A systematic DFT study of structure and electronic properties of titanium dioxide.

DFT functionals are of paramount importance for an accurate electronic and structural description of transition metal systems. In this work, a systematic analysis using some well-known and commonly used DFT functionals is performed. A comparison of the structural and energetic parameters calculated with the available experimental data is made in order to find the adequate functional for an accurate description of the TiO2 bulk and surface of both anatase and rutile structures. In the absence of experimental data on the surface energy, the theoretical predictions obtained using the high-accuracy HSE06 functional were used as a reference to compare against the surface energy values calculated with the other DFT functionals. A clear improvement in the electronic description of both anatase and rutile was observed by introducing the Hubbard U correction term to PBE, PW91, and OptPBE functionals. The OptPBE-U4 functional was found to offer a good compromise between accurately describing the structural and electronic properties of titania.

Identification of two novel chemical classes of Autotaxin (ATX) inhibitors using Enalos Asclepios KNIME nodes.

Autotaxin is a secreted lysophospholipase D which is a member of the ectonucleotide pyrophosphatase/phosphodiesterase family converting extracellular lysophosphatidylcholine and other non-choline lysophospholipids, such as lysophosphatidylethanolamine and lysophosphatidylserine, to the lipid mediator lysophosphatidic acid. Autotaxin is implicated in various fibroproliferative diseases including interstitial lung diseases, such as idiopathic pulmonary fibrosis and hepatic fibrosis, as well as in cancer. In this study, we present an effort of identifying ATX inhibitors that bind to allosteric ATX binding sites using the Enalos Asclepios KNIME Node. All the available PDB crystal structures of ATX were collected, prepared, and aligned. Visual examination of these structures led to the identification of four crystal structures of human ATX co-crystallized with four known inhibitors. These inhibitors bind to five binding sites with five different binding modes. These five binding sites were thereafter used to virtually screen a compound library of 14,000 compounds to identify molecules that bind to allosteric sites. Based on the binding mode and interactions, the docking score, and the frequency that a compound comes up as a top-ranked among the five binding sites, 24 compounds were selected for in vitro testing. Finally, two compounds emerged with inhibitory activity against ATX in the low micromolar range, while their mode of inhibition and binding pattern were also studied. The two derivatives identified herein can serve as "hits" towards developing novel classes of ATX allosteric inhibitors.

Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists.

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure-activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI).

Systematic Review of Naturally Derived Substances That Act as Inhibitors of the Nicotine Metabolizing Enzyme Cytochrome P450 2A6.

Tobacco smoking has been highlighted as a major health challenge in modern societies. Despite not causing death directly, smoking has been associated with several health issues, such as cardiovascular diseases, respiratory disorders, and several cancer types. Moreover, exposure to nicotine during pregnancy has been associated with adverse neurological disorders in babies. Nicotine Replacement Therapy (NRT) is the most common strategy employed for smoking cessation, but despite its widespread use, NRT presents with low success and adherence rates. This is attributed partially to the rate of nicotine metabolism by cytochrome P450 2A6 (CYP2A6) in each individual. Nicotine addiction is correlated with the high rate of its metabolism, and thus, novel strategies need to be implemented in NRT protocols. Naturally derived products are a cost-efficient and rich source for potential inhibitors, with the main advantages being their abundance and ease of isolation. This systematic review aims to summarize the natural products that have been identified as CYP2A6 inhibitors, validated through in vitro and/or in vivo assays, and could be implemented as nicotine metabolism inhibitors. The scope is to present the different compounds and highlight their possible implementation in NRT strategies. Additionally, this information would provide valuable insight regarding CYP2A6 inhibitors, that can be utilized in drug development via the use of in silico methodologies and machine-learning models to identify new potential lead compounds for optimization and implementation in NRT regimes.

Discovery of the First-in-Class Inhibitors of Hypoxia Up-Regulated Protein†1 (HYOU1) Suppressing Pathogenic Fibroblast Activation.

