Spike glycoprotein of the SARS-CoV-2 virus and its structure play a crucial role in the infections of cells containing angiotensin-converting enzyme 2 (ACE2) as well as in the interactions of this virus with surfaces. Protection against viruses and often even their deactivation is one of the great varieties of graphene applications. The structural changes of the non-glycosylated monomer of the spike glycoprotein trimer (denoted as S-protein in this work) triggered by its adsorption onto graphene at the initial stage are investigated by means of atomistic molecular dynamics simulations. The adsorption of the S protein happens readily during the first 10 ns. The shape of the S-protein becomes more prolate during the adsorption, but this trend, albeit less pronounced, is observed also for the freely relaxing S-protein in water. The receptor-binding domain (RBD) of the free and adsorbed S-protein manifests itself as the most rigid fragment of the whole S-protein. The adsorption even enhances the rigidity of the whole S-protein as well as its subunits. Only one residue of the RBD involved in the specific interactions with ACE2 during the cell infection is involved in the direct contact of the adsorbed S-protein with the graphene. The new intramolecular hydrogen bonds formed during the S-protein adsorption replace the S-protein-water hydrogen bonds; this trend, although less apparent, is observed also during the relaxation of the free S protein in water. In the initial phase, the secondary structure of the RBD fragment specifically interacting with ACE2 receptor is not affected during the S-protein adsorption onto the graphene.

<jats:p>This chapter discusses key elements when devising a transition from traditional laboratory classes towards a digital platform. First, an overview of the types of online and digital chemistry laboratory teaching methods is described. Then it is analyzed a specific case of an abrupt transition of curricula of a practical chemistry undergraduate class. The assessment will be argued with a series of tasks that aim to identify challenges using a real-life laboratory transition. This will be done by outlining the major influences of teacher's transition outtakes. </jats:p>

In this work we present a computational analysis together with experimental studies, focusing on the interaction between a benzothiazole (BTS) and lysozyme. Results obtained from isothermal titration calorimetry, UV-vis, and fluorescence were contrasted and complemented with molecular docking and machine learning techniques. The free energy values obtained both experimentally and theoretically showed excellent similarity. Calorimetry, UV-vis, and 3D/2D-lig-plot analysis revealed that the most relevant interactions between BTS and lysozyme are based on a predominance of aromatic, hydrophobic Van der Waals interactions, mainly aromatic edge-to-face (T-shaped) pi-pi stacking interactions between the benzene ring belonging to the 2-(methylthio)-benzothiazole moiety of BTS and the aromatic amino acid residue TRP108 of the lysozyme receptor. Next, conventional hydrogen bonding interactions contribute to the stability of the BTS-lysozyme coupling complex. In addition, mechanistic approaches performed using elastic network models revealed that the BTS ligand theoretically induces propagation of allosteric signals, suggesting non-physiological conformational flexing in large blocks of lysozyme affecting alpha-helices. Likewise, the BTS ligand interacts directly with allosteric residues, inducing perturbations in the conformational dynamics expressed as a moderate conformational softening in the alpha-helices H1, H2, and their corresponding beta-loop in the lysozyme receptor, in contrast to the unbound state of lysozyme.</p>

This work aims at unravelling the interactions in magnetic ionic liquids (MILs) by applying Symmetry-Adapted Perturbation Theory (SAPT) calculations, as well as based on those to set-up a polarisable force field model for these liquids. The targeted MILs comprise two different cations, namely: 1-butyl-3-methylimidazolium ([Bmim](+)) and 1-ethyl-3-methylimidazolium ([Emim](+)), along with several metal halides anions such as [FeCl4](-), [FeBr4](-), [ZnCl3](-) and [SnCl4](2-) To begin with, DFT geometry optimisations of such MILs were performed, which in turn revealed that the metallic anions prefer to stay close to the region of the carbon atom between the nitrogen atoms in the imidazolium fragment. Then, a SAPT study was carried out to find the optimal separation of the monomers and the different contributions for their interaction energy. It was found that the main contribution to the interaction energy is the electrostatic interaction component, followed by the dispersion one in most of the cases. The SAPT results were compared with those obtained by employing the local energy decomposition scheme based on the DLPNO-CCSD(T) method, the latter showing slightly lower values for the interaction energy as well as an increase of the distance between the minima centres of mass. Finally, the calculated SAPT interaction energies were found to correlate well with the melting points experimentally measured for these MILs.

Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets. Given the difficulties of experimental assays for estimating the genotoxicity of many NMs on diverse biological targets, development of alternative methodologies is crucial to establish their level of safety. In silico modelling approaches, such as Quantitative Structure-Toxicity Relationships (QSTR), are now considered a promising solution for such purpose. In this work, a perturbation theory machine learning (PTML) based QSTR approach is proposed for predicting the genotoxicity of metal oxide NMs under various experimental assay conditions. The application of such perturbation approach to 6084 NM-NM pair cases, set up from 78 unique NMs, afforded a final PTML-QSTR model with an accuracy better than 96% for both training and test sets. This model was then used to predict the genotoxicity of some NMs not included in the modelling dataset. The results for this independent data set were in excellent agreement with the experimental ones. Overall, that thus suggests that the derived PTML-QSTR model is a reliable in silico tool to rapidly and cost-efficiently assess the genotoxicity of metal oxide NMs. Finally, and most importantly, the model provides important insights regarding the mechanism of the genotoxicity triggered by these NMs.

Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti- trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods.

Azithromycin (AZY) is among the antibiotics with the highest concentrations in aqueous matrices. Conventional wastewater treatment plants (WWTPs) do not fully remove this compound and it has been found in the environment. The health effects of AZY on humans and the environment are not yet understood. Therefore, the development of sensitive and selective analytical methods for AZY determination is important. In this work, an electrochemical molecularly imprinted polymer (MIP) sensor was developed for the quantification of AZY. The selection of the functional monomer was performed by molecular modelling using quantum mechanics calculations. The MIP was electropolymerized by cyclic voltammetry (CV) on a screen-printed carbon electrode (SPCE) using a solution containing 4-aminobenzoic acid (4-ABA) in the presence of AZY as a template molecule. The characterisation of the sensor was carried out using scanning electron microscopy (SEM), CV and electrochemical impedance spectroscopy (EIS). The analysis of AZY was performed by differential pulse voltammetry (DPV) in the linear range between 0.5 and 10.0 mu M, with a limit of detection (LOD) of 0.08 mu M (S/N = 3) and a limit of quantification (LOQ) of 0.3 mu M (S/N = 10). The MIP sensor was found to have selectivity to recognise AZY molecules and was successfully applied in the analysis of tap water and water samples collected upstream of a WWTP output in the Ave River. The proposed sensor presents a simple, selective and environmentally friendly strategy for AZY determination in environmental water.