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A medicinal chemistry approach combining in silico and in vitro methodologies was performed aiming at identifying and characterizing putative allosteric drug-binding sites (aDBSs) at the interface of the transmembrane- and nucleotide-binding domains (TMD-NBD) of P-glycoprotein. Two aDBSs were identified, one in TMD1/NBD1 and another one in TMD2/NBD2, by means of in silico fragment-based molecular dynamics and characterized in terms of size, polarity, and lining residues. From a small library of thioxanthone and flavanone derivatives, experimentally described to bind at the TMD-NBD interfaces, several compounds were identified to be able to decrease the verapamil-stimulated ATPase activity. An IC50 of 81 ± 6.6 μM is reported for a flavanone derivative in the ATPase assays, providing evidence for an allosteric efflux modulation in P-glycoprotein. Molecular docking and molecular dynamics gave additional insights on the binding mode on how flavanone derivatives may act as allosteric inhibitors.

The modulation of drug efflux by P-glycoprotein (P-gp, ABCB1) represents one of the most promising approaches to overcome multidrug resistance (MDR) in cancer cells, however the mechanisms of drug specificity and signal-transmission are still poorly understood, hampering the development of more selective and efficient P-gp modulators. In this study, the impact of four P-gp mutations (G185V, G830V, F978A and ΔF335) on drug-binding and efflux-related signal-transmission mechanism was comprehensively evaluated in the presence of ligands within the drug-binding pocket (DBP), which are experimentally related with changes in their drug efflux profiles. The severe repacking of the transmembrane helices (TMH), induced by mutations and exacerbated by the presence of ligands, indicates that P-gp is sensitive to perturbations in the transmembrane region. Alterations on drug-binding were also observed as a consequence of the TMH repacking, but were not always correlated with alterations on ligands binding mode and/or binding affinity. Finally, and although all P-gp variants holo systems showed considerable changes in the intracellular coupling helices/nucleotide-binding domain (ICH-NBD) interactions, they seem to be primarily induced by the mutation itself rather than by the presence of ligands within the DBP. The data further suggest that the changes in drug efflux experimentally reported are mostly related with changes on drug specificity rather than effects on signal-transmission mechanism. We also hypothesize that an increase in the drug-binding affinity may also be related with the decreased drug efflux, while minor changes in binding affinities are possibly related with the increased drug efflux observed in transfected cells.

We present a comprehensive review of methods and applications of molecular simulations of interfacial systems. We give a detailed overview of the main techniques and major challenges in the followi...

The tau protein is considered an important qualitative and quantitative biomarker for Alzheimer’s disease in its asymptomatic phase. In 2011, biomarkers were suggested by the National Institute on Aging-Azheimer’s Association as a new criterion for the early diagnosis of Alzheimer’s disease. Thus, highlighting the non-existence of theoretical research on the subject, we investigated the binding interaction properties between phosphorylated tau protein and a theoretically modeled ligands constituted by the fullerol functionalized with radiopharmaceuticals from an in silico approach via molecular docking and density functional theory (DFT) ab initio computational simulation. The results demonstrated that the ligand with the greatest affinity-based binding energy to the protein was fullerol + F-THK5105. However, all systems were considered promising for the development of a potential diagnostic nanomarker. These theoretical results could efficiently contribute to reduce the time and the cost for future experimental preclinical studies and open new opportunities toward molecular recognition in nanomedicine.

At the core of the development of more efficient and reliable fuel cells (FCs), there are several essential chemical reactions, namely carbon monoxide (CO) oxidation. This reaction is a keystone in the cleaning of hydrogen fuel used in fuel cells due to strong poisoning by this species of the platinum catalyst used in these devices. The present work aims to provide insight regarding the activation of CO oxidation by gold or silver microfacets possessing low coordinated atoms. To achieve this, density functional theory (DFT) quantum calculations, which determined two competing reaction pathways for CO oxidation, i.e., by molecularly adsorbed oxygen, and by dissociated oxygen, are combined with first-principles kinetic Monte Carlo (1p-kMC) simulations, which employed the resulting DFT parameters in order to address the effect of temperature and partial pressures and the interplay of the elementary reaction events. The use of 1p-kMC is a step further from available works regarding the CO oxidation on gold- and silver-based catalysts for cleansing of hydrogen that is used as a fuel in FCs. Indeed, this research contributes to the conclusion that CO oxidation should preferentially occur on silver microfacets, while the obtained turnover frequencies (TOFs) reinforced such a conclusion.

