Interplay of Effective Surface Area, Mass Transport, and Electrochemical Features in Nanoporous Nucleic Acid Sensors.

Electrochemical biosensors transduce biochemical events (e.g., DNA hybridization) to electrical signals and can be readily interfaced with electronic instrumentation for portability. Nanostructuring the working electrode enhances sensor performance via augmented effective surface area that increases the capture probability of an analyte. However, increasing the effective surface area via thicker nanostructured electrodes hinders the analyte's permeation into the nanostructured volume and limits its access to deeper electrode surfaces. Here, we use nanoporous gold (np-Au) with various thicknesses and pore morphologies coupled with a methylene blue (MB) reporter-tagged DNA probe for DNA target detection as a model system to study the influence of electrode features on electrochemical sensing performance. Independent of the DNA target concentration, the hybridization current (surrogate for detection sensitivity) increases with the surface enhancement factor (EF), until an EF of ∼5, after which the sensor performance deteriorates. Electrochemical and fluorometric quantification of a desorbed DNA probe suggest that DNA permeation is severely limited for higher EFs. In addition, undesirable capacitive currents disguise the faradaic currents from the MB reporter at larger EFs that require higher square wave voltammetry (SWV) frequencies. Finally, a real-time hybridization study reveals that expanding the effective surface area beyond EFs of ∼5 decreases sensor performance.

Anomalous Trends in Nucleic Acid-Based Electrochemical Biosensors with Nanoporous Gold Electrodes.

Molecular diagnostics have significantly advanced the early detection of diseases, where electrochemical sensing of biomarkers has shown considerable promise. For a nucleic acid-based electrochemical sensor with signal-off behavior, the performance is evaluated by percent signal suppression (% ss), which indicates the change in current after hybridization. The % ss is generally due to more redox molecules (e.g., methylene blue) associating with the probe DNA bases in the single-strand form than the double-strand form upon hybridization with the target nucleic acid. Nanostructured electrodes generally enhance electrochemical sensor performance via several mechanisms, including increased number of capture probes per electrode volume and unique nanoscale transport phenomena. Here, we employ nanoporous gold (np-Au) as a model electrode material to study the influence of probe immobilization solution concentration on sensor performance and the underlying mechanisms. Unlike planar gold (pl-Au) electrodes, where % ss reaches a steady state with increasing concentration of the grafting solution, the % ss displays peak performance at certain grafting solution concentrations followed by rapid deterioration and reversal of the % ss polarity, suggesting an unexpected case of increased charge transfer upon hybridization. Fluorometric assessments of electrochemically desorbed nucleic acids for different electrode morphologies reveal that a significant amount of DNA molecules (unhybridized and hybridized) remain within the nanopores posthybridization. Analysis of electrochemical signals (e.g., square wave voltammogram shape) suggests that the large unbound nucleic acid concentration may be altering the modes of methylene blue interaction with the nucleic acids and charge transfer to the electrode surfaces.

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models.

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential.

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as "correlations along two parallel lines." This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: "inactive/active" (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews's correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

Chemical characterization, in vitro biological activity of essential oils and extracts of three Eryngium L. species and molecular docking of selected major compounds.

Many Eryngium species have been traditionally used as ornamental, edible or medicinal plants. The gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS) analyses have shown that the major compounds in the aerial parts were spathulenol (in E. campestre and E. palmatum oils) and germacrene D (in E. amethystinum oil). The main compounds in the root oil were nonanoic acid, 2,3,4-trimethylbenzaldehyde and octanoic acid for E. campestre, E. amethystinum and E. palmatum, respectively. All the oils expressed the highest potential against Gram-positive bacteria Staphylococcus aureus as well as Gram-negative Klebsiella pneumoniae and Proteus mirabilis. Molecular docking analysis was used for determining a potential antibacterial activity mechanism of compounds present in the essential oils. Molecular docking confirmed that the binding affinity of spathulenol to the active site of tyrosyl-tRNA synthetase was the highest among the tested dominant compounds. Regarding the total phenolic content (determined by the Folin-Ciocalteu assay) and flavonoid content (evaluated using aluminum nitrate nonahydrate), the highest amount was found in the ethyl acetate extract of E. palmatum. The results of DPPH and ABTS assay indicated that the highest antioxidant activity was present in the water extract of E. amethystinum. Extracts of the aerial parts presented as minimum inhibitory concentration (MIC) expressed the activity in the range 0.004-20.00 mg/mL, with the highest activity exhibited by the acetone and ethyl acetate extracts against Proteus mirabilis. The obtained results suggest that Eryngium species may be considered a beneficial native source of the compounds with antioxidant and antimicrobial properties.

