Colorimetric and visual determination of uric acid based on decolorization of manganese dioxide nanosheet dispersions.

Serum levels of uric acid (UA) play an important role in the prevention of diseases. Developing a rapid and accurate way to detect UA is still a meaningful task. Hence, positively charged manganese dioxide nanosheets (MnO2NSs) with an average latter size of 100 nm and an ultra-thin thickness of below 1 nm have been prepared. They can be well dispersed in water and form stable yellow-brown solutions. The MnO2NSs can be decomposed by UA via redox reaction, leading to a decline of a characteristic absorption peak (374 nm) and a color fading of MnO2NSs solution. On this basis, an enzyme-free colorimetric sensing system for the detection of UA has been developed. The sensing system shows many advantages, including a wide linear range of 0.10-50.0 µmol/L, a limit of quantitation (LOQ) of 0.10 µmol/L, a low limit of detection (LOD) of 0.047 µmol/L (3σ/m), and rapid response without need of strict time control. Moreover, a simple and convenient visual sensor for UA detection has also been developed by adding an appropriate amount of phthalocyanine to provide a blue background color, which helps to increase visual discrimination. Finally, the strategy has been successfully applied to detect UA in human serum and urine samples.

Hybridizing Carbon-Based Dot-Capped Manganese Dioxide Nanosheets and Gold Nanoparticles as a Highly Sensitive Surface-Enhanced Raman Scattering Substrate.

Surface-enhanced Raman Scattering (SERS) is a sensitive and nondestructive technique that provides fingerprint structural information of molecules. Designing and constructing sensitive and stable SERS substrates is of great significance for the application of the technique. In this study, single-layer carbon-based dots (CDs) are used as capping agents to synthesize gold nanoparticles (AuNPs/CDs) and manganese dioxide nanosheets (MnO2/CDs), which are then hybridized through a simple cocentrifugation method. After the hybridization, the monodispersive AuNPs/CDs aggregate obviously into some clusters exhibiting strong SERS activity due to the electromagnetic "hot spots" effect, and the MnO2/CDs also show outstanding SERS activity due to the charge-transfer resonance effect. The obtained nanohybrids (MnO2/CDs/AuNPs) with robust chemical stability combine well with the electromagnetic enhancement of AuNPs/CDs and chemical enhancement of MnO2/CDs, leading to an ultrahigh enhancement factor of 3.9 × 108. Based on the novel SERS substrate, a sensitive and rapid sensing system for the detection of malachite green is developed, with a low detection limit of 1 × 10-9 M. This work provides a valuable model for designing and fabricating high-performance SERS substrates.

Tune the Fluorescence and Electrochemiluminescence of Graphitic Carbon Nitride Nanosheets by Controlling the Defect States.

The effects of defect states on the fluorescence (FL) and electrochemiluminescence (ECL) properties of graphite phase carbon nitride (g-CN) are systematically investigated for the first time. The g-CN nanosheets (CNNSs) obtained at different condensation temperatures are used as the study models. It can be found that all the CNNSs have two kinds of defect states, one is originated from the edge of CNNSs (labeled as CN-defect) and the other is attributed to the partially carbonization regions (labeled as C-defect). Both two kinds of defect states substantially affect the luminescent properties of CNNSs. Both the FL and ECL signals of CNNSs contain a band gap emission and two defect emissions. For the FL of CNNSs, decreasing the density of defect states can increase efficiently the FL quantum yield, while increasing the density of defect states can make the FL spectra red shift. For the ECL of CNNSs, increasing the density of CN-defect states and decreasing the density of C-defect states are greatly important to improve the ECL activity. This work provides a deep insight into the FL and ECL mechanisms of g-CN, and is of significance in tuning the FL and ECL properties of g-CN. Also, it will greatly promote the applications of CNNSs based on the FL and ECL properties.

Real-time measurement of CO2 isotopologue ratios in exhaled breath by a hollow waveguide based mid-infrared gas sensor.

