baseline estimation and denoising with sparsity was performed before revealing the unique molecular dynamics of each substance by DFT. The SSM method was proposed to identify these fentanyl analogs based on weighted spectral cosine–cross similarity and fingerprint discrete Fréchet distance, generating a matching list by stepwise searching the entire spectral database. The top matched list returned the identification results of the target fentanyl analogs with accuracies of 94.48~99.33%. Results from this work provide algorithms’ increased reliability, which serves as an artificial intelligence-based tool for high-precision fentanyl analysis in real-world samples.     

Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology

Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and recently adapted to the graph convolutional network (GCN), is inherently a 2D representation of 3D molecules. Herein we propose an advanced version of the GCN, called 3DGCN, which receives 3D molecular information from a molecular graph augmented by information on bond direction. While outperforming state-of-the-art deep-learning models in the prediction of chemical and biological properties, 3DGCN has the ability to both generalize and distinguish molecular rotations in 3D, beyond 2D, which has great impact on drug discovery and development, not to mention the design of chemical reactions.     

Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.     

生物降解高聚物的分子设计

介绍生物降解高聚物的化学与物理改性方法，对生物降解高聚物的应用及前景作了较为全面的论述。     

Molecular Iodine/Cyclophosphamide Synergism on Chemoresistant Neuroblastoma Models

Neuroblastoma (Nb), the most common extracranial tumor in children, exhibited remarkable phenotypic diversity and heterogeneous clinical behavior. Tumors with MYCN overexpression have a worse prognosis. MYCN promotes tumor progression by inducing cell proliferation, de-differentiation, and dysregulated mitochondrial metabolism. Cyclophosphamide (CFF) at minimum effective oral doses (metronomic therapy) exerts beneficial actions on chemoresistant cancers. Molecular iodine (I₂) in coadministration with all-trans retinoic acid synergizes apoptosis and cell differentiation in Nb cells. This work analyzes the impact of I₂ and CFF on the viability (culture) and tumor progression (xenografts) of Nb chemoresistant SK-N-BE(2) cells. Results showed that both molecules induce dose-response antiproliferative effects, and I₂ increases the sensibility of Nb cells to CFF, triggering PPARγ expression and acting as a mitocan in mitochondrial metabolism. In vivo oral I₂/metronomic CFF treatments showed significant inhibition in xenograft growth, decreasing proliferation (Survivin) and activating apoptosis signaling (P53, Bax/Bcl-2). In addition, I₂ decreased the expression of master markers of malignancy (MYCN, TrkB), vasculature remodeling, and increased differentiation signaling (PPARγ and TrkA). Furthermore, I₂ supplementation prevented loss of body weight and hemorrhagic cystitis secondary to CFF in nude mice. These results allow us to propose the I₂ supplement in metronomic CFF treatments to increase the effectiveness of chemotherapy and reduce side effects.     

Identification of Corosolic and Oleanolic Acids as Molecules Antagonizing the Human RORγT Nuclear Receptor Using the Calculated Fingerprints of the Molecular Similarity

RORγT is a protein product of the RORC gene belonging to the nuclear receptor subfamily of retinoic-acid-receptor-related orphan receptors (RORs). RORγT is preferentially expressed in Th17 lymphocytes and drives their differentiation from naive CD4+ cells and is involved in the regulation of the expression of numerous Th17-specific cytokines, such as IL-17. Because Th17 cells are implicated in the pathology of autoimmune diseases (e.g., psoriasis, inflammatory bowel disease, multiple sclerosis), RORγT, whose activity is regulated by ligands, has been recognized as a drug target in potential therapies against these diseases. The identification of such ligands is time-consuming and usually requires the screening of chemical libraries. Herein, using a Tanimoto similarity search, we found corosolic acid and other pentacyclic tritepenes in the library we previously screened as compounds highly similar to the RORγT inverse agonist ursolic acid. Furthermore, using gene reporter assays and Th17 lymphocytes, we distinguished compounds that exert stronger biological effects (ursolic, corosolic, and oleanolic acid) from those that are ineffective (asiatic and maslinic acids), providing evidence that such combinatorial methodology (in silico and experimental) might help wet screenings to achieve more accurate results, eliminating false negatives.     

Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics

Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.     

