ABSTRACT
Aromatic stacking exists widely and plays important roles in protein-ligand interactions. Computational tools to automatically analyze the geometry and accurately calculate the energy of stacking interactions are desired for structure-based drug design. Herein, we employed a Behler-Parrinello neural network (BPNN) to build predictive models for aromatic stacking interactions and further integrated it into an open-source Python package named AromTool for benzene-containing aromatic stacking analysis. Based on extensive testing, AromTool presents desirable precision in comparison to DFT calculations and excellent efficiency for high-throughput aromatic stacking analysis of protein-ligand complexes.
Subject(s)
Hydrocarbons, Aromatic/chemistry , Proteins/chemistry , Density Functional Theory , Ligands , Models, Molecular , Neural Networks, Computer , Protein Binding , Structure-Activity Relationship , ThermodynamicsABSTRACT
Natural products are the major resource of drug discovery, and terpenoids represent the largest family of natural products. Terpenome is defined as all terpenoid-like and terpenoid-derived natural compounds, including the terpenoids, steroids, and their derivatives. Herein, aiming to navigate the chemical and biological space of terpenome, the first comprehensive database dedicated to terpenome research has been developed by collecting over 110â¯000 terpenome molecules from various resources, distributed in 14â¯351 species, belonging to 1109 families, and showing activity against 1366 biological targets. Much of the publically available information or computationally predicted properties for each terpenome molecule is annotated and integrated into TeroKit (http://terokit.qmclab.com/), serving as free Web server for academic use. Moreover, several practical toolkits, such as target profiling and conformer generation modules, are also implemented to facilitate the drug discovery of terpenome.
Subject(s)
Biological Products , Terpenes , Computers , Databases, Factual , Drug DiscoveryABSTRACT
Terpenoids represent the largest family of natural products (NPs) with dramatically chemical and structural diversity, which makes terpenoids the important compound resources of drug discovery. However, comprehensive understanding on the structure-function features for terpenoid NPs is limited. In this work, we have systematically explored the chemical and biological space of terpenoid NPs, including their distribution, physicochemical properties, scaffold features, and functional applications, by utilizing various cheminformatics and bioinformatics approaches. We have not only confirmed that terpenoid NPs have good drug-likeness and great potential for drug discovery but, more importantly, illuminated the uniqueness of cyclic scaffold diversity in different species (plants, fungi, bacteria, and animals) and the specificity of biological function for the dominant fused-ring scaffolds of terpenoids. The present work supplies a valuable reference for identifying the new structure and unknown function of terpenoid NPs.
Subject(s)
Biological Products/chemistry , Drug Discovery/methods , Pharmaceutical Preparations/chemistry , Terpenes/chemistry , Animals , Bacteria/chemistry , Biological Products/pharmacology , Cheminformatics/methods , Computational Biology/methods , Fungi/chemistry , Humans , Pharmacology , Plants/chemistry , Terpenes/pharmacologyABSTRACT
In order to investigate the emission enhancement mechanisms of reheating Double Pulse Laser-Induced Breakdown Spectroscopy (DP-LIBS), single pulse LIBS (SP-LIBS) and reheating DP-LIBS were carried out on an alloy steel sample respectively. The plasma emission was collected by an Echelle spectrometer with high resolution, while the plasma structure was monitored via fast-photography. The temporal and spatial evolutio ns of the plasma generated by SP-LIBS and reheating DP-LIBS were being studied. It is found that the plasma temperature in reheating DP-LIBS was higher than that of SP-LIBS, and there was a turning point for the decay rate of plasma temperature in reheating DP-LIBS when the delay time was equal to the interpulse time of DP-LIBS. Moreover, the inte nsity of the plasma image was increased by reheating DP-LIBS, and the height and width of the central region of the plasma were increased about 23.5% and 15.1% respectively. The results of spatial distribution showed that the intensity of Fe II and N I lines in the plasma were obviously enhanced by reheating DP-LIBS when the distance from the sample surface was larger than 0.6 mm. While the intensity enhancement for Fe I lines were little, even in some positio ns the intensity of Fe I lines decreased. The plasma temperature of double-pulse configuration was about 2 000 K higher than that of SP-LIBS, and a larger hot region in the plasma was generated. It is evidenced that the emission enhancement mechanisms in reheating DP-LIBS is that the second laser pulse re-excited the plasma induced by the first laser pulse, and the higher plasma temperature resulted from the re-exciting process.
ABSTRACT
In order to overcome the influence of self-absorption on quantitative analysis, the optimizing process of very fast simulated annealing algorithm was studied. According to basic theory of plasma emission spectrum, a new algorithm for self-absorpton correction based on multi-particles spectra was proposed, and the algorithm flowchart was given. With the self-absorption correction algorithm mentioned above, the spectra of refining slag and blast furnace slag were corrected. The effect of self-ab sorption correction on the quantitative analysis results was analyzed based on calibration free method. Comparison of Boltzmann plots before and after self-absorption correction indicated that the plasma temperatures calculated with spectra after self-absorption correction tended to be uniform, and remained stable around 11,600 K. The Boltzmann plots constructed with plasma spectra of the same particle after self-absorption correction indicated that the intercepts were almost the same except for one group data. With calibration free method and spectra after self-absorption correction, the contents of components in slag were analyzed. For refining slag, quantitative analysis precision of MgO was low. If ignoring the existence of MgO, the relative errors of quantitative analysis results of CaO, Al2 O3 and SiOs were much smaller. For blast furnace slag in which the content of MgO was 8.49%, the relative error of quantitative analysis result of Al2 O3 was 2.38%, which was the smallest. And the relative error of quantitative analysis result of MgO was 28.27%, which was still the biggest.
ABSTRACT
Most quantitative models used in laser-induced breakdown spectroscopy (LIBS) are based on the hypothesis that laser-induced plasma approaches the state of local thermal equilibrium (LTE). However, the local equilibrium is possible only at a specific time segment during the evolution. As the populations of each energy level does not follow Boltzmann distribution in non-LTE condition, those quantitative models using single spectral line would be inaccurate. A multivariate nonlinear model, in which the LTE is not required, was proposed in this article to reduce the signal fluctuation and improve the accuracy of quantitative analysis. This multivariate nonlinear model was compared with the internal calibration model which is based on the LTE condition. The content of Mn in steel samples was determined by using the two models, respectively. A minor error and a minor relative standard deviation (RSD) were observed in multivariate nonlinear model. This result demonstrates that multivariate nonlinear model can improve measurement accuracy and repeatability.