Fibroblasts are key regulators of inflammation, fibrosis, and cancer. Targeting their activation in these complex diseases has emerged as a novel strategy to restore tissue homeostasis. Here, we present a multidisciplinary lead discovery approach to identify and optimize small molecule inhibitors of pathogenic fibroblast activation. The study encompasses medicinal chemistry, molecular phenotyping assays, chemoproteomics, bulk RNA-sequencing analysis, target validation experiments, and chemical absorption, distribution, metabolism, excretion and toxicity (ADMET)/pharmacokinetic (PK)/in vivo evaluation. The parallel synthesis employed for the production of the new benzamide derivatives enabled us to a) pinpoint key structural elements of the scaffold that provide potent fibroblast-deactivating effects in cells, b) discriminate atoms or groups that favor or disfavor a desirable ADMET profile, and c) identify metabolic "hot spots". Furthermore, we report the discovery of the first-in-class inhibitor leads for hypoxia up-regulated protein 1 (HYOU1), a member of the heat shock protein 70 (HSP70) family often associated with cellular stress responses, particularly under hypoxic conditions. Targeting HYOU1 may therefore represent a potentially novel strategy to modulate fibroblast activation and treat chronic inflammatory and fibrotic disorders.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Exploring the Binding Effects of Natural Products and Antihypertensive Drugs on SARS-CoV-2: An In Silico Investigation of Main Protease and Spike Protein.

In this in silico study, we conducted an in-depth exploration of the potential of natural products and antihypertensive molecules that could serve as inhibitors targeting the key proteins of the SARS-CoV-2 virus: the main protease (Mpro) and the spike (S) protein. By utilizing Induced Fit Docking (IFD), we assessed the binding affinities of the molecules under study to these crucial viral components. To further comprehend the stability and molecular interactions of the "protein-ligand" complexes that derived from docking studies, we performed molecular dynamics (MD) simulations, shedding light on the molecular basis of potential drug candidates for COVID-19 treatment. Moreover, we employed Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations on all "protein-ligand" complexes, underscoring the robust binding capabilities of rosmarinic acid, curcumin, and quercetin against Mpro, and salvianolic acid b, rosmarinic acid, and quercetin toward the S protein. Furthermore, in order to expand our search for potent inhibitors, we conducted a structure similarity analysis, using the Enalos Suite, based on the molecules that indicated the most favored results in the in silico studies. The Enalos Suite generated 115 structurally similar compounds to salvianolic acid, rosmarinic acid, and quercetin. These compounds underwent IFD calculations, leading to the identification of two salvianolic acid analogues that exhibited strong binding to all the examined binding sites in both proteins, showcasing their potential as multi-target inhibitors. These findings introduce exciting possibilities for the development of novel therapeutic agents aiming to effectively disrupt the SARS-CoV-2 virus lifecycle.

Reverse Micelles As Antioxidant Carriers: An Experimental and Molecular Dynamics Study.

Water-in-oil microemulsions with biocompatible components were formulated to be used as carriers of natural antioxidants, such as hydroxytyrosol (HT) and gallic acid (GA). The system was composed of a mixture of natural surfactants, lecithin and monoglycerides, medium chain triglycerides, and aqueous phase. A dual approach was undertaken to study the structure and dynamics of these complicated systems. First, experimental data were collected by using adequate techniques, such as dynamic light scattering (DLS) and electron paramagnetic resonance (EPR) spectroscopy. Following this, a coarse-grained molecular dynamics (CGMD) study based on the experimental composition using the MARTINI force field was conducted. The simulations revealed the spontaneous formation of reverse micelles (RMs) starting from completely random initial conformations, underlying their enhanced thermodynamic stability. The location of the bioactive molecules, as well as the structure of the RM, were in accordance with the experimental findings. Furthermore, GA molecules were found to be located inside the water core, in contrast to the HT ones, which seem to lie at the surfactant interfacial layer. The difference in the antioxidants' molecular location was only revealed in detail from the computational analysis and explains the RM's swelling observed by GA in DLS measurements.

Unscrambling micro-solvation of -COOH and -NH groups in neat dimethyl sulfoxide: insights from 1H-NMR spectroscopy and computational studies.