In this Letter, we report the first observation of the capacitance-potential hysteresis at the ionic liquid | electrode interface in atomistic molecular dynamics simulations. While modeling the differential capacitance dependence on the potential scan direction, we detected two long-living types of interfacial structure for the BMImPF6 ionic liquid at specific charge densities of the gold Au(111) surface. These structures differ in how counterions overscreen the surface charge. The high barrier for the transition from one structure to another slows down the interfacial restructuring process and leads to the marked capacitance-potential hysteresis.

By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.

Background Bioremediation is a biotechnology field that uses living organisms to remove contaminants from soil and water; therefore, they could be used to treat oil spills from the environment. Methods Herein, we present a new mechanistic approach combining Molecular Docking Simulation and Density Functional Theory to modeling the bioremediation-based nanointeractions of a heterogeneous mixture of oil-derived hydrocarbons by using pristine and oxidized graphene nanostructures and the substrate-specific transport protein (TodX) from Pseudomonas putida. Results The theoretical evidences pointing that the binding interactions are mainly based on noncovalent bonds characteristic of physical adsorption mechanism mimicking the “Trojan-horse effect”. Conclusion These results open new horizons to improve bioremediation strategies in over-saturation conditions against oil-spills and expanding the use of nanotechnologies in the context of environmental modeling health and safety.

Three-dimensional conformational crystallographic binding-modes are of paramount importance to understand the docking mechanism of protein-ligand interactions and to identify potential "leading drugs" conformers towards rational drugs-design. Herein, we present an integrated computational-experimental study tackling the problem of multiple binding modes among the ligand 3-(2-Benzothiazolylthio)-propane sulfonic acid (BTS) and the fibrinogen receptor (E-region). Based on molecular docking simulations, we found that the free energy of binding values for nine of different BTS-docking complexes (i.e., BTS-pose_1-9) were very close. We have also identified a docking-mechanism of BTS-interaction mainly based on non-covalent hydrophobic interactions with H-bond contacts stabilizing the fibrinogen-BTS docking complexes. Interestingly, the different BTS-poses_1-9 were found to be able to block the fibrinogen binding site (E-region) by inducing local perturbations in effector and allosteric residues, reducing the degree of collectivity in its flexibility normal modes. As such, we theoretically suggest that the BTS-binding modes can significantly affect the physiological condition of the unoccupied fibrinogen protein structure by bringing global and local perturbations in the frequency domain spectra. The proposed theoretical mechanisms, the interactions involved and the conformational changes suggested, were further corroborated by different experimental techniques such as isothermal titration calorimetry (ITC), zeta potential, UV-vis, fluorescence and small angle X-ray scattering (SAXS). The combined results shall open new avenues towards the application of complex supra-molecular information in rational drugs-design.

Functionalizing graphene with polymers is an important area of research, owing to the potential applications of graphene in biomedicine and nanotechnology. In this paper, we investigate the covalen...

The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box–Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (&gt, 90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries—i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.

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 μM, with a limit of detection (LOD) of 0.08 μM (S/N = 3) and a limit of quantification (LOQ) of 0.3 μM (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.

Quantitative structure-activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized end points is a crucial task for the QSAR researcher. In the present study, we propose an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, "exhaustive" double cross-validation and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. These tools are freely available for download from https://dtclab.webs.com/software-tools . We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.

We present a computational analysis coupled with experimental studies, focusing on the binding-interaction between beta-adrenoreceptor blocking agents (acebutolol and propranolol) with fibrinogen protein (E-region). Herein, computational modeling on structural validation and flexibility properties of fibrinogen E-region showed that the E-region interacting residues, which form the funnel-shaped hydrophobic cavity for ligand-binding, can be efficiently modeled. The obtained free energy of binding (FEB) values for the docking complexes, namely acebutolol/fibrinogen E-region and propranolol/fibrinogen E-region, were very close and amounted to - 6.9 kcal/mol and - 6.8 kcal/mol, respectively. They were supported by a high binding-accuracy (R.M.S.D < 2 A) for the best crystallographic binding-poses in both cases. In this regard, we identify a docking-mechanism of interaction for the propranolol and acebutolol mainly based on non-covalent hydrophobic contacts with the fibrinogen E-region binding-site. Besides, the beta-adrenoreceptor blocking agents are able to induce local perturbations affecting particularly the fibrinogen E-region allosteric residues linked to significant changes in the inter-residue communication and flexibility properties of residue network. In this sense, we show that the key biophysical parameters like frequency and collectivity degree may be compromised in different ways by the interaction with acebutolol and propranolol. Isothermal titration calorimetry, zeta potential and small angle X-ray scattering (SAXS) measurements were performed to complete and corroborate computational analysis. The combined experimental results point out that acebutolol acts to a lesser extent to fibrinogen structure than propranolol.