A hollow waveguide (HWG) based mid-infrared gas sensor using a 2.73 µm distributed feedback (DFB) laser was developed for simultaneously measuring the concentration changes of the three isotopologues 13CO2, 12CO2, and 18OC16O in exhaled breath by direct absorption spectroscopy, and then determining the 13CO2/12CO2 isotope ratio (δ13C) and 18OC16O/12CO2 isotope ratio (δ18O). The HWG sensor showed a fast response time of 3 s. Continuous measurement of δ13C and δ18O in the standard CO2 sample with known isotopic ratios for ∼2 h was performed. Precisions of 2.20‰ and 1.98‰ for δ13C and δ18O respectively at optimal integration time of 734 s were estimated from Allan variance analysis. Accuracy of -0.49‰ and -1.20‰ for δ13C and δ18O, respectively, were obtained with comparison to the values of the reference standard. The Kalman filtering method was employed to improve the precision and accuracy of the HWG sensor while maintaining high time resolution. Precision of 5.45‰ and 4.88‰ and the accuracy of 0.21‰ and -1.13‰ for δ13C and δ18O, respectively, were obtained at the integration time of 0.54 s with the application of Kalman filtering. The concentrations of 12CO2, 13CO2 and 18OC16O in breath cycles were measured and processed by Kalman filtering in real time. The measured values of δ18O and δ13C in exhaled breath were estimated to be -21.35‰ and -33.64‰, respectively, with the integration time of 1 s. This study demonstrates the ability of the HWG sensor to obtain δ13C and δ18O values in breath samples and its potential for immediate respiratory monitoring and disease diagnosis.

Assessing Force Field Potential Energy Function Accuracy via a Multipolar Description of Atomic Electrostatic Interactions in RNA.

Computational investigations of RNA properties often rely on a molecular mechanical approach to define molecular potential energy. Force fields for RNA typically employ a point charge model of electrostatics, which does not provide a realistic quantum-mechanical picture. In reality, electron distributions around nuclei are not spherically symmetric and are geometry dependent. A multipole expansion method which allows for incorporation of polarizability and anisotropy in a force field is described, and its applicability to modeling the behavior of RNA molecules is investigated. Transferability of the model, critical for force field development, is also investigated.

Molecular modeling study of the induced-fit effect on kinase inhibition: the case of fibroblast growth factor receptor 3 (FGFR3).

Tyrosine kinases are a wide family of targets with strong pharmacological relevance. These proteins undergo large-scale conformational motions able to inactivate them. By the end of one of these structural processes, a new cavity is opened allowing the access to a specific type of inhibitors, called type II. The kinase domain of fibroblast growth factor receptor 3 (FGFR3) falls into this family of kinases. We describe here, for the first time, its inactivation process through target molecular dynamics. The transient cavity, at the crossroad between the DFGout and Cα helix out inactivation is herein explored. Molecular docking calculations of known ligands demonstrated that type II inhibitors are able to interact with this metastable transient conformation of FGFR3 kinase. Besides, supplemental computations were conducted and clearly show that type II inhibitors drive the kinase inactivation process through specific stabilization with the DFG triad. This induced-fit effect of type II ligands toward FGFR3 might be extrapolated to other kinase systems and provides meaningful structural information for future drug developments.

Receptor- and ligand-based 3D-QSAR study for a series of non-nucleoside HIV-1 reverse transcriptase inhibitors.

Molecular modeling of a series of HIV reverse transcriptase (RT) non-nucleoside inhibitors (2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners) was carried out by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. Docking simulations were employed to position the inhibitors into RT active site to determine the most probable binding mode and most reliable conformations. The study was conducted using a complex receptor-based and ligand-based alignment procedure and different alignment modes were studied to obtain highly reliable and predictive CoMFA and CoMSIA models with cross-validated q(2) value of 0.723 and 0.760, respectively. Furthermore, the CoMFA and CoMSIA contour maps with the 3D structure of the target (the binding site of RT) inlaid were obtained to better understand the interaction between the RT protein and the inhibitors and the structural requirements for inhibitory activity against HIV-1. We show that for 2-amino-6-arylsulfonylbenzonitriles inhibitors to have appreciable inhibitory activity, bulky and hydrophobic groups in 3- and 5-position of the B ring are required. Moreover, H-bond donor groups in 2-position of the A ring to build up H-bonding with the Lys101 residue of the RT protein are also favorable to activity.