Dimethyl sulfoxide (DMSO) has a significant, multi-faceted role in medicine, pharmacy, and biology as well as in biophysical chemistry and catalysis. Its physical properties and impact on biomolecular structures still attract major scientific interest, especially the interactions of DMSO with biomolecular functional groups. In the present study, we shed light on the "isolated" carboxylic (-COOH) and amide (-NH) interactions in neat DMSO via1H NMR studies along with extensive theoretical approaches, i.e. molecular dynamics (MD) simulations, density functional theory (DFT), and ab initio calculations, applied on model compounds (i.e. acetic and benzoic acid, ethyl acetamidocyanoacetate). Both experimental and theoretical results show excellent agreement, thereby permitting the calculation of the association constants between the studied compounds and DMSO molecules. Our coupled MD simulations, DFT and ab initio calculations, and NMR spectroscopy results indicated that complex formation is entropically driven and DMSO molecules undergo multiple strong interactions with the studied molecules, particularly with the -COOH groups. The combined experimental and theoretical techniques unraveled the interactions of DMSO with the most abundant functional groups of peptides (i.e. peptide bonds, side chain and terminal carboxyl groups) in high detail, providing significant insights on the underlying thermodynamics driving these interactions. Moreover, the developed methodology for the analysis of the simulation results could serve as a template for future thermodynamic and kinetic studies of similar systems.

A quantum mechanical approach to the oxidation mechanism of graphene oxide (GO).

Elucidation of the reaction mechanism concerning the oxidation above the face and at the edge of a large, oxidized graphene (GO) cluster, namely C80H22O, by molecular oxygen in the first excited state (1Δg) was achieved with quantum mechanical calculations using the ONIOM two-layer method. Oxidation on the face of the aforementioned cluster leads to the formation of an ozone molecule, whereas oxygen molecule attack at the edge of the oxidized graphene surface either launches an ozonide -a five-membered ring species- formation during its outward approach or an 1,3-dioxetane -a four-membered ring species- production along its inward invasion. A detailed examination of the proposed pathways suggests that the ozonide formation should overcome almost one and a half times an adiabatic energy barrier with respect to the ozone production and is strongly exergonic by up to -50.1 kcal mol-1, supporting the experimental findings that both compounds are critically involved in the explosive deoxygenation of GO. On the other hand, the 1,3-dioxetane alternative pathway is considered even more exergonic, although it requires an overwhelming adiabatic energy barrier of 29.8 kcal mol-1 to accomplish its target.

Examining Hemin and its Derivatives: Induction of Heme-Oxygenase-1 Activity and Oxidative Stress in Breast Cancer Cells through Collaborative Experimental Analysis and Molecular Dynamics Simulations.

Hemin triggers intracellular reactive oxygen species (ROS) accumulation and enhances heme oxygenase-1 (HOX-1) activity, indicating its potential as an anticancer agent, though precise control of its intracellular levels is crucial. The study explores the impact of hemin and its derivatives, hemin-tyrosine, and hemin-styrene (H-Styr) conjugates on migration, HOX-1 expression, specific apoptosis markers, mitochondrial functions, and ROS generation in breast cancer cells. Molecular docking and dynamics simulations were used to understand the interactions among HOX-1, heme, and the compounds. Hemin outperforms its derivatives in inducing HOX-1 expression, exhibiting pro-oxidative effects and reducing cell migration. Molecular simulations show that heme binds favorably to HOX-1, followed by the other compounds, primarily through van der Waals and electrostatic forces. However, only van der Waals forces determine the H-Styr complexation. These interactions, influenced by metalloporphyrin characteristics, provide insights into HOX-1 regulation and ROS generation, potentially guiding the development of breast cancer therapies targeting oxidative stress.

Probing micro-solvation in "numbers": the case of neutral dipeptides in water.

How many solvent molecules and in what way do they interact directly with biomolecules? This is one of the most challenging questions regarding a deep understanding of biomolecular functionalism and solvation. We herein present a novel NMR spectroscopic study, achieving for the first time the quantification of the directly interacting water molecules with several neutral dipeptides. Our proposed method is supported by both molecular dynamics simulations and density functional theory calculations, advanced analysis of which allowed the identification of the direct interactions between solute-solvent molecules in the zwitterionic L-alanyl-L-alanine dipeptide-water system. Beyond the quantification of dipeptide-water molecule direct interactions, this NMR technique could be useful for the determination and elucidation of small to moderate bio-organic molecular groups' direct interactions with various polar solvent molecules, shedding light on the biomolecular micro-solvation processes and behaviour in various solvents.

Statistical Inference of Rate Constants in Chemical and Biochemical Reaction Networks Using an "Inverse" Event-Driven Kinetic Monte Carlo Method.