Molecular dynamics simulations of the electrical double layer at electrode–ionic liquid interfaces allow for molecular level interpretation of the interfacial phenomena and properties, such as differential capacitance (C). In this work, we have simulated an ionic liquid – 1-butyl-3-methylimidazolium hexafluorophosphate – at three gold surfaces, namely: Au(100), Au(110), and Au(111) surfaces. Atomic corrugation of the gold surface leads to higher C values due to the rapprochement of the surface and electrolyte charge planes. Likewise, by accounting for the shift of surface charge plane position towards the electrolyte also results in higher C values. The presented insight shows that a simple correction to the simulation data improves the agreement with the experimental data.

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

Recently, water transport through graphene oxide (GO) nanochannels has gained much attention because of its potential applications in desalination and water filtration. In this work, molecular dynamics (MD) simulations were carried out to elucidate the filtration efficiency and water structure in GO nanochannels of varying oxidation degree (from 0% to 40%) and interlayer spacing (from 0.6 nm to 1.0 nm). The results from these simulations show that ion permeation is observed in 1.0 nm channels, but ion rejection is close to complete for the narrower channels, irrespective of the degree of oxidation. Furthermore, water permeation increases with increasing interlayer separation and decreases with increasing oxidation degree. In general, water molecules prefer flowing through unoxidized regions of the narrower channels, but show no preferential path when flowing through the wider ones. Moreover, the analysis of the interaction between water molecules and the hydroxyl groups inside the GO channel shows that OH groups play a vital role disrupting the solvation sphere of the salt ions. However, with increasing oxidation of the GO wall, the formation of intra-layer hydrogen bonds becomes relevant, decreasing the overall number of hydrogen bonds involving water molecules, and thus preventing further decrease of the water permeance. To sum up, our results indicate that the best GO channel for the desalination is the one with interlayer spacing 0.8 nm and oxidation degree of 10% or lower.

Because of the increasing demand of greener solvents, deep eutectic solvents (DES) have just emerged as low-cost alternative solvents for a broad range of applications. However, recent toxicity ass...

Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

Predicting enzyme function and enzyme subclasses is always a key objective in fields such as biotechnology, biochemistry, medicinal chemistry, physiology, and so on. The Protein Data Bank (PDB) is the largest information archive of biological macromolecular structures, with more than 150 000 entries for proteins, nucleic acids, and complex assemblies. Among these entries, there are more than 4000 proteins whose functions remain unknown because no detectable homology to proteins whose functions are known has been found. The problem is that our ability to isolate proteins and identify their sequences far exceeds our ability to assign them a defined function. As a result, there is a growing interest in this topic, and several methods have been developed to identify protein function based on these innovative approaches. In this work, we have applied perturbation theory to an original data set consisting of 19 187 enzymes representing all 59 subclasses present in the protein data bank. In addition, we developed a series of artificial neural network models able to predict enzyme-enzyme pairs of query-template sequences with accuracy, specificity, and sensitivity greater than 90% in both training and validation series. As a likely application of this methodology and to further validate our approach, we used our novel model to predict a set of enzymes belonging to the yeast Pichia stipites. This yeast has been widely studied because it is commonly present in nature and produces a high ethanol yield by converting lignocellulosic biomass into bioethanol through the xylose reductase enzyme. Using this premise, we tested our model on 222 enzymes including xylose reductase, that is, the enzyme responsible for the conversion of biomass into bioethanol.