Using Deep Learning for Compound Selectivity Prediction.

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.

Targeted molecular dynamics (TMD) of the full-length KcsA potassium channel: on the role of the cytoplasmic domain in the opening process.

Some recent papers clearly indicate that the cytoplasmic domain of KcsA plays a role in pH sensing. We have performed, for the first time, a targeted molecular dynamics (TMD) simulation of the opening of full-length KcsA at pH 4 and pH 7, with a special interest for the cytoplasmic domain. Association energy calculations show a stabilization at pH 7 confirming that the protonation of some amino-acids at pH 4 in this domain plays a role in the opening process. A careful analysis of the pH dependent charges borne by residues in the cytoplasmic domain and their interactions confirms some literature experimental data and permits to give further insight into the role played by some of them in the opening process.

Molecular dynamics simulations of 2-amino-6-arylsulphonylbenzonitriles analogues as HIV inhibitors: interaction modes and binding free energies.

Molecular dynamics (MD) simulations in water environment were carried out on the HIV-1 reverse transcriptase (RT), and its complexes with one representative of each of three series of inhibitors: 2-amino-6-arylsulphonylbenzonitriles and their thio and sulphinyl congeners. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) was used to calculate the binding free energy based on the obtained MD trajectories. Calculated energies are correlated to activity. A comparison of interaction modes, binding free energy, contributions of the residues to the binding free energy and H-bonds was carried out with the average structures. The results show that there exist different interaction modes between RT and ligands and different specific interactions with some residues. The higher binding affinity of the most potent inhibitor in the series of molecules under study is favoured by electrostatic interactions and solvation contribution.

Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression.

Quantitative structure-activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas using linear free energy relationship (LFER). Eight molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs to perform multiple linear regression (MLR), support vector machine (SVM) and projection pursuit regression (PPR) studies. Compared with MLR model, the SVM and PPR models give better results with the predicted correlation coefficient (R(2)) of 0.867 and 0.834 and the squared standard error (s(2)) of 0.095 and 0.119 for the training set and R(2) of 0.732 and 0.726 and s(2) of 0.210 and 0.207 for the test set, respectively. It indicates that the SVM and PPR approaches are more adapted to the set of molecules we studied. In addition, methods used in this paper are simple, practical and effective for chemists to predict the human CCR5 chemokine receptor.

QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods.

A quantitative structure-activity relationship study of a series of HIV-1 reverse transcriptase inhibitors (2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners) was performed. Topological and geometrical, as well as quantum mechanical energy-related and charge distribution-related descriptors generated from CODESSA, were selected to describe the molecules. Principal component analysis (PCA) was used to select the training set. Six techniques: multiple linear regression (MLR), multivariate adaptive regression splines (MARS), radial basis function neural networks (RBFNN), general regression neural networks (GRNN), projection pursuit regression (PPR) and support vector machine (SVM) were used to establish QSAR models for two data sets: anti-HIV-1 activity and HIV-1 reverse transcriptase binding affinity. Results showed that PPR and SVM models provided powerful capacity of prediction.

QSPR prediction of GC retention indices for nitrogen-containing polycyclic aromatic compounds from heuristically computed molecular descriptors.

Gas chromatographic retention indices of nitrogen-containing polycyclic aromatic compounds (N-PACs) have been predicted by quantitative structure-property relationship (QSPR) analysis based on heuristic method (HM) implemented in CODESSA. In order to indicate the influence of different molecular descriptors on retention indices and well understand the important structural factors affecting the experimental values, three multivariable linear models derived from three groups of different molecular descriptors were built. Moreover, each molecular descriptor in these models was discussed to well understand the relationship between molecular structures and their retention indices. The proposed models gave the following results: the square of correlation coefficient, R(2), for the models with one, two and three molecular descriptors was 0.9571, 0.9776 and 0.9846, respectively.