Glycopolymeric inhibitors of β-glucuronidase. II. Synthesis of glycopolymers containing pendant L-gulonic moieties and effects of the carboxyl group in the glycopolymers upon the activity of β-glucuronidase

A new styrene derivative having an L -gulonic moiety, N-(p-vinylbenzyl)-6-L -gulonamide (VB-6-Glco, 3) was synthesized from L -gulono-1,4-lactone and p-vinylbenzylamine. The styrene derivative (3) was subjected to the radical homopolymerization and copolymerization with acrylamide and acrylic acid. The hydrolysis of p-nitrophenyl β-D -glucuronide with β-glucuronidase was scarcely inhibited in the presence of the glycopolymer prepared from 3 and acrylamide (P(VB-6-Glco-co-AAm), 4), whereas the glycopolymer prepared from 3 and acrylic acid (P(VB-6-Glco-co-AAK), 5) was found to suppress the enzyme activity strongly as well as a polystyrene derivative having pendant D -glucaric moieties (P(VB-6-GlcaH-co-AAm), 6). From the kinetic analysis of the hydrolysis, the glycopolymers 6 and 5 were postulated to inhibit the enzyme competitively and uncompetitively, respectively. © 1999 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 37: 2773–2779, 1999     

Discrete filters for large eddy simulation

This paper summarizes several results relative to discrete filters for subgrid‐scale (SGS) models based on a multi‐level filtering procedure. First, a theoretical study of discrete filters in physical space is performed. The analysis is done in the uniform one‐dimensional case, and is then extended to the general multi‐dimensional case for arbitrary structured and unstructured meshes. Some equivalence classes for the discrete filters are defined, based either on a differential approximation or the associated transfer function. Methods for the definition of discrete filters are proposed in the general case, including the approximation of continuous convolution filters. Second, the sensitivity of several SGS models with respect to the test filter is investigated. The selected models are: the dynamic Smagorinsky model, the mixed scale model (MSM), the selective MSM and the Liu–Meneveau–Katz (LMK) similarity model. Improved versions, which explicitly account for the spectral width of the test filter of the MSM and the LMK similarity model are proposed. The analysis, which reveals a significant influence of the test filter, is done through a priori testing on a 128³ field issued from the large eddy simulation (LES) of freely decaying homogeneous isotropic turbulence. Copyright © 1999 John Wiley & Sons, Ltd.     

Natural Xanthine Oxidase Inhibitor 5-O-Caffeoylshikimic Acid Ameliorates Kidney Injury Caused by Hyperuricemia in Mice

Xanthine oxidase (XOD) inhibition has long been considered an effective anti-hyperuricemia strategy. To identify effective natural XOD inhibitors with little side effects, we performed a XOD inhibitory assay-coupled isolation of compounds from Smilacis Glabrae Rhizoma (SGR), a traditional Chinese medicine frequently prescribed as anti-hyperuricemia agent for centuries. Through the in vitro XOD inhibitory assay, we obtained a novel XOD inhibitor, 5-O-caffeoylshikimic acid (#1, 5OCSA) with IC₅₀ of 13.96 μM, as well as two known XOD inhibitors, quercetin (#3) and astilbin (#6). Meanwhile, we performed in silico molecular docking and found 5OCSA could interact with the active sites of XOD (PDB ID: 3NVY) with a binding energy of −8.6 kcal/mol, suggesting 5OCSA inhibits XOD by binding with its active site. To evaluate the in vivo effects on XOD, we generated a hyperuricemia mice model by intraperitoneal injection of potassium oxonate (300 mg/kg) and oral gavage of hypoxanthine (500 mg/kg) for 7 days. 5OCSA could inhibit both hepatic and serum XOD in vivo, together with an improvement of histological and multiple serological parameters in kidney injury and HUA. Collectively, our results suggested that 5OCSA may be developed into a safe and effective XOD inhibitor based on in vitro, in silico and in vivo evidence.     