The applicability of deep eutectic solvents is determined by their physicochemical properties. In turn, the properties of eutectic mixtures are the result of the components’ molar ratio and chemical composition. Owing to the relatively low viscosities displayed by alcohol-based deep eutectic solvents (DESs), their application in industry is more appealing. Modeling the composition–property relationships established in polyalcohol-based mixtures is crucial for both understanding and predicting their behavior. In this work, a physicochemical property–structure comparison study is made between four choline chloride polyalcohol-based DESs, namely, ethaline, propeline, propaneline, and glyceline. Physicochemical properties obtained from molecular dynamic simulations are compared to experimental data, whenever possible. The simulations cover the temperature range from 298.15 to 348.15 K. The simulated and literature experimental data are generally in good agreement for all the studied DESs. Structural properties, such as radial and spatial distribution functions, coordination numbers, hydrogen bond donor (HBD)–HBD aggregate formation, and hydrogen bonding are analyzed in detail. The higher prevalence of HBD:HBD and HBD:anion hydrogen bonds is likely to be the major reason for the relatively high density and viscosity of glyceline as well as for lower DES self-diffusions.

In this work, we report the TMtopo data set containing optimized geometries, quantum calculated properties, and quantum topological descriptors for 1110 first row TM complexes. Properties were computed at the TPSSh/Def2-TZVP level of theory and the quantum topological descriptors were collected under the framework of the Quantum Theory of Atoms in Molecules (QTAIM), including a systematic topological survey of the Laplacian of the electron density, ∇2ρ(r). This survey yielded novel insights on the proliferation of inner Valence Shell Charge Concentrations (iVSCCs, local minima of ∇2ρ(r)) in the metal center, suggesting that their number is determinant for the stabilization of the metal center in a more intense manner than their arrangement opposing each of the metal’s ligands (Inorg. Chem. 2016, 55, 3653). Pairwise representation of the collected properties revealed overall low correlation, although some structure could be perceived in the data (specially when considering the topological features of ∇ 2 ρ(r)). This suggests that the TMtopo data set could be usefully exploited in the data-driven discovery of new TM complexes with interesting properties for applications in as catalysis, opto-electronics and sustainable energy production and storage.

The aim of this work is to quantify the evolution of the various noncovalent interparticle interactions in the binary mixtures of imidazolium-based ionic liquids (ILs) with perfluorinated anions, hexafluorophosphate (C4mimPF6), bis(fluorosulfonyl)imide (C4mimNTf2), and tris(pentafluoroethyl)trifluorophosphate (C4mimFAP), and deep eutectic solvents (DESs), composed by choline chloride (ChCl), and ethylene- (EG) and propylene glycol (PG) molecules. For this purpose, by using combined molecular dynamics and density functional theory methodology, we analyze the most important intermolecular, interionic, and ion-molecular hydrogen bonding interactions in ILs and DESs. We found that the addition of FAP− to a mixture enhances the nonpolar domain segregations and weakens the hydrogen-bond network. It was confirmed that H2-site is the most prone to cation–anion interactions, whereas in the binary mixtures of ILs two anions compete for the most acidic hydrogen of imidazolium ring. Nonetheless, most of the hydrogen bonds in the studied IL mixtures are rather weak and can be referred to as “hydrogen-bond-like interactions.” The FAP-anion forms notably less hydrogen-bond-like interactions and only in neat IL or in FAP-rich regions of IL mixtures, stabilizing the ion pair mostly electrostatically. For the DES mixtures, it was established that ethaline-2 (molar ratio of ChCl and EG is 1:2) does not contain any real hydrogen bonds. Propeline (DES-based on ChCl and PG), however, presents weak hydrogen-bond formation between cation and anion and medium hydrogen-bond formation between anion and PG molecule.

The ever-increasing presence of contaminants in environmental waters is an alarming issue, not only because of their harmful effects in the environment but also because of their risk to human health. Pharmaceuticals and pesticides, among other compounds of daily use, such as personal care products or plasticisers, are being released into water bodies. This release mainly occurs through wastewater since the treatments applied in many wastewater treatment plants are not able to completely remove these substances. Therefore, the analysis of these contaminants is essential but this is difficult due to the great variety of contaminating substances. Facing this analytical challenge, electrochemical sensing based on molecularly imprinted polymers (MIPs) has become an interesting field for environmental monitoring. Benefiting from their superior chemical and physical stability, low-cost production, high selectivity and rapid response, MIPs combined with miniaturized electrochemical transducers offer the possibility to detect target analytes in-situ. In most reports, the construction of these sensors include nanomaterials to improve their analytical characteristics, especially their sensitivity. Moreover, these sensors have been successfully applied in real water samples without the need of laborious pre-treatment steps. This review provides a general overview of electrochemical MIP-based sensors that have been reported for the detection of pharmaceuticals, pesticides, heavy metals and other contaminants in water samples in the past decade. Special attention is given to the construction of the sensors, including different functional monomers, sensing platforms and materials employed to achieve the best sensitivity. Additionally, several parameters, such as the limit of detection, the linear concentration range and the type of water samples that were analysed are compiled.

