Fast Multi‐residue Screening for 84 Pesticides in Tea by Gas Chromatography with Dual‐tower Auto‐sampler,Dual‐column and Dual Detectors

A fast multi‐residue screening method for determining pesticides in tea is described. Pesticides are extracted from tea with acetone and methylene chloride, then enriched and cleaned up with solid phase extraction (SPE) prior to gas chromatographic determination. The fast screening is achieved by a gas chromatograph system equipped with dual‐column, dual‐tower auto‐sampler and both electron capture detector (ECD) and flame photometric detector (FPD). Optimal conditions are investigated for the prospective pesticides including column selection, detection mode, the retention behaviors, quantitative calibration, as well as the recoveries and repeatability of pesticides from tea samples. Under the optimal conditions, with the FPD‐P detector accompanied CP‐SIL 13CB column, 48 pesticides can be separated well and detected within 38 min; and with a DB‐5 column, 35 ECD‐detectable pesticides can be separated and detected within 46 min. The recoveries of 84 pesticides in tea samples are 65–120% with 0.34–16% RSD for spiking 0.02–3.0 mg/kg standard species. Because of the thermal instability of most pesticides, direct cold extraction of pesticides from a tea sample is recommended. The proposed method provided a very fast and efficient procedure to screen 84 pesticides from a complicated tea sample matrix.     

Vibrational spectroscopic encoding of polystyrene-based resin beads: converting the encoding peaks into barcodes

A detailed approach is described for the vibrational spectroscopic encoding of polystyrene-based resin beads by converting the infrared absorption peaks suitable for encoding (encoding peaks) into barcodes. Based on combining the FT-IR measurements and the quantum-chemical computations, the vibrational characteristics of p-tert-butylstyrene monomer, polystyrene and poly(p-tert-butylstyrene) resin beads are analyzed, which are helpful for the selection of encoding peaks. The vibrational spectroscopic encoding of polystyrene-based resin beads could be obtained by converting the wavenumber, intensity and full width at half maximum (FWHM) of the encoding peaks into barcodes automatically through a computer program designed in our laboratory.     

Computational combinatorial ligand design: Application to human α-thrombin

Summary A new method is presented for computer-aided ligand design by combinatorial selection of fragments that bind favorably to a macromolecular target of known three-dimensional structure. Firstly, the multiple-copy simultaneous-search procedure (MCSS) is used to exhaustively search for optimal positions and orientations of functional groups on the surface of the macromolecule (enzyme or receptor fragment). The MCSS minima are then sorted according to an approximated binding free energy, whose solvation component is expressed as a sum of separate electrostatic and nonpolar contributions. The electrostatic solvation energy is calculated by the numerical solution of the linearized Poisson-Boltzmann equation, while the nonpolar contribution to the binding free energy is assumed to be proportional to the loss in solvent-accessible surface area. The program developed for computational combinatorial ligand design (CCLD) allows the fast and automatic generation of a multitude of highly diverse compounds, by connecting in a combinatorial fashion the functional groups in their minimized positions. The fragments are linked as two atoms may be either fused, or connected by a covalent bond or a small linker unit. To avoid the combinatorial explosion problem, pruning of the growing ligand is performed according to the average value of the approximated binding free energy of its fragments. The method is illustrated here by constructing candidate ligands for the active site of human -thrombin. The MCSS minima with favorable binding free energy reproduce the interaction patterns of known inhibitors. Starting from these fragments, CCLD generates a set of compounds that are closely related to high-affinity thrombin inhibitors. In addition, putative ligands with novel binding motifs are suggested. Probable implications of the MCSS-CCLD approach for the evolving scenario of drug discovery are discussed.     

Virtual Screening and Hit Selection of Natural Compounds as Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) is one of the classical targets in the treatment of Alzheimer’s disease (AD). Inhibition of AChE slows down the hydrolysis of acetycholine and increases choline levels, improving the cognitive function. The achieved success of plant-based natural drugs acting as AChE inhibitors, such as galantamine (GAL) from Galanthus genus and huperzine A from Huperzia serrate (approved drug in China), in the treatment of AD, and the fact that natural compounds (NCs) are considered as safer and less toxic compared to synthetic drugs, led us to screen the available NCs (almost 150,000) in the ZINC12 database for AChE inhibitory activity. The compounds were screened virtually by molecular docking, filtered for suitable ADME properties, and 32 ligands from 23 structural groups were selected. The stability of the complexes was estimated via 1 μs molecular dynamics simulation. Ten compounds formed stable complexes with the enzyme and had a vendor and a reasonable price per mg. They were tested for AChE inhibitory and antioxidant activity. Five compounds showed weak AChE inhibition and three of them exhibited high antioxidant activity.     