Electrically Guided DNA Immobilization and Multiplexed DNA Detection with Nanoporous Gold Electrodes.

Molecular diagnostics have significantly advanced the early detection of diseases, where the electrochemical sensing of biomarkers (e.g., DNA, RNA, proteins) using multiple electrode arrays (MEAs) has shown considerable promise. Nanostructuring the electrode surface results in higher surface coverage of capture probes and more favorable orientation, as well as transport phenomena unique to nanoscale, ultimately leading to enhanced sensor performance. The central goal of this study is to investigate the influence of electrode nanostructure on electrically-guided immobilization of DNA probes for nucleic acid detection in a multiplexed format. To that end, we used nanoporous gold (np-Au) electrodes that reduced the limit of detection (LOD) for DNA targets by two orders of magnitude compared to their planar counterparts, where the LOD was further improved by an additional order of magnitude after reducing the electrode diameter. The reduced electrode diameter also made it possible to create a np-Au MEA encapsulated in a microfluidic channel. The electro-grafting reduced the necessary incubation time to immobilize DNA probes into the porous electrodes down to 10 min (25-fold reduction compared to passive immobilization) and allowed for grafting a different DNA probe sequence onto each electrode in the array. The resulting platform was successfully used for the multiplexed detection of three different biomarker genes relevant to breast cancer diagnosis.

QSPR in forensic analysis - The prediction of retention time of pesticide residues based on the Monte Carlo method.

A method for the prediction of retention indices of pesticides using the Monte Carlo method and with optimal molecular descriptors based on local graph invariants and the SMILES notation of studied compounds has been presented. Quite satisfactory results were obtained with the proposed method, since a robust model with good statistical quality was developed. The predictive potential of the applied approach was tested and the robustness of the model was proven with different methods. The best calculated QSPR model had following statistical parameters: r2 = 0.9182 for the training set and r2 = 0.8939 for the test set. Structural indicators defined as molecular fragments responsible for the increases and decreases of gas chromatographic retention indices activity were calculated.

Potent anti-melanogenic activity and favorable toxicity profile of selected 4-phenyl hydroxycoumarins in the zebrafish model and the computational molecular modeling studies.

7-Hydroxy-4-phenylcoumarin (7C) and 5,7-dihydroxy-4-phenylcoumarin (5,7C) have been evaluated as potential anti-melanogenic agents in the zebrafish (Danio rerio) model in comparison to commercially utilized depigmenting agents hydroquinone and kojic acid. 7C and 5,7C decreased the body pigmentation at 5 µg/mL, while did not affect the embryos development and survival at doses ≤50 µg/mL and ≤25 µg/mL. Unlike hydroquinone and kojic acid, 4-phenyl hydroxycoumarins were no melanocytotoxic, showed no cardiotoxic side effects, neither caused neutropenia in zebrafish embryos, suggesting these compounds may present novel skin-whitening agents with improved pharmacological properties. Inhibition of tyrosinase was identified as the possible mode of anti-melanogenic action. Molecular docking studies using the homology model of human tyrosinase as well as adenylate cyclase revealed excellent correlation with experimentally obtained results.

Redox behavior and biological properties of ferrocene bearing porphyrins.

In order to improve antimicrobial effects of previously studied meso-tetrakis(4-ferrocenylphenyl)porphyrin 1, we have modified its structure by replacing two trans-positioned ferrocenylphenyl moieties with methoxy methylene substituted tert-butylphenyl moieties. Newly synthesized 54,154-bis-(ferrocenyl)-104,204-bis-(tert-butyl)-102,106,202,206-tetrakis-(methoxy-methylene)-5,10,15,20-tetraphenylporphyrin 4 was chemically characterized in detail (by NMR, UV/Vis, IR, MALDI-TOF and ESI MS spectrometry, cyclic voltammetry, prediction of the relative lipophilicity as well as computational methods) and its biological effects were studied in terms of its antibacterial and antifungal activity (both with and without photoactivation), cytotoxicity, hemolysis and DNA cleavage. New ferrocene bearing porphyrin 4 has demonstrated a broader antimicrobial spectrum and modified effects on eukaryotic cells compared to 1. This was discussed in terms of its i) increased lipophilicity, while exhibiting lower toxicity, and ii) the redox potential of a two-electron process that is shifted to lower values, in comparison to ferrocene, thus, entering the physiologically available range and being activated towards redox interactions with biomolecules.

Prediction of gas chromatographic retention indices based on Monte Carlo method.