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

In this work, one of the most prevalent polypharmacology drug&ndash, drug interaction events that occurs between two widely used beta-blocker drugs&mdash, i.e., acebutolol and propranolol&mdash, with the most abundant blood plasma fibrinogen protein was evaluated. Towards that end, molecular docking and Density Functional Theory (DFT) calculations were used as complementary tools. A fibrinogen crystallographic validation for the three best ranked binding-sites shows 100% of conformationally favored residues with total absence of restricted flexibility. From those three sites, results on both the binding-site druggability and ligand transport analysis-based free energy trajectories pointed out the most preferred biophysical environment site for drug&ndash, drug interactions. Furthermore, the total affinity for the stabilization of the drug&ndash, drug complexes was mostly influenced by steric energy contributions, based mainly on multiple hydrophobic contacts with critical residues (THR22: P and SER50: Q) in such best-ranked site. Additionally, the DFT calculations revealed that the beta-blocker drug&ndash, drug complexes have a spontaneous thermodynamic stabilization following the same affinity order obtained in the docking simulations, without covalent-bond formation between both interacting beta-blockers in the best-ranked site. Lastly, experimental ultrasound density and velocity measurements were performed and allowed us to validate and corroborate the computational obtained results.

G-Protein coupled receptors (GPCRs) are involved in a myriad of pathways key for human physiology through the formation of complexes with intracellular partners such as G-proteins and arrestins (Arrs). However, the structural and dynamical determinants of these complexes are still largely unknown. Herein, we developed a computational big-data pipeline that enables the structural characterization of GPCR complexes with no available structure. This pipeline was used to study a well-known group of catecholamine receptors, the human dopamine receptor (DXR) family and its complexes, producing novel insights into the physiological properties of these important drug targets. A detailed description of the protein interfaces of all members of the DXR family (D1R, D2R, D3R, D4R, and D5R) and the corresponding protein interfaces of their binding partners (Arrs: Arr2 and Arr3; G-proteins: Gi1, Gi2, Gi3, Go, Gob, Gq, Gslo, Gssh, Gt2, and Gz) was generated. To produce reliable structures of the DXR family in complex with either G-proteins or Arrs, we performed homology modeling using as templates the structures of the β2-adrenergic receptor (β2AR) bound to Gs, the rhodopsin bound to Gi, and the recently acquired neurotensin receptor-1 (NTSR1) and muscarinic 2 receptor (M2R) bound to arrestin (Arr). Among others, the work demonstrated that the three partner groups, Arrs and Gs- and Gi-proteins, are all structurally and dynamically distinct. Additionally, it was revealed the involvement of different structural motifs in G-protein selective coupling between D1- and D2-like receptors. Having constructed and analyzed 50 models involving DXR, this work represents an unprecedented large-scale analysis of GPCR-intracellular partner interface determinants. All data is available at www.moreiralab.com/resources/dxr.

[EN] Higher Education Student burnout is an increasingly educational and social concern. The problem is complex and multilayered, demanding new approaches in predicting hazardous situations that can lead to the demise of the mental and physical well-being of the students. This work proposes a new model that can be used to predict and prevent such educational and/or social scenarios, resourcing to new tools, as the Reductio ad dystopia and speculative data. It departs from recent social quantum-based models and selected speculative literature works while introducing the use of social network theory to add the time variable to the model. The results clearly indicate that speculative and real scenarios can be juxtaposed in such a model, and concludes that a time interval for predicting the occurrence of the problem can be one of its advantages., This work had the financial support of Fundação para a Ciência e a Tecnologia (FCT/MEC) through national funds (UIDB/50006/2020 grant and funding into framework of Norma Transitória - DL 57/2016/CP1346/0019). Ana S. Moura further acknowledges contract IF CEECIND/03631/2017. The authors also want to thank the invaluable help of Patrícia C. T. Gonçalves in the development of the graph models.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp ≈ Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