Computational Simulation of HIV Protease Inhibitors to the Main Protease (Mpro) of SARS-CoV-2: Implications for COVID-19 Drugs Design

SARS-CoV-2 is highly homologous to SARS-CoV. To date, the main protease (Mpro) of SARS-CoV-2 is regarded as an important drug target for the treatment of Coronavirus Disease 2019 (COVID-19). Some experiments confirmed that several HIV protease inhibitors present the inhibitory effects on the replication of SARS-CoV-2 by inhibiting Mpro. However, the mechanism of action has still not been studied very clearly. In this work, the interaction mechanism of four HIV protease inhibitors Darunavir (DRV), Lopinavir (LPV), Nelfinavir (NFV), and Ritonavire (RTV) targeting SARS-CoV-2 Mpro was explored by applying docking, molecular dynamics (MD) simulations, and MM–GBSA methods using the broad-spectrum antiviral drug Ribavirin (RBV) as the negative and nonspecific control. Our results revealed that LPV, RTV, and NFV have higher binding affinities with Mpro, and they all interact with catalytic residues His41 and the other two key amino acids Met49 and Met165. Pharmacophore model analysis further revealed that the aromatic ring, hydrogen bond donor, and hydrophobic group are the essential infrastructure of Mpro inhibitors. Overall, this study applied computational simulation methods to study the interaction mechanism of HIV-1 protease inhibitors with SARS-CoV-2 Mpro, and the findings provide useful insights for the development of novel anti-SARS-CoV-2 agents for the treatment of COVID-19.     

Design,Synthesis, Biological Evaluation,and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q² = 0.797 and r² = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q² = 0.567 and r² = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).     

Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling

Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton’s tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets.     

掺杂对铒、钕蒽基配合物的光二聚反应及光磁性能调控的影响

选择具有光化学活性的蒽甲基膦酸二乙酯(depma)为配体、部分镧系离子为金属中心构筑了3例同构的单核配合物Ln (SCN)₂(NO₃)(depma)₂(4-hpy)₂(Ln=Er (1Er),Nd (2Nd),Y (3Y)),对其单分子磁体行为、发光性质和光二聚行为等进行了研究。荧光测试显示1Er和2Nd因配体到稀土离子的能量转移而显示近红外发光,但在可见光区无明显发光行为,同时不能发生光二聚反应。而3Y可以发射黄绿色荧光,并发生光二聚反应生成[Y (SCN)₂(NO₃)(depma₂)(4-hpy)₂]_n(3Y-UV)。磁性测试显示1Er和2Nd在低温下均表现出场诱导的慢磁弛豫性质,其中2Nd的弛豫过程主要由拉曼过程主导。通过将Er^Ⅲ或Nd^Ⅲ掺杂到3Y晶格中,得到同构配合物Ln_0.1Y_0.9(SCN)₂(NO₃)(depma)₂(4-hpy)₂(Ln=Er (1Er@Y),Nd (2Nd@Y)),测试结果表明掺杂样品可以发生部分光二聚行为,同时伴随发光颜色的变化。此外,化合物2Nd@Y光反应前后磁性也发生了变化,拉曼过程的指数从3.8(2Nd@Y)提升到5.2(2Nd@Y-UV),对应于光照前后晶格声子结构的变化。     

用分子动力学模拟天然气水合物的抑制效应

用分子动力学模拟方法确定了结构H型(SH)天然气水合物的稳定晶体生长面为(001), 系统研究了277 K时三种动力学抑制剂对此晶面的影响. 模拟显示抑制剂中的氧与表面水分子形成氢键, 从而破坏原有的稳定结构, 造成水合物笼型结构坍塌, 达到抑制水合物形成的效果. 比较三种不同动力学抑制剂对SH的抑制效果得出: PVCap＞PEO＞PVP. 在此基础上研究了PVCap对天然气水合物结构I型(SI), 结构II型(SII)和SH三种不同晶型的抑制效应. 模拟发现抑制效果的次序为: SH＞SI＞SII.     

一种氨烷基膦酸型水处理剂的合成及缓蚀阻垢性能的研究

介绍了一种氨烷基膦酸型水处理剂二甲基氨基甲叉二膦酸(DMAMDP)的合成方法,并进行了缓蚀阻垢性能的研究。结果表明,DMAMDP在阻碳酸钙、硫酸钙、磷酸钙和稳定锌等方面,以及缓蚀性能均优于常用有机膦酸水处理剂HEDP和ATMP。