A new method for the prediction of retention indices using Monte Carlo method and based on local graph invariants and SMILES notation of studied compounds has been presented. Very satisfactory results were obtained with the proposed method, since robust model with good statistical quality was developed. The predictive potential of the applied approach was tested and the robustness of the model was proven with different methods. The best calculated QSPR model had following statistical parameters: r2=0.8097 for the training set and r2=0.9372 for the test set. Structural indicators defined responsible for the increases and decreases of gas chromatographic retention indices activity have been calculated.

Bioactive Pentacyclic Triterpene Ester Derivatives from Alnus viridis ssp. viridis Bark.

Seven derivatives of pentacyclic triterpene acids (1-7) were isolated from the bark of Alnus viridis ssp. viridis using a combination of column chromatography and semipreparative HPLC. Compounds 1-3, 6, and 7 were determined to be new after spectroscopic data interpretation and were assigned as 27-hydroxyalphitolic acid derivatives (1-3), a 27-hydroxybetulinic acid derivative (6), and a 3-epi-maslinic acid derivative (7), respectively. Pentacyclic triterpenoids with a C-27 hydroxymethyl group have been found in species of the genus Alnus for the first time. These compounds were subjected to cytotoxicity testing against a number of cancer cell lines. Also, selected pentacyclic triterpenoids were selected as potential inhibitors of topoisomerases I and IIα for an in silico investigation.

The Monte Carlo technique as a tool to predict LOAEL.

Quantitative structure - activity relationships (QSARs) for the Lowest Observed Adverse Effect Level (LOAEL) for a large set of organic compounds (n = 341) are suggested. The molecular structures of these compounds are represented by Simplified Molecular Input-Line Entry Systems (SMILES). A criteria for the estimation quality of split into the "visible" training set (used for developing a model) and "invisible" external validation set is suggested. The correlation between the above criterion and the predictive potential of developed QSAR model (root-mean-square error for "invisible" validation set) has been detected. One-variable models are built up for several different splits into the "visible" training set and "invisible" validation set. The statistical quality of these models is quite good. Mechanistic interpretation and the domain of applicability for these models are defined according to probabilistic point of view. The methodology for defining applicability domain in QSAR modeling with SMILES notation based optimal descriptors is presented.

Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna.

Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r2 = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.

Synthesis and evaluation of thiophene-based guanylhydrazones (iminoguanidines) efficient against panel of voriconazole-resistant fungal isolates.

A series of new thiophene-based guanylhydrazones (iminoguanidines) were synthesized in high yields using a straightforward two-step procedure. The antifungal activity of compounds was evaluated against a wide range of medicaly important fungal strains including yeasts, molds, and dermatophytes in comparison to clinically used drug voriconazole. Cytotoxic properties of compounds were also determined using human lung fibroblast cell line and hemolysis assay. All guanylhydrazones showed significant activity against broad spectrum of clinically important species of Candida spp., Aspergillus fumigatus, Fusarium oxysporum, Microsporum canis and Trichophyton mentagrophytes, which was in some cases comparable or better than activity of voriconazole. More importantly, compounds 10, 11, 13, 14, 18 and 21 exhibited excellent activity against voriconazole-resistant Candida albicans CA5 with very low minimal inhibitory concentration (MIC) values <2 µg mL(-1). Derivative 14, bearing bromine on the phenyl ring, was the most effective compound with MICs ranging from 0.25 to 6.25 µg mL(-1). However, bis-guanylhydrazone 18 showed better selectivity in terms of therapeutic index values. In vivo embryotoxicity on zebrafish (Danio rerio) showed improved toxicity profile of 11, 14 and 18 in comparison to that of voriconazole. Most guanylhydrazones also inhibited C. albicans yeast to hyphal transition, essential for its biofilm formation, while 11 and 18 were able to disperse preformed Candida biofilms. All guanylhydrazones showed the equal potential to interact with genomic DNA of C. albicans in vitro, thus indicating a possible mechanism of their action, as well as possible mechanism of observed cytotoxic effects. Tested compounds did not have significant hemolytic effect and caused low liposome leakage, which excluded the cell membrane as a primary target. On the basis of computational docking experiments using both human and cytochrome P450 from Candida it was concluded that the most active guanylhydrazones had minimal structural prerequisites to interact with the cytochrome P450 14α-demethylase (CYP51). Promising guanylhydrazone derivatives also showed satisfactory pharmacokinetic profile based on molecular calculations.

Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions.

The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.

QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids.

Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization. In this work, the representation of the molecular structure of peptides (mastoparan analogs) by a sequence of amino acids has been used to establish quantitative structure-activity relationships (QSARs) for their antibacterial activity. The data for the studied peptides were split three times into the training, calibration and test sets. The Monte Carlo method was used as a computational technique for QSAR models calculation. The statistical quality of QSAR for the antibacterial activity of peptides for the external validation set was: n=7, r(2)=0.8067, s=0.248 (split 1); n=6, r(2)=0.8319, s=0.169 (split 2); and n=6, r(2)=0.6996, s=0.297 (split 3). The stated statistical parameters favor the presented QSAR models in comparison to 2D and 3D descriptor based ones. The Monte Carlo method gave a reasonably good prediction for the antibacterial activity of peptides. The statistical quality of the prediction is different for three random splits. However, the predictive potential is reasonably well for all cases. The presented QSAR modeling approach can be an attractive alternative of 3D QSAR at least for the described peptides.

In silico prediction of the ß-cyclodextrin complexation based on Monte Carlo method.

In this study QSPR models were developed to predict the complexation of structurally diverse compounds with ß-cyclodextrin based on SMILES notation optimal descriptors using Monte Carlo method. The predictive potential of the applied approach was tested with three random splits into the sub-training, calibration, test and validation sets and with different statistical methods. Obtained results demonstrate that Monte Carlo method based modeling is a very promising computational method in the QSPR studies for predicting the complexation of structurally diverse compounds with ß-cyclodextrin. The SMILES attributes (structural features both local and global), defined as molecular fragments, which are promoters of the increase/decrease of molecular binding constants were identified. These structural features were correlated to the complexation process and their identification helped to improve the understanding for the complexation mechanisms of the host molecules.

Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors.

The Monte Carlo method was used for QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors. The first QSAR model was developed for a series of 74 3-anilino-4-arylmaleimide derivatives. The second QSAR model was developed for a series of 177 maleimide derivatives. QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Two splits have been examined: one split into the training and test set for the first QSAR model, and one split into the training, test and validation set for the second. The statistical quality of the developed model is very good. The calculated model for 3-anilino-4-arylmaleimide derivatives had following statistical parameters: r(2)=0.8617 for the training set; r(2)=0.8659, and r(m)(2)=0.7361 for the test set. The calculated model for maleimide derivatives had following statistical parameters: r(2)=0.9435, for the training, r(2)=0.9262 and r(m)(2)=0.8199 for the test and r(2)=0.8418, r(av)(m)(2)=0.7469 and ∆r(m)(2)=0.1476 for the validation set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential glycogen synthase kinase-3ß inhibitors has been presented by using defined structural alerts.

Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design.

SMILES notation based optimal descriptors as a universal tool for the QSAR analysis with further application in drug discovery and design is presented. The basis of this QSAR modeling is Monte Carlo method which has important advantages over other methods, like the possibility of analysis of a QSAR as a random event, is discussed. The advantages of SMILES notation based optimal descriptors in comparison to commonly used descriptors are defined. The published results of QSAR modeling with SMILES notation based optimal descriptors applied for various pharmacologically important endpoints are listed. The presented QSAR modeling approach obeys OECD principles and has mechanistic interpretation with possibility to identify molecular fragments that contribute in positive and negative way to studied biological activity, what is of big importance in computer aided drug design of new compounds with desired activity.

Selected 4-phenyl hydroxycoumarins: in vitro cytotoxicity, teratogenic effect on zebrafish (Danio rerio) embryos and molecular docking study.

A study of structure cytotoxic-activity relationship of three hydroxy 4-phenyl-coumarins and basic coumarin molecule against two human cell lines (MRC5 fibroblasts and A375 melanoma cells) is presented. Of all investigated compounds the highest cytotoxic activity in both cell lines was determined for 7,8-dihydroxy-4-phenyl coumarin. SAR studies revealed the influence of phenyl group and hydroxyl group's number and position on cytotoxic activity. In addition, to get an insight about their binding preferences at the active site of the receptor (catalytic subunit of cAMP-dependent protein kinase) molecular docking studies were performed. Docking studies suggest that 4-phenyl hydroxycoumarins are potent cAMP-dependent protein kinase inhibitors, better than their analogs without phenyl group. The teratogenic potential was assessed in zebrafish embryo toxicity test and results showed that 4-phenyl dihydroxycoumarins were more while 7-hydroxy-4-phenyl coumarin was less embryo toxic in comparison to coumarin. In order to examine selected 4-phenyl hydroxycoumarins as a new lead compounds the druglikeness of selected 4-phenyl hydroxycoumarins was estimated by using Lipinski's "rule of five". All selected 4-phenyl hydroxycoumarins proved to have satisfying pharmacokinetic profile.