In this work, we carried out a systematic and detailed study of water under severely confined conditions, based on atomistic molecular dynamics (MD) simulations. Specifically, MD simulations were performed for water confined in single-walled capped carbon nanotubes of chiral indices between (4,4) and (10,10). The structure and dynamics of confined water, and its dependence on the diameter and length of the capped carbon nanotubes (CCNTs) was examined, alongside with the influence of the latter on water immersion. Our results show that the axial water density decreases with increasing distance from the open face of the CCNTs, and that water forms well-defined shells along the radius of the CCNTs. The confined water molecules closer to the water–wall interfacial region tend to orient themselves pointing their OH bonds towards the wall. This trend becomes less evident in the inner regions of the CCNTs, so that the water molecules in the central region of the (10,10) CCNT are almost randomly oriented. On the other hand, the water molecules inside the (6,6) CCNT form a single-file chain along the CCNT's axis. Furthermore, the average number of hydrogen bonds per water molecule decreases with increasing length of the CCNT and decreasing diameter. Hydrogen bonding and water orientations in the CCNTs significantly affect the mobility of water, being the mobility of water faster in the (7,7) CCNT than in other CCNTs of the same length.

Quantitative Structure-Activity Relationship (QSAR) models are becoming one of the most interesting fields for developing therapeutics and therapeutics related patents. At present, QSAR methodologies comprise a series of possibilities, including joining forces with machine learning methods and increasing even more the swiftness they might bring to the prospective development of therapeutics in the Health Sciences scope.After evaluating the period from 2010 to early 2018, the areas covered by the reviewed QSAR based therapeutics patents comprise three main fields (drug development, risk assessment and novel QSAR methodologies), and several areas, from cancer and cancer related symptomatology to neurodegenerative diseases, such as Parkinson's disease, or even monitoring several chemical particles carrier-mediums or interface frontiers.Among the several conclusions drawn from this reviewing, some pertain to the near future of investigative research on QSAR based inventions for therapeutic purposes, while others include the prospective of an even more grown interest on cytotoxicity assessment with in silico models and protocols. Further, the type of compounds described in these types of patents is likely to see an increase in neurodegenerative diseases therapeutics, as the novel methodologies meet the challenging global health needs as human life expectancy increases.

Correction for ‘An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers’ by David Ferro-Costas et al., Phys. Chem. Chem. Phys., 2022, DOI: 10.1039/d1cp03928h.

Nanotechnology has led to the development of new nanomaterials with unique properties and a wide variety of applications. In the present study, we focused on the cellular uptake of a group of nanoparticles with a single metal core by pancreatic cancer cells, which has been studied by Yap et al. (Rsc Advances, 2012, 2 (2):8489-8496) using classification models. In this work, the development of a further Quantitative Nanostructure-Activity Relationship (QNAR) model was performed by linear multiple linear regression (MLR) and nonlinear artificial neural network (ANN) techniques to accurately predict the cellular uptake values of these compounds by dividing them into three groups. Judging from the attained statistical results, our derived QNAR models have an acceptable overall accuracy and robustness, as well as good predictivity on the external data sets. Moreover, the results of this study provide some insights on how engineered nanomaterial features influence cellular responses and thereby outline possible approaches for developing and applying predictive computational models for biological responses caused by exposure to nanomaterials.

Mixing of ionic liquids provides new opportunities for their tuning, enabling the applications of ionic liquid mixtures to expand. At the same time, the genesis of the fundamental properties of ionic liquid mixtures is still poorly understood. In this study we carried out a molecular dynamics simulation of binary mixtures of 1-buthyl-3-methylimidazolium hexafluorophosphate, 1-buthyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, and 1-buthyl-3-methylimidazolium tris(perfluoroethyl)trifluorophosphate ([C4mim][PF6] + [C4mim][NTf2], [C4mim][PF6] + [C4mim][FAP], [C4mim][FAP] + [C4mim][NTf2]) in a wide concentration range at 303.15 K and complemented it with quantum mechanical calculations. Three pure ionic liquids underwent the same kind of analysis for comparison purposes. We found that the addition of the [FAP]--anion to a mixture enhances the segregation of non-polar domains and weakens the hydrogen-bond network. The H-bonds in the studied mixtures are rather weak, as follows from QTAIM analysis, with the rarest occurrence for the [FAP]--anion. The competition of two anions in the mixtures for the most acidic hydrogen of the 1-butyl-3-methylimidazolium cation is reported. In most of the cases, the smaller anion ([PF6]- or [NTf2]-) with stronger charge concentration displaces the bigger one ([NTf2]- or [FAP]-) from the preferred coordination site. The existing nano-segregation in some mixtures notably slows down ion diffusion. Our results show that the differences in anion size, shape and nature are the main reasons for nano-segregation and the non-ideal behavior of ionic liquid mixtures.

