Differential impact of diesel exhaust particles on glutamatergic and dopaminergic neurons in Caenorhabditis elegans: A neurodegenerative perspective.

The growing body of evidence links exposure to particulate matter pollutants with an increased risk of neurodegenerative diseases. In the present study, we investigated whether diesel exhaust particles can induce neurobehavioral alterations associated with neurodegenerative effects on glutamatergic and dopaminergic neurons in Caenorhabditis elegans (C. elegans). Exposure to DEP at concentrations of 0.167 µg/cm2 and 1.67 µg/cm2 resulted in significant developmental delays and altered locomotion behaviour. These effects were accompanied by discernible alterations in the expressions of antioxidant genes sod-3 and gst-4 observed in transgenic strains. Behaviour analysis demonstrated a significant reduction in average speed (p < 0.001), altered paths, and decreased swimming activities (p < 0.01), particularly at mid and high doses. Subsequent assessment of neurodegeneration markers in glutamatergic (DA1240) and dopaminergic (BZ555) transgenic worms revealed notable glutamatergic neuron degeneration at 0.167 µg/cm2 (∼30 % moderate, ∼20 % advanced) and 1.67 µg/cm2 (∼28 % moderate, ∼24 % advanced, p < 0.0001), while dopaminergic neurons exhibited structural deformities (∼16 %) without significant degeneration in terms of blebs and breaks. Furthermore, in silico docking simulations suggest the presence of an antagonistic competitive inhibition induced by DEP in the evaluated neuro-targets, stronger for the glutamatergic transporter than for the dopaminergic receptor from the comparative binding affinity point of view. The results underscore DEP's distinctive neurodegenerative effects and suggest a link between locomotion defects and glutamatergic neurodegeneration in C. elegans, providing insights into environmental health risks assessment.

Decoding the conformational binding of drug mixtures on ovalbumin: An integrated multimodal network.

This research addresses the crucial necessity for a deeper understanding of the binding interactions between surfactants and proteins, with a specific focus on ovalbumin. Considering ovalbumin's role in diverse biochemical processes, it remains a subject of significant interest for drug discovery and design. To fill existing knowledge gaps, we investigated the binding interaction between dicloxacillin and cetyltrimethylammonium bromide (CTAB) on ovalbumin, employing a comprehensive approach that combines computational modeling with experimental validations. Using the ezPocket tool, the computational phase predicted ten relevant binding sites on ovalbumin's surface. The isobologram combination index (CI) heatmap strongly suggested a complex interplay of antagonistic and synergistic effects. Besides, a conformational drug-drug interaction network was proposed to explore the stability of the surfactant mixture within specific binding sites of ovalbumin, revealing a dynamic landscape of suggested antagonist effects. Experimental validations through UV-vis, Fluorescence, and circular dichroism (CD) spectroscopy further corroborated the computational findings, confirming the formation of stable complexes. Finally, this study not only advances our comprehension of ovalbumin's interactions with surfactants but also offers a multidimensional perspective and an advanced methodological framework for efficient therapeutic strategies, opening new avenues for future applications in drug development and applied biochemistry.

A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.

BACKGROUND: The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles. RESULTS: Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R2 and Q2-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity. CONCLUSIONS: The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.

Computational Modeling on Binding Interactions of Cyclodextrin s with the Human Multidrug Resistance P-glycoprotein Toward Efficient Drug-delivery System Applications.

BACKGROUND: Herein, molecular docking approaches and DFT ab initio simulations were combined for the first time, to study the key interactions of cyclodextrins (CDs: α-CD, ß-CD, and γ-CD) family with potential pharmacological relevance and the multidrug resistance P-gp protein toward efficient drug-delivery applications. The treatment of neurological disorders and cancer therapy where the multiple drug-resistance phenomenon mediated by the P-gp protein constitutes the fundamental cause of unsuccessful therapies. OBJECTIVES: To understand more about the CD docking mechanism and the P-gp. METHODS: In order to achieve the main goal, the computational docking process was used. The observed docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions,and also hybrid electrostatic/side-chain interactions of the CD-ligands' OHmotifs with acceptor and donor characteristics, which might theoretically cause local perturbations in the TMD/P-gp inter-residues network, influencing ligand extrusion through the blood-brain barrier. P-gp residues were conformationally favored. Despite the structural differences, all the cyclodextrins exhibit very close Gibbs free binding energy values (or affinity) by the P-gp binding site (transmembrane domains - TMDs). RESULT: The obtained theoretical docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions, and also hybrid electrostatic/side-chain interactions of the OH-motifs of the CD-ligands with acceptor and donor properties which theoretically could induce allosteric local-perturbations in the TMDs-inter-residues network of P-gp modulating to the CD-ligand extrusion from the blood-brain-barrier (or cancer cells). CONCLUSION: Finally, these theoretical results open new horizons for evaluating new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery applications and precision nanomedicine.