The use of rate models for networks of stochastic reactions is frequently used to comprehend the macroscopically observed dynamic properties of finite size reactive systems as well as their relationship to the underlying molecular events. Τhis particular approach usually stumbles on parameter derivation associated with stochastic kinetics, a quite demanding procedure. The present study incorporates a novel algorithm, which infers kinetic parameters from the system's time evolution, manifested as changes in molecular species populations. The proposed methodology reconstructs distributions required to infer kinetic parameters of a stochastic process pertaining to either a simulation or experimental data. The suggested approach accurately replicates rate constants of the stochastic reaction networks, which have evolved over time by event-driven Monte Carlo (MC) simulations using the Gillespie algorithm. Furthermore, our approach has been successfully used to estimate rate constants of association and dissociation events between molecular species developing during molecular dynamics (MD) simulations. We certainly believe that our method will be remarkably helpful for considering the macroscopic characteristic molecular roots related to stochastic physical and biological processes.

Prediction of Adsorption and Diffusion of Shale Gas in Composite Pores Consisting of Kaolinite and Kerogen using Molecular Simulation.

Natural gas production from shale formations is one of the most recent and fast growing developments in the oil and gas industry. The accurate prediction of the adsorption and transport of shale gas is essential for estimating shale gas production capacity and improving existing extractions. To realistically represent heterogeneous shale formations, a composite pore model was built from a kaolinite slit mesopore hosting a kerogen matrix. Moreover, empty slabs (2, 3, and 4 nm) were added between the kerogen matrix and siloxane surface of kaolinite. Using Grand-Canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, the adsorption and diffusion of pure methane, pure ethane, and a shale gas mixture were computed at various high pressures (100, 150, and 250 atm) and temperature of 298.15 K. The addition of an inner slit pore was found to significantly increase the excess adsorption of methane, as a pure component and in the shale gas mixture. The saturation of the composite pore with methane was observed to be at a higher pressure compared to ethane. The excess adsorption of carbon dioxide was not largely affected by pressure, and the local number density profile showed its strong affinity to kerogen micropores and the hydroxylated gibbsite surface under all conditions and pore widths. Lateral diffusion coefficients were found to increase with increasing the width of the empty slab inside the composite pore. Statistical errors of diffusion coefficients were found to be large for the case of shale gas components present at low composition. A larger composite pore configuration was created to investigate the diffusion of methane in different regions of the composite pore. The calculated diffusion coefficients and mean residence times were found to be indicative of the different adsorption mechanisms occurring inside the pore.

"Hit" to lead optimization and chemoinformatic studies for a new series of Autotaxin inhibitors.

Robust experimental evidence has highlighted the role of Autotaxin (ATX)/Lysophosphatidic acid (LPA) axis not only in the pathogenesis of chronic inflammatory conditions and especially in fibroproliferative diseases but also in several types of cancer. As a result, different series of substrate-, lipid-based and small-molecule ATX inhibitors have been identified thus far by both academia and pharma. The "crowning achievement" of these drug discovery campaigns was the development and entry of the first-in-class ATX inhibitor (ziritaxestat, GLPG-1690) in advanced clinical trials against idiopathic pulmonary fibrosis. Herein, the potency optimization efforts of a new series of Autotaxin inhibitors, namely 2-substituted-2,6-dihydro-4H-thieno[3,4-c]pyrazol-1-substituted amide, is described using a previously identified novel chemical scaffold as a "hit". The mode of inhibition of the most promising ATX inhibitors was investigated, while their cellular activity, aqueous solubility and cytotoxicity were evaluated. Our pharmacological results were corroborated by chemoinformatic tools (molecular docking and molecular dynamics simulations) deployed, to provide insight into the binding mechanism of the synthesized inhibitors to ATX.

Molecular Dynamics Simulation of Pure n-Alkanes and Their Mixtures at Elevated Temperatures Using Atomistic and Coarse-Grained Force Fields.

The properties of higher n-alkanes and their mixtures is a topic of significant interest for the oil and chemical industry. However, the experimental data at high temperatures are scarce. The present study focuses on simulating n-dodecane, n-octacosane, their binary mixture at a n-dodecane mole fraction of 0.3, and a model mixture of the commercially available hydrocarbon wax SX-70 to evaluate the performance of several force fields on the reproduction of properties such as liquid densities, surface tension, and viscosities. Molecular dynamics simulations over a broad temperature range from 323.15 to 573.15 K were employed in examining a broad set of atomistic molecular models assessed for the reproduction of experimental data. The well-established united atom TraPPE (TraPPE-UA) was compared against the all atom optimized potentials for liquid simulations (OPLS) reparametrization for long n-alkanes, L-OPLS, as well as Lipid14 and MARTINI force fields. All models qualitatively reproduce the temperature dependence of the aforementioned properties, but TraPPE-UA was found to reproduce liquid densities most accurately and consistently over the entire temperature range. TraPPE-UA and MARTINI were very successful in reproducing surface tensions, and L-OPLS was found to be the most accurate in reproducing the measured viscosities as compared to the other models. Our simulations show that these widely used force fields originating from the world of biomolecular simulations are suitable candidates in the study of n-alkane properties, both in the pure and mixture states.