Evolution from fossil fuel energy to renewable energy sources and technologies is in the spotlight towards an accelerated energy transition process. One of the challenges of the intermittent renewable energy production is related to the existence of an appropriate energy storage technology in order to effectively use the renewable energy generated. Electrochemical energy storage devices rely on the key property of the electrical double layer integral capacitance. The use of mixed ionic liquids can be an effective strategy to increase the performance of electric double layer capacitors. Here, the studies on the interfacial behaviour of ionic liquids mixtures containing a common ion for a model mercury/ionic liquid interface are reported. Enhancement of the differential capacitance, nearly 3 times higher compared to ILs in the pure state, was achieved by an appropriate combination of ion size both in cation and the anion and asymmetry. The results are interpreted as a consequence of surface voids occupation and by the accumulation of more counter ions and displacement larger anion by the smaller anion in the mixture.

Graphene based materials are nowadays extensively studied because of their potential applications as gas sensors, biosensors or adsorbents. Doping the graphene surface with heteroatoms or transition metals can improve its electronic properties and chemical reactivity. Polyaromatic hydrocarbons coronene and circumcoronene can be used as models of tiny graphene quantum dots. The adsorption of a set of organic molecules (water, hydrogen peroxide, hydrogen sulfide, methanol, ethanol and oxygen molecule) over the copper-doped coronene and circumcoronene was theoretically studied using density functional theory (DFT) and quantum theory of atoms in molecules (QTAIM). In the case of coronene, only one site was considered for the Cu-doping, whereas in the case of circumcoronene being a polyaromatic hydrocarbon composed of 54 carbon atoms, three different sites for Cu-doping were considered. For the systems under study, the adsorption of O 2 was found energetically the most favorable, with energetic outcome ranging from –3.1 to −3.7 eV related to the position of dopant Cu atom. Changes in the topology of charge densities at Cu and in its vicinity after the adsorption of studied molecules were investigated in the framework of QTAIM. In addition, QTAIM analysis of bond critical points (BCP) was employed to study the character of the newly formed chemical bonds. The results of this study point out the suitability of Cu-doped graphene materials as sensors and/or adsorbents in practical applications.

Amitriptyline (AMT) frequent presence in environmental waters reflects the continuous consumption growth and raises issues on the importance of its monitorization. In this work, a sensitive and selective electrochemiluminescence (ECL) sensor was constructed using molecularly imprinted polymer (MIP) recognition element for AMT detection. Molecular dynamics (MD) simulations were performed to select the best functional monomer. Precipitation polymerization was followed to prepare the MIP microspheres using methacrylic acid (MAA) as functional monomer, ethylene glycol methacrylate (EGDMA) as crosslinker and chloroform (CHL) as solvent. The MIP sensor was then prepared on a low cost and disposable screen-printed electrodes (SPCEs), previously modified with single-walled carbon nanotubes (SWCNTs), by drop coating a solution containing the MIP microspheres synthesized. The mechanism of detection was based in the system Ru(bpy)32+/AMT, where AMT acts as co-reactor of Ru(bpy)32+ ECL. Several parameters controlling the preparation process of the sensor and AMT detection were optimised. The MIP/SWCNTs/SPCE ECL sensor showed good analytical performance with a linear correlation between ECL signal and the AMT concentration ranging from 0.1 to 200 µM (R2 = 0.9991). The limits of detection (LOD) and quantification (LOQ) were found to be 0.4 µM (S/N=3) and 1.5 µM (S/N = 10), respectively. The MIP ECL sensor displayed good selectivity to recognise AMT molecules when compared with analoge structures and it was successfully applied in real water samples with good recovery values (90 to 112%). The developed MIP ECL sensor is suitable for integration with portable devices for AMT detection in environmental waters.

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.