Modeling and experimental validation of covalent immobilization of Trametes maxima laccase on glyoxyl and MANA-Sepharose CL 4B supports, for the use in bioconversion of residual colorants.

Our novel strategy for the rational design of immobilized derivatives (RDID) is directed to predict the behavior of the protein immobilized derivative before its synthesis, by the usage of mathematic algorithms and bioinformatics tools. However, this approach needs to be validated for each target enzyme. The objective of this work was to validate the RDID strategy for covalent immobilization of the enzyme laccase from Trametes maxima MUCL 44155 on glyoxyl- and monoaminoethyl-N-aminoethyl (MANA)-Sepharose CL 4B supports. Protein surface clusters, more probable configurations of the protein-supports systems at immobilization pHs, immobilized enzyme activity, and protein load were predicted by RDID1.0 software. Afterward, immobilization was performed and predictions were experimentally confirmed. As a result, the laccase-MANA-Sepharose CL 4B immobilized derivative is better than laccase-glyoxyl-Sepharose CL 4B in predicted immobilized derivative activity (63.6% vs. 29.5%). Activity prediction was confirmed by an experimentally expressed enzymatic activity of 68%, using 2,6-dimethoxyphenol as substrate. Experimental maximum protein load matches the estimated value (11.2 ± 1.3 vs. 12.1 protein mg/support mL). The laccase-MANA-Sepharose CL 4B biocatalyst has a high specificity for the acid blue 62 colorant. The results obtained in this work suggest the possibility of using this biocatalyst for wastewater treatment.

Unraveling the Compositional and Molecular Features Involved in Lysozyme-Benzothiazole Derivative Interactions.

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) π-π 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 α-helices. Likewise, the BTS ligand interacts directly with allosteric residues, inducing perturbations in the conformational dynamics expressed as a moderate conformational softening in the α-helices H1, H2, and their corresponding ß-loop in the lysozyme receptor, in contrast to the unbound state of lysozyme.

Advanced Materials Based on Nanosized Hydroxyapatite.

The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques. The focus of this review is the various attempts to improve new hydroxyapatite-based materials. First, we comment on the most used processing routes, highlighting their advantages and disadvantages. We will now focus on other routes, less common due to their specificity and/or recent development. We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning. In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy. The review concludes with an outlook for possible new research directions.

Corrigendum to: Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures.

Due to an oversight of the publisher, Page no 2310 was missing in the published paper and page no 2311 repeated twice in the article entitled "Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures, 2020, 20(25), 2308-2325 [1]. The page no 2310 is added in the article and the repetition of page no 2311 is corrected. The original article can be found online at https://doi.org/10.2174/1568026620666200820145412.

New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches.

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

Targeting Beta-Blocker Drug-Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study.

In this work, one of the most prevalent polypharmacology drug-drug interaction events that occurs between two widely used beta-blocker drugs-i.e., acebutolol and propranolol-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-drug interactions. Furthermore, the total affinity for the stabilization of the drug-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-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.

Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures.

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.

Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches.

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.

Developing a Multi-target Model to Predict the Activity of Monoamine Oxidase A and B Drugs.

INTRODUCTION: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson's disease (PD), Alzheimer's disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant efforts have been devoted to finding new compounds with more selectivity and less side effects. One of the most used approaches is based on the use of computational approaches since they are time and money-saving and may allow us to find a more relevant structure-activity relationship. OBJECTIVE: In this manuscript, we will review the most relevant computational approaches aimed at the prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask model aimed at predicting MAO-A and MAO-B inhibitors. METHODS: The QSAR multi-task model herein developed was based on the use of the linear discriminant analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl. The molecular descriptors used was calculated using the Dragon software. Classical statistical tests were performed to check the validity and robustness of the model. RESULTS: The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical performed tests indicated that this model is robust and reproducible. CONCLUSION: MAOIs are compounds of large interest since they are largely used in the treatment of very serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development of new and more selective MAO inhibitors.

