Targeting Acetylcholinesterase: Identification of Chemical Leads by High Throughput Screening, Structure Determination and Molecular Modeling

Acetylcholinesterase (AChE) is an essential enzyme that terminates cholinergic transmission by rapid hydrolysis of the neurotransmitter acetylcholine. Compounds inhibiting this enzyme can be used (inter alia) to treat cholinergic deficiencies (e.g. in Alzheimer's disease), but may also act as dangerous toxins (e.g. nerve agents such as sarin). Treatment of nerve agent poisoning involves use of antidotes, small molecules capable of reactivating AChE. We have screened a collection of organic molecules to assess their ability to inhibit the enzymatic activity of AChE, aiming to find lead compounds for further optimization leading to drugs with increased efficacy and/or decreased side effects. 124 inhibitors were discovered, with considerable chemical diversity regarding size, polarity, flexibility and charge distribution. An extensive structure determination campaign resulted in a set of crystal structures of protein-ligand complexes. Overall, the ligands have substantial interactions with the peripheral anionic site of AChE, and the majority form additional interactions with the catalytic site (CAS). Reproduction of the bioactive conformation of six of the ligands using molecular docking simulations required modification of the default parameter settings of the docking software. The results show that docking-assisted structure-based design of AChE inhibitors is challenging and requires crystallographic support to obtain reliable results, at least with currently available software. The complex formed between C5685 and Mus musculus AChE (C5685•mAChE) is a representative structure for the general binding mode of the determined structures. The CAS binding part of C5685 could not be structurally determined due to a disordered electron density map and the developed docking protocol was used to predict the binding modes of this part of the molecule. We believe that chemical modifications of our discovered inhibitors, biochemical and biophysical characterization, crystallography and computational chemistry provide a route to novel AChE inhibitors and reactivators

Автоматизированная система обеспечения оптимальных условий выращивания сельскохозяйственных культур в защищенном грунте

Mosquitoes of the Anopheles (An.) and Aedes (Ae.) genus are principal vectors of human diseases including malaria, dengue and yellow fever. Insecticide-based vector control is an established and important way of preventing transmission of such infections. Currently used insecticides can efficiently control mosquito populations, but there are growing concerns about emerging resistance, off-target toxicity and their ability to alter ecosystems. A potential target for the development of insecticides with reduced off-target toxicity is the cholinergic enzyme acetylcholinesterase (AChE). Herein, we report cloning, baculoviral expression and functional characterization of the wild-type AChE genes (ace-1) from An. gambiae and Ae. aegypti, including a naturally occurring insecticide-resistant (G119S) mutant of An. gambiae. Using enzymatic digestion and liquid chromatography-tandem mass spectrometry we found that the secreted proteins were post-translationally modified. The Michaelis-Menten constants and turnover numbers of the mosquito enzymes were lower than those of the orthologous AChEs from Mus musculus and Homo sapiens. We also found that the G119S substitution reduced the turnover rate of substrates and the potency of selected covalent inhibitors. Furthermore, non-covalent inhibitors were less sensitive to the G119S substitution and differentiate the mosquito enzymes from corresponding vertebrate enzymes. Our findings indicate that it may be possible to develop selective non-covalent inhibitors that effectively target both the wild-type and insecticide resistant mutants of mosquito AChE

Совершенствование учебного процесса на военно-медицинском факультете в Белорусском государственном медицинском университете

ОБРАЗОВАНИЕ МЕДИЦИНСКОЕМЕДИЦИНСКИЕ УЧЕБНЫЕ ЗАВЕДЕНИЯВУЗЫУЧЕБНЫЙ ПРОЦЕССВОЕННО-МЕДИЦИНСКИЙ ФАКУЛЬТЕТБГМ

Некоторые аспекты эффективности обучения при переходе от очной формы обучения к дистанционной

ВЫСШЕЕ МЕДИЦИНСКОЕ ОБРАЗОВАНИЕОБРАЗОВАНИЕ МЕДИЦИНСКОЕ /МЕТОДЫОЧНАЯ ФОРМА ОБУЧЕНИЯЗАОЧНАЯ ФОРМА ОБУЧЕНИЯДИСТАНЦИОННОЕ ОБУЧЕНИЕ /МЕТОДЫОБРАЗОВАТЕЛЬНЫЙ ПРОЦЕССХИМИЯ (ДИСЦИПЛИНА

