No abstract

This article presents an analysis of available scientific data on the morphology and nanostructure of the COVID-19 coronavirus. Possible mechanisms of influence of nanosilver particles on the coronavirus are considered. Models of nanosilver complexes with spike protein of coronavirus amino acids were constructed using computer quantum-chemical modeling. The values of electron density distribution, highest occupied molecular orbital, lowest unoccupied molecular orbital, and electron density distribution gradient for each constructed model are obtained. As a result of quantum chemical modeling, it was found that silver nanoparticles can interact with the following amino acids: Proline, glutamine, lysine, arginine, asparagine, histidine, glutamic and aspartic acids, tryptophan, and cysteine, which is due to the presence of additional -NH2, -NH, -SH and -COOH groups in these amino acids that are not involved in the formation of a peptide bond. The freedom of additional groups makes it possible to interact with nanosilver. Analysis of the obtained data showed that the most energy-efficient interaction is the formation of the "tryptophan-nanosilver" complex (E= -5856.83 kcal/mol). Based on the findings of quantum chemical calculations, the most stable complex is the "cysteine-nanosilver" (ΔE = 0.16 a.u).

Molecular and crystal structure of the title compound has been determined. The (PN)3 and (SiO)4 rings form the dihedral angle of 100.1° and with the HNCH2CH2CH2 plane the angles of 110.2° and 75.7° respectively. The molecular packing is built of alternating layers of (PN)3 and (SiO)4 beterocycles.

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-Non Commercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

No abstract

No abstract

No abstract

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-Non Commercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.