Graphene oxide and GST-omega enzyme: An interaction that affects arsenic metabolism in the shrimp Litopenaeus vannamei.

Arsenic (As) is one of the most widespread contaminants; it is found in almost every environment. Its toxic effects on living organisms have been studied for decades, but the interaction of this metalloid with other contaminants is still relatively unknown, mainly whether this interaction occurs with emerging contaminants such as nanomaterials. To examine this relationship, the marine shrimp Litopenaeus vannamei was exposed for 48 h to As, graphene oxide (GO; two different concentrations) or a combination of both, and gills, hepatopancreas and muscle tissues were sampled. Glutathione S-transferase (GST)-omega gene expression and activity were assessed. As accumulation and speciation (metabolisation capacity) were also examined. Finally, a molecular docking simulation was performed to verify the possible interaction between the nanomaterial and GST-omega. The main finding was that GO modulated the As toxic effect: it decreased GST-omega activity, a consequence related to altered As accumulation and metabolism. Besides, the molecular docking simulation confirmed the capacity of GO to interact with the enzyme structure, which also can be related to the decreased GST-omega activity and subsequently to the altered As accumulation and metabolisation pattern.

Rapanone, a naturally occurring benzoquinone, inhibits mitochondrial respiration and induces HepG2 cell death.

Rapanone is a natural occurring benzoquinone with several biological effects including unclear cytotoxic mechanisms. Here we addressed if mitochondria are involved in the cytotoxicity of rapanone towards cancer cells by employing hepatic carcinoma (HepG2) cells and isolated rat liver mitochondria. In the HepG2, rapanone (20-40 µM) induced a concentration-dependent mitochondrial membrane potential dissipation, ATP depletion, hydrogen peroxide generation and, phosphatidyl serine externalization; the latter being indicative of apoptosis induction. Rapanone toxicity towards primary rats hepatocytes (IC50 = 35.58 ± 1.50 µM) was lower than that found for HepG2 cells (IC50 = 27.89 ± 0.75 µM). Loading of isolated mitochondria with rapanone (5-20 µM) caused a concentration-dependent inhibition of phosphorylating and uncoupled respirations supported by complex I (glutamate and malate) or the complex II (succinate) substrates, being the latter eliminated by complex IV substrate (TMPD/ascorbate). Rapanone also dissipated mitochondrial membrane potential, depleted ATP content, released Ca2+ from Ca2+-loaded mitochondria, increased ROS generation, cytochrome c release and membrane fluidity. Further analysis demonstrated that rapanone prevented the cytochrome c reduction in the presence of decylbenzilquinol, identifying complex III as the site of its inhibitory action. Computational docking results of rapanone to cytochrome bc1 (Cyt bc1) complex from the human sources found spontaneous thermodynamic processes for the quinone-Qo and Qi binding interactions, supporting the experimental in vitro assays. Collectively, these observations suggest that rapanone impairs mitochondrial respiration by inhibiting electron transport chain at Complex III and promotes mitochondrial dysfunction. This property is potentially involved in rapanone toxicity on cancer cells.

Structural and energetic evolution of fibrinogen toward to the betablocker interactions.

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 Å) 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.

New Mechanistic Insight on the PIM-1 Kinase Inhibitor AZD1208 Using Multidrug Resistant Human Erythroleukemia Cell Lines and Molecular Docking Simulations.