Changes in plant community diversity and management effects in semi-natural meadows in southern Sweden

The objectives of this thesis were to: 1) survey the vegetation in semi-natural meadows in south-central Sweden, 2) discern meadow vegetation changes in eight semi-permanent plots between studies performed in the 1960s and in 1990, 3) experimentally investigate the effects of variations in management intensity in one dry and one mesic meadow, 4) experimentally investigate the effects of different management practices or absence of management on species dynamics in meadow vegetation. 1. In the survey of meadows nine plant communities were recognised, ranging from wet to dry. The most obvious difference between investigated years was the decrease in plant community diversity. Thus two wet-moist plant communities found in the earlier study were missing in 1990, and one had more or less disappeared. 2. The comparison of semi-permanent plots studied in the 1960s and again in 1990 revealed changes in the vegetation within the plant communities. These were, e.g. increased cover of vascular plants, increased cover and number of graminoids, and increased cover of species with primary habitats others than grassland as well as decreased cover of species supposed to be favoured by mowing. Furthermore, considerable species dynamics were found. 3. Dry meadow vegetation was more prone to changes than mesic meadow vegetation, both in plots where the present management was simulated, as well as in totally unmanaged plots. Both raking and grazing had positive effects on species abundance in the mesic meadow. In the dry meadow raking had both positive and negative effects on species abundance, whereas grazing had almost only negative effects. When management was abandoned species richness declined, more so in the mesic meadow were several species disappeared already after one year of abandonment. 4. The mesic meadow was highly dynamic at the smallest scale studied (0.01 m2), e.g. the vegetation turnover index was twice as high at the 0.01-m2 scale as at the larger scales. Species dynamics increased if management was intensified, as well as when it was abandoned. However, in the most intensively managed plots, the increased species dynamics was due to the appearance of species, whereas in unmanaged plots it was due to the disappearance of species

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure–retention relationship (QSRR) approaches for prediction of retention times (1tR and 2tR) and indices (linear retention indices (LRIs) and a new polyethylene glycol–based retention index (PEG-2I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a “federation of local models,” was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the 1tR and LRI, and 5% and 12% for the 2tR and PEG-2I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-2I

Probing Molecular Interactions within Class II MHC A(q)/Glycopeptide/T-Cell Receptor Complexes Associated with Collagen-Induced Arthritis.

T cells obtained in a mouse model for rheumatoid arthritis are activated by a glycopeptide fragment from rat type II collagen (CII) bound to the class II major histocompatibility complex A(q) molecule. We report a comparative model of A(q) in complex with the glycopeptide CII260-267. This model was used in a structure-based design approach where the amide bond between Ala(261) and Gly(262) in the glycopeptide was selected for replacement with psi[COCH2], psi[CH2NH2+], and psi[(E)-CH=CH] isosteres. Ala-Gly isostere building blocks were then synthesized and introduced in CII260-267 and CII259-273 glycopeptides. The modified glycopeptides were evaluated for binding to the A(q) molecule, and the results were interpreted in view of the A(q)/glycopeptide model. Moreover, recognition by a panel of T-cell hybridomas revealed high sensitivity for the backbone modifications. These studies contribute to the understanding of the interactions in the ternary A(q)/glycopeptide/T-cell receptor complexes that activate T cells in autoimmune arthritis and suggest possibilities for new vaccination approaches

Quantitative Structure-Activity Relationship of Peptides Binding to the Class II Major Histocompatibility Complex Molecule A(q) Associated with Autoimmune Arthritis.

Presentation of (glyco)peptides by the class II major histocompatibility complex molecule Aq to T cells plays a central role in collagen-induced arthritis, an animal model for the autoimmune disease rheumatoid arthritis. A peptide library was designed using statistical molecular design in amino acid space in which five positions in the minimal mouse collagen type II binding epitope CII260-267 were varied. A substantially reduced peptide library of 24 peptides with diverse and representative molecular characteristics was selected, synthesized, and evaluated for the binding strength to Aq. A multivariate QSAR model was established by correlating calculated descriptors, compressed to its principle properties, with the binding data using partial least-square regression. The model was successfully validated by an external test set. Interpretation of the model provided a molecular property binding motif for peptides interacting with Aq. The information may be useful in future research directed toward new treatments of rheumatoid arthritis