Similarity Solution in Chemical Engineering Guided by Dimensional Analysis

Similarity solution is a classical topic in chemical engineering,frequently encountered in analysis of flow and transport phenomena in semi-infinite domains.In this article,dimensional analysis is applied to resolve systematically the conditions for the existence of similarity solutions and formulate a basic procedure to get such solutions.With several classic examples,the method for finding the suitable combination of independent variables from original ones is demonstrated,so that the original partial differential equation can be transformed into a simpler ordinary differential equation,through which the desired similarity solution is finally achieved.In-depth analysis of one-dimensional diffusion/reaction problems by dimensional analysis results in some new insights.The elaboration is significant for deep insight of similarity solution and its application in chemical engineering.     

基于极限学习机的汽轮机故障诊断

针对汽轮机故障诊断中出现的多故障识别问题,为有效提高汽轮机多故障诊断的准确率,提出了基于极限学习机的汽轮机故障诊断方法.极限学习机算法在训练时只需设置隐含层神经元个数,从而解决了如神经网络及支持向量机等多参数选取困难的问题,有效地提高了学习机的训练速度.在确定了最优参数的基础上,将极限学习机应用于汽轮机故障诊断模型中,并将极限学习机的故障诊断结果与支持向量机的诊断结果进行对比.结果表明：基于极限学习机的多故障诊断速度及准确率均明显优于支持向量机的诊断结果,对汽轮机故障诊断的实践有非常显著的指导作用.     

基于C的化工基础数据识别算法

提出了化工诺模图类数据计算机识别的一种新算法,利用C语言,通过在气体、液体粘度的实现,说明该算法的可靠性,可望为此类化工经验图的数据库化提供一种途径。     

分子电性作用矢量用于合成产率预测

应用分子电性作用矢量(molecular electronegativity interaction vector,MEIV)建立了42种Wittig反应产率的定量构效关系模型,考察了反应物分子结构对合成产率的内在影响。结合多元线性回归、逐步回归变量筛选方法和QSAR建模技术,得到7参数QSAR模型,交互校验的复相关系数分别为0.985、0.973;选用外部集对模型健壮性、预测能力进行考察,亦获得良好结果:R=0.984、R=0.965。模型稳定性良好,预测能力颇强。     

Molecular Fingerprint Recombination: Generating Hybrid Fingerprints for Similarity Searching from Different Fingerprint Types

Molecular fingerprints have a long history in computational medicinal chemistry and continue to be popular tools for similarity searching. Over the years, a variety of fingerprint types have been introduced. We report an approach to identify preferred bit subsets in fingerprints of different design and “recombine” these bit segments into “hybrid fingerprints”. These compound class‐directed fingerprint representations are found to increase the similarity search performance of their parental fingerprints, which can be rationalized by the often complementary nature of distinct fingerprint features.     

Correlation between Protein Sequence Similarity and Crystallization Reagents in the Biological Macromolecule Crystallization Database

The protein structural entries grew far slower than the sequence entries. This is partly due to the bottleneck in obtaining diffraction quality protein crystals for structural determination using X-ray crystallography. The first step to achieve protein crystallization is to find out suitable chemical reagents. However, it is not an easy task. Exhausting trial and error tests of numerous combinations of different reagents mixed with the protein solution are usually necessary to screen out the pursuing crystallization conditions. Therefore, any attempts to help find suitable reagents for protein crystallization are helpful. In this paper, an analysis of the relationship between the protein sequence similarity and the crystallization reagents according to the information from the existing databases is presented. We extracted information of reagents and sequences from the Biological Macromolecule Crystallization Database (BMCD) and the Protein Data Bank (PDB) database, classified the proteins into different clusters according to the sequence similarity, and statistically analyzed the relationship between the sequence similarity and the crystallization reagents. The results showed that there is a pronounced positive correlation between them. Therefore, according to the correlation, prediction of feasible chemical reagents that are suitable to be used in crystallization screens for a specific protein is possible.     

基于物联感知的空气质量监测系统

针对目前中小城市和城乡郊区空气质量监测网络不健全的现状,基于物联感知技术、嵌入式技术和Android技术设计了一套空气质量实时监测系统。该系统以32位嵌入式单片机STM32为控制核心,选用高精度传感器模组完成对PM2.5、PM10、SO_2、CO、O_3及NO_2等空气质量指标的采集;搭建远程服务器完成数据存储,并利用ArcGIS服务器提供地图服务,采用Android客户端和微信公众号完成数据的显示与管理,具有较好的可靠性、实时性和实用性。