BACKGROUND: PIM-1 is a kinase which has been related to the oncogenic processes like cell survival, proliferation, and multidrug resistance (MDR). This kinase is known for its ability to phosphorylate the main extrusion pump (ABCB1) related to the MDR phenotype. OBJECTIVE: In the present work, we tested a new mechanistic insight on the AZD1208 (PIM-1 specific inhibitor) under interaction with chemotherapy agents such as Daunorubicin (DNR) and Vincristine (VCR). MATERIALS AND METHODS: In order to verify a potential cytotoxic effect based on pharmacological synergism, two MDR cell lines were used: Lucena (resistant to VCR) and FEPS (resistant to DNR), both derived from the K562 non-MDR cell line, by MTT analyses. The activity of Pgp was ascertained by measuring accumulation and the directional flux of Rh123. Furthermore, we performed a molecular docking simulation to delve into the molecular mechanism of PIM-1 alone, and combined with chemotherapeutic agents (VCR and DNR). RESULTS: Our in vitro results have shown that AZD1208 alone decreases cell viability of MDR cells. However, co-exposure of AZD1208 and DNR or VCR reverses this effect. When we analyzed the ABCB1 activity AZD1208 alone was not able to affect the pump extrusion. Differently, co-exposure of AZD1208 and DNR or VCR impaired ABCB1 activity, which could be explained by compensatory expression of abcb1 or other extrusion pumps not analyzed here. Docking analysis showed that AZD1208 is capable of performing hydrophobic interactions with PIM-1 ATP- binding-site residues with stronger interaction-based negative free energy (FEB, kcal/mol) than the ATP itself, mimicking an ATP-competitive inhibitory pattern of interaction. On the same way, VCR and DNR may theoretically interact at the same biophysical environment of AZD1208 and also compete with ATP by the PIM-1 active site. These evidences suggest that AZD1208 may induce pharmacodynamic interaction with VCR and DNR, weakening its cytotoxic potential in the ATP-binding site from PIM-1 observed in the in vitro experiments. CONCLUSION: Finally, the current results could have a pre-clinical relevance potential in the rational polypharmacology strategies to prevent multiple-drugs resistance in human leukemia cancer therapy.

Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1).

We present an in silico approach for modeling the noncovalent interactions between the human mitochondrial voltage-dependent anion channel (hVDAC1) and a family of single-walled carbon nanotubes (SWCNTs) with a defined pattern of topological vacancies ( v = 1-16), obtained by removing atoms from the SWCNT surface. The general results showed more stable docking interaction complexes (SWCNT-hVDAC1), with more negative Gibbs free energy of binding affinity values, and a strong dependence on the vacancy number ( R2 = 0.93) and vacancy formation energy ( R2 = 0.96). In addition, for most of the SWCNT vacancies that were analyzed, the interatomic distances for the interactions of the SWCNT-hVDAC1 complex with the functional catalytic residues (i.e., Pro7, Gln199, Gln182, Phe181, Val20, Asp19, Lys15, Gly14, Asp12, Ala11, and Arg18) that form the hVDAC1 active site (i.e., the voltage-sensing N-terminal α-helix segment) were very similar to or shorter than the interatomic distances of these residues for ATP-hVDAC1 interactions. In particular, the hVDAC1 residues that can be phosphorylated like Tyr10, Tyr198, and Se16 were significantly perturbed by the interactions with SWCNT with at least nine vacancies. In addition, the SWCNT vacancy family members can affect the flexibility properties of the hVDAC1 N-terminal α-helix segment inducing different patterns of local perturbations in inter-residue communication. Finally, vacancy quantitative structure-binding relationships (V-QSBRs) were unveiled for setting up a robust model that can predict the strength of docking interactions between SWCNTs with a specific topological vacancy and hVDAC1. The developed V-QSBR model classified properly all of the SWCNTs with a different number of SWCNT vacancies with exceptional sensitivity and specificity (both equal to 100%), indicating a strong potential to unequivocally predict the influence of SWCNT vacancies on the mitochondrial channel interactions.

Interactions of graphene derivatives with glutamate-neurotransmitter: A parallel first principles - Docking investigation.

Glutamate plays an important role in excitatory neurotransmission, learning, and memory processes, and under pathological conditions it is directly associated with several chronic neurological disorders, such as depression, epilepsy, schizophrenia, and Parkinson's. Therefore, the detection and quantification of Glutamate is important for the rapid diagnosis of these diseases. Using first principles and molecular docking simulations we have evaluated the energetic, structural, and binding properties of graphene derivatives, such as pristine graphene (pristine-Gr) and oxidized graphene with carboxylic (Gr-COOH), carbonyl (Gr-COH), hydroxyl (Gr-OH), and epoxy (-O-) groups interacting with the glutamate neurotransmitter. The calculated binding affinity free energies from the docking complexes (glutamate-graphene family) suggest higher oxidized graphene-based glutamate molecular recognition than the pristine-Gr, with the following order of oxidized graphene derivatives according to ab initio results: (Gr-O∼Gr-COOH ∼ Gr-COH > Gr-OH)>pristine-Gr. Herein, the ab initio binding energies found for the glutamate-graphene family complexes are in the range of 0.24-0.80 eV. The configurations studied showed a biophysical adsorption regime without significant changes in the physico-chemical properties of the adsorbed glutamate neurotransmitter, in accordance with the general acceptance criteria of the detection systems.

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.

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.