New Chemotypes for the Inhibition of (p)ppGpp Synthesis in the Quest for New Antimicrobial Compounds

Antimicrobial resistance (AMR) poses a serious threat to our society from both the medical and economic point of view, while the antibiotic discovery pipeline has been dwindling over the last decades. Targeting non-essential bacterial pathways, such as those leading to antibiotic persistence, a bacterial bet-hedging strategy, will lead to new molecular entities displaying low selective pressure, thereby reducing the insurgence of AMR. Here, we describe a way to target (p)ppGpp (guanosine tetra- or penta-phosphate) signaling, a non-essential pathway involved in the formation of persisters, with a structure-based approach. A superfamily of enzymes called RSH (RelA/SpoT Homolog) regulates the intracellular levels of this alarmone. We virtually screened several fragment libraries against the (p)ppGpp synthetase domain of our RSH chosen model Rel_Seq, selected three main chemotypes, and measured their interaction with Rel_Seq by thermal shift assay and STD-NMR. Most of the tested fragments are selective for the synthetase domain, allowing us to select the aminobenzoic acid scaffold as a hit for lead development.     

Production and Purification of Pectinase from Bacillus subtilis 15A-B92 and Its Biotechnological Applications

Enzymes that degrade pectin are called pectinases. Pectinases of microbial origin are used in juice clarification as the process is cost-effective. This study screened a pectinase-producing bacterium isolated from soil and identified as Bacillus subtilis 15A B-92 based on the 16S rRNA molecular technique. The purified pectinase from the isolate showed 99.6 U/mg specific activity and 11.6-fold purity. The molecular weight of the purified bacterial pectinase was 14.41 ± 1 kD. Optimum pectinase activity was found at pH 4.5 and 50 °C, and the enzyme was 100% stable for 3.5 h in these conditions. No enzymatic inhibition or activation effect was seen with Fe²⁺, Ca²⁺, or Mg²⁺. However, a slight inhibition was seen with Cu²⁺, Mn²⁺, and Zn²⁺. Tween 20 and 80 slightly inhibited the pectinase, whereas iodoacetic acid (IAA), ethylenediaminetetraacetate (EDTA), urea, and sodium dodecyl sulfate (SDS) showed potent inhibition. The bacterial pectinase degraded citrus pectin (100%); however, it was inactive in the presence of galactose. With citrus pectin as the substrate, the Km and Vmax were calculated as 1.72 mg/mL and 1609 U/g, respectively. The high affinity of pectinase for its substrate makes the process cost-effective when utilized in food industries. The obtained pectinase was able to clarify orange and apple juices, justifying its application in the food industry.     

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

In Silico Drug Repurposing of FDA-Approved Drugs Highlighting Promacta as a Potential Inhibitor of H7N9 Influenza Virus

Influenza virus infections continue to be a significant and recurrent public health problem. Although vaccine efficacy varies, regular immunisation is the most effective method for suppressing the influenza virus. Antiviral drugs are available for influenza, although two of the four FDA-approved antiviral treatments have resulted in significant drug resistance. Therefore, new treatments are being sought to reduce the burden of flu-related illness. The time-consuming development of treatments for new and re-emerging diseases such as influenza and the high failure rate are increasing concerns. In this context, we used an in silico-based drug repurposing method to repurpose FDA-approved drugs as potential therapies against the H7N9 virus. To find potential inhibitors, a total of 2568 drugs were screened. Promacta, tucatinib, and lurasidone were identified as promising hits in the DrugBank database. According to the calculations of MM-GBSA, tucatinib (−54.11 kcal/mol) and Promacta (−56.20 kcal/mol) occupied the active site of neuraminidase with a higher binding affinity than the standard drug peramivir (−49.09 kcal/mol). Molecular dynamics (MD) simulation studies showed that the C-α atom backbones of the complexes of tucatinib and Promacta neuraminidase were stable throughout the simulation period. According to ADME analysis, the hit compounds have a high gastrointestinal absorption (GI) and do not exhibit properties that allow them to cross the blood–brain barrier (BBB). According to the in silico toxicity prediction, Promacta is not cardiotoxic, while lurasidone and tucatinib show only weak inhibition. Therefore, we propose to test these compounds experimentally against the influenza H7N9 virus. The investigation and validation of these potential H7N9 inhibitors would be beneficial in order to bring these compounds into clinical settings.     

基于微流控技术的细胞水平高通量药物筛选系统的研究进展

药物筛选是新药研发的关键步骤,创新药物的发现需要采用适当的药物作用靶点对大量化合物样品进行筛选.高通量筛选系统能够实现数千个反应同时测试和分析,大大提高了药物筛选的实验规模和效率.其中基于细胞水平的高通量药物筛选系统因为更加接近人体生理条件,成为主要的筛选模式.而目前发展成熟的高通量细胞筛选系统主要基于多孔板,存在细胞...