Discovery of New non-steroidal selective glucocorticoid receptor agonists.

Glucocorticoids (GCs) are widely used as potent anti-inflammatory drugs; however, GC therapy is often accompanied by adverse side effects. The anti-inflammatory action of GCs is exerted through the glucocorticoid receptor (GR) in part by antagonizing the pro-inflammatory nuclear factor k B (NF-kB) whereas the majority of side effects are assumed to be mediated by transactivation of GR target genes. We set out to identify novel non-steroidal selective GR agonists (SEGRA) favoring transrepression of NF-kB target genes over transactivation of genes associated with undesirable effects. Our virtual screening protocol was driven by a pharmacophore model based on a pyrrolidinone amide analogue (named as 'compound 12' in Biggadike et al 2009, PNAS USA 106, 18,114) bound to the extended binding pocket of the GR ligand binding domain (GR-LBD). Ambinter library (7.8 million compounds) was queried by our validated pharmacophore hypothesis and the prioritized compounds were biologically evaluated using a series of well-established screening assays. Two structurally similar hits (1 and 13) were identified that bind to GR, induce its translocation to the nucleus, do not mediate transactivation of GR target genes whereas partially repress a number of pro-inflammatory NF-kB target genes, in a GR-dependent manner. Explanatory molecular dynamics (MD) calculations could detail the per-residue interactions accounting for the binding of 1 and 13 to the extended binding pocket of GR. The discovered 1,3-benzothiazole analogs introduce a new class of genuine SEGRA paving the way for hit-to-lead optimization.

Publisher Correction: An integrated bacterial system for the discovery of chemical rescuers of disease-associated protein misfolding.

In the version of this Article originally published, in Fig. 1c-e, on the x axes, the lines labelled 'Aß42' and 'Aß42(F19S;L34P)' grouped the data incorrectly; the line labelled Aß42 should have grouped the data for Random 1-2 and Clones 1-10, and the line labelled Aß42(F19S;L34P) should have only grouped the data for Random 1-2 on the right end of the plots and blots. These figures have now been corrected in all versions of the Article.

A DFT study of the nitric oxide and tyrosyl radical interaction: a proposed radical mechanism.

The present study employs a complete theoretical investigation, at the B3LYP/cc-pVTZ level of theory, of the interactions between the tyrosyl radical and nitric oxide, exploring in detail the nitrotyrosine formation radical mechanism. Tyrosyl radicals play an essential role in catalytic reactions of numerous enzymes and biological systems have regulated appropriate mechanisms for their formation. Nitric oxide reacts with the tyrosyl radical and affords a weak intermediate complex which, through a sequence of non-ionic water catalyzed and biologically feasible intermediate reactions, yields the iminoxyl radical. The iminoxyl radical further combines with hydroxyl radical, a species present in pathophysiological conditions, to yield nitrotyrosine.

Computational investigation of fullerene-DNA interactions: Implications of fullerene's size and functionalization on DNA structure and binding energetics.

DNA is the building block of life, as it carries the biological information controlling development, function and reproduction of all organisms. However, its central role in storing and transferring genetic information can be severely hindered by molecules with structure altering abilities. Fullerenes are nanoparticles that find a broad spectrum of uses, but their toxicological effects on living organisms upon exposure remain unclear. The present study examines the interactions of a diverse array of fullerenes with DNA, by means of Molecular Dynamics and MM-PBSA methodologies, with special focus on structural deformations that may hint toxicity implications. Our results show that pristine and hydroxylated fullerenes have no unwinding effects upon DNA structure, with the latter displaying binding preference to the DNA major groove, achieved by both direct formation of hydrogen bonds and water molecule mediation. Fluorinated derivatives are capable of penetrating DNA structure, forming intercalative complexes